From 17 to 22 May, the 13th World Urban Forum (WUF) will take place in Baku, Azerbaijan. For the second time in a row and the third time since 2020, an authoritarian state will host UN-Habitat’s flagship conference on urban development. As Germany and other democracies scale back their presence at the forum, the visibility of authoritarian states is increasing. If this trend persists, the conference risks becoming a global stage for authoritarian urbanism.

The last WUF in 2024 served as a case in point. Held in Cairo, the Egyptian host government used the event’s global media and policy attention to promote its controversial New Administrative Capital: a multi-billion-dollar master-planned city that has been heavily criticised for mainly catering to the wealthy, remaining largely uninhabited, and crowding out investment in Egypt’s existing cities. The magazine The Economist poignantly called the project “Egypt's new pyramid scheme”.

There is no shortage of urgent urban challenges demanding new development approaches. The African continent is urbanising at an unprecedented rate, projected to double its urban population from 700 million in 2020 to 1.4 billion in 2050. Globally, approximately 1 billion people still live in informal settlements, often with insufficient access to clean water and sanitation. Urban areas account for over 70 per cent of global greenhouse gas emissions, rendering them indispensable for climate mitigation.

Yet, the planning approach favoured by many autocracies – and prominently on display at their WUF pavilions – to tackle these urban challenges often fails, principally because it is not designed to benefit the majority of their citizens. Rather, the approach, which the political scientist James Scott famously termed “authoritarian high modernism”, usually mainly serves regime interests and fantasies, such as entrenching political power and exerting social control. As an ideology, it privileges top-down master plans that prioritise state-led, coercive social engineering disconnected from people’s lived realities.

Apart from strengthening authoritarian regimes, history shows that this planning approach often exacerbates rather than solves urban problems. It tends to waste vast public and private resources, and often results in massive social and environmental damage. Prominent examples of authoritarian high modernism gone wrong include master-planned “new cities”, such as Malaysia’s “Forest City”, Saudi Arabia’s “The Line”, and Abu Dhabi’s failed eco-city “Masdar City”. By some estimates, two thirds of these new city projects remain nearly empty or completely uninhabited, resulting in “ghost cities”. Projects like these primarily benefit political elites, special interests, and investors – not citizens, their urban needs, and their human rights.

Alternative urban development models that prioritise urban upgrading rather than master plans, citizen inclusion rather than elite exclusivity, and residents’ needs rather than regime interests, are often less visually captivating. However, these more pluralist planning approaches frequently not only work better but are also essential for reducing urban inequality, including in informal settlements.

Presenting pluralist approaches to urban development at conferences such as WUF therefore matters. Thousands of policymakers, investors, journalists, and civil society representatives come together at such events to survey, discuss, and envision urban development pathways. At the last WUF in Cairo, Germany’s pavilion provided an important island of democratic discourse amidst a sea of autocratic blueprints. The pavilion was designed as a public forum, and brought together diverse perspectives and logics and facilitated inclusive discussions.

This year in Baku, however, there will be no German pavilion. Other democracies are also facing cuts to their development programmes and scaling back their presence at the conference. The absence of these pavilions and meeting spaces will represent lost opportunities for exposing WUF participants from around the world to more democratic, pluralistic, and sustainable urban development pathways.

Obviously, the urban visions presented at the pavilions will only be one dimension of the event. But if Germany and other democracies are serious about supporting partners to address urgent urban challenges in a fiscally responsible and effective way, they cannot opt out of the global competition of envisioning the cities of tomorrow. Germany should therefore consider organising a pavilion at the next WUF in Mexico City in 2028 and, together with its democratic partners, explore options for disrupting the authoritarian trend, including hosting a future forum itself.

Tax expenditures (TEs) are benefits granted through the tax system that lower government revenue and the taxliability of beneficiaries. Governments worldwide use TEs to pursue different policy goals such as attracting investment, boosting innovation and mitigating inequality. At the same time, TEs are costly: according to the Global Tax Expenditures Database (GTED), the worldwide average over the 1990-2023 period is 3.7 percent of GDP and 23.0 percent of tax revenue (Redonda et al., 2025). When ill designed, they can be ineffective in reaching their stated goals. They can also be highly distortive and trigger negative externalities. Yet, despite the fact that TEs have similar effects on public budgets as direct spending programmes, the lack of transparency in the TE field is striking, as only 116 out of 218 jurisdictions have reported on TEs at least once since 1990.1 In addition, the quality, regularity and scope of such reports are highly heterogeneous and, in many cases, do not allow to engage in meaningful discussions on the effectiveness and efficiency of TEs. The Global Tax Expenditures Transparency Index (GTETI) is the first comparative assessment of TE reporting covering jurisdictions worldwide. It provides a systematic framework to rank jurisdictions according to the regularity, quality and scope of their TE reports, and seeks to increase transparency and accountability in the TE field. Note that countries are not scored, ranked or compared on the size of revenue forgone reported, nor on the quality of their TE policy as such. This new version of the Companion Paper introduces the GTETI, outlines the updates made to the index since December 2024, and provides an in-depth explanation of its five dimensions and 25 indicators. It also discusses the rationale, scope, methodology, and assumptions underpinning the GTETI assessment process. The Companion Paper explains the limitations and issues users should bear in mind when consulting the index, which is publicly available free of charge on the Tax Expenditures Lab website, www.taxexpenditures.org.

Agentic AI is arriving in development cooperation before the sector has the means to govern it. Unlike the chatbots most practitioners have used, agentic systems take actions and coordinate on their own by calling other tools and stringing together steps to complete a task. When the OECD DAC Governance Network meets this month to discuss AI, the hard question to answer is how to adopt and govern such a rapidly evolving and increasingly autonomous technology. As the top donor among DAC countries, Germany should play a leadership role in the development sector’s technological transformation. The Federal Ministry for Economic Cooperation and Development (BMZ) can host a pooled technical capacity that experiments with AI tools and sets accountability standards. What it needs is policy willingness and an institutional framework that treats AI as a moving technological frontier.

The development cooperation sector is not ready for agentic AI

The pace of change is the first problem. In only eighteen months, the technological frontier has moved substantively from chatbots that retrieve information to systems that can generate data and act across multiple tools without supervision. Stanford’s AI Index 2025 documents that the cost of running these models has also fallen more than 280-fold over the same period, with lowered barriers to advanced AI. With innovation cycles measured in weeks, the development cooperation sector is at odds with this pace, with long procurement timelines and rigid bureaucracy. 

This mismatch creates a capacity problem. Development organisations are struggling to simultaneously track the AI innovations and build tools for their programming, while closing internal skills gaps. The likely outcome would be individual staff experimenting on the side, along with small in-house units using whichever commercial model is cheapest.

There are two implications of this capacity constraint. First, it will impede scalability. An OECD overview of existing AI use cases suggests that most pilots stay in the exploratory phase and rarely scale. Second, haphazard AI applications will lead to fragmentation with incompatible standards and create an accountability gap across the sector.

The deeper constraint is data. AI is only as capable as the information environment it draws on. In fragile states and digitally under-connected communities, where, for example, many under-resourced African languages barely appear in training data, agentic systems will produce misleading outputs, with grave implications for development programming.

BMZ’s leadership role

BMZ has done some groundwork that can be leveraged for sectoral reform. The BMZ Data Lab has developed pipelines and a ministerial chatbot (KIEZ). FAIR Forward has worked with seven partner countries on open AI data and policy, and produced insightful resources. The BMZ’s new reform plan also cites AI in three places, including the establishment of a new working hub for strategic foresight. But the plan still treats AI as an instrument to be deployed, not as a moving frontier that needs continuous institutional capacity to track.

BMZ could establish a team that serves as a sector-wide AI capability. Consider an agile, fast-moving unit of engineers and development practitioners working to pool resources across different stakeholders, develop protocols, and stress-test cutting-edge AI tools for donors and development organisations for safe yet faster adoption. Such an AI team would have a broader mandate than just serving the ministry internally. The team would work on shorter contracting and procurement cycles than the sector typically allows, with clauses that let tools evolve as the technology does. Smaller donors and other development organisations would tap the same resource through co-development, joint training, and open access to the tools and protocols it produces.

This kind of unit answers two problems at once. BMZ would be a central node for pooling resources together for joint capacity within the sector. And it can generate sector-specific algorithmic accountability standards, for example, mandatory decision-audit trails for any agentic AI system. These tools, in turn, will strengthen the BMZ to actively shape the normative discussions for the use of AI in development cooperation globally.

The same unit can also tackle the data problem. It could create guidelines for “data readiness assessments”, as a standard prerequisite for any AI-assisted programming (akin to environmental impact assessments). It can also help turn decades of tacit institutional knowledge within the sector into publicly available, AI-assisted, and actionable knowledge (going beyond KIEZ) that could, first, give more visibility to digitally under-connected communities, and second, empower practitioners globally. Instead of a 100-page static PDF toolkit, imagine an interactive system that a practitioner can ask: “What worked in contexts similar to mine?” and receive synthesised, sourced answers.

Germany should not miss this narrow window of opportunity to be the leading voice in AI transformation for development cooperation.

Artificial intelligence (AI) presents numerous challenges and opportunities for development cooperation. This column focuses on how AI can help development organisations become learning organisations. Whether it does so, however, depends on how AI-supported analysis and learning are embedded in organisations. Paradoxically, the US development agency USAID, which was closed in July 2025, is an interesting example of how and what can be learned for development cooperation with AI – and what its limits may be.

USAID’s aborted AI learning initiative

After many years as a USAID staff member, the second author of this column left the organisation in 2021 to study how development organisations can learn from their own experiences more systematically. This work resulted in the Development Evidence Large Language Model (DELLM), an AI system developed specifically for development cooperation. USAID became one of the first clients. To train DELLM, experts first manually coded countless text segments from the approximately 100,000 USAID evaluation reports, spanning more than 60 years. Increasingly automated but guided and supervised by experts, the model learned to ‘read’ these reports and distinguish between sectors, cooperation approaches, outcomes, positive and negative lessons and other aspects.

Once training was complete, USAID staff had the organisation’s vast institutional knowledge at their fingertips. DELLM could be used for operational planning as well as for organisational learning. In terms of operational planning, staff were now able to use the model to access subject-specific and regional insights from over six decades within minutes. Previously, each of these queries would have required months of research and incurred high costs. Due to the sudden closure of USAID in 2025, however, the model could not be further used for organisational learning. As long as evaluation reports were still internally accessible, DELLM was instead used to analyse and preserve insights from the organisation’s past. The results from this search for the most important overall success factors of USAID interventions are insightful in themselves. But they also suggest why translating AI-supported analysis into organisation learning is not straightforward.

Success factors for development cooperation

The search for the most important overarching success factors of USAID development interventions yielded the following insights: (1) Decision-making should take place as closely to the ground as possible in order to receive rapid implementation feedback and be able to adjust course. (2) Reforms should be practical, i.e. build on existing systems and seek to adapt them. (3) Effective solutions should be rapidly embedded locally so that they continue to work even after the end of funding. (4) Reform processes should be led by local actors rather than merely involving them in a consultative capacity. Finally, (5) collaboration with the middle tier is crucial – that is, with partners who are responsible for the practical implementation of measures. What do these five lessons tell us about the potential of AI-supported organisational learning for development cooperation and about its limits?

First, these insights are not new to development effectiveness experts. At the same time, approaches to development cooperation that are based on them are still rarely used. One example are approaches to adaptive development cooperation. Despite having been successfully implemented in some cases, including in German development cooperation, they remain exceptions. At the political level, systematic organisational learning is hampered by development policy’s focus on furthering national interests and by accountability concerns. But it is also held back by the structures and incentives within development ministries and agencies. Organisational learning is unlikely to happen unless evidence-based work is valued and encouraged, generalist career tracks are complemented by specialist ones, objectives and shortcomings are openly discussed and the organisation’s culture supports all that. 

Embedding AI-supported learning for development

AI has the potential to provide access to an organisation’s institutional knowledge in a comprehensive, intuitive and needs-based manner. This is particularly useful for operational planning. But in terms of organisational learning and reform, the specific advantages of AI and the necessary enabling conditions should be kept in mind. First, like for all AI-tools, the quality of their output is only as good as the quality of the underlying data and their training. Second, to translate insights into organisational learning, they have to be embedded in an open, self-critical and evidence-based organisational culture. Third, while AI-supported insights may not be entirely new, together with other sources of knowledge such as development research, they can serve to review and complement existing knowledge. In a conducive organisational environment, AI can therefore be an important tool among others to help transform development organisations into learning organisations and increase their effectiveness and efficiency.

Dr Michael Roll is a sociologist and senior researcher in the research department “Transformation of Political (Dis-)Order” at the German Institute of Development and Sustainability (IDOS) in Bonn.

Lindsey Moore is the CEO and Founder of DevelopMetrics and adjunct professor of AI and Policy at Georgetown University. Her work focuses on responsible artificial intelligence, evidence synthesis, and knowledge management for international development.

The transfer of development finance rests, primarily, on two pillars: first, human and organisational expertise, and, second, financial flows intermediated through trusted organisations. Today, both elements are being profoundly reshaped simultaneously by disruptive innovations - expertise by AI and financial flows by blockchain-based payment and smart contract solutions.

Traditionally, development finance depends on the expertise of professionals in development banks, international organisations, ministries, and other local partners who design and evaluate projects, assess risks, and allocate know-how and capital, based on accumulated knowledge and (often imperfect) data.

First, by processing vast datasets, ranging from satellite imagery to real-time economic indicators, AI systems can generate insights that surpass human analytical capacity in speed and scale. For development finance institutions (DFIs), this creates both an opportunity and a challenge: they must transition from being repositories of expertise to becoming orchestrators of AI-enabled knowledge systems.

At the same time, the second pillar of development finance, financial flows, is undergoing an equally profound transformation. Up to now, capital flows in development finance have largely been intermediated through centralised institutions. Blockchain technology disrupts this paradigm by enabling decentralised, transparent, and immutable financial infrastructures. Through smart contracts, funds can be disbursed automatically when predefined conditions are met.

The most profound transformation emerges from the interaction between these two disruptions. AI reshapes how decisions are made, while blockchain reshapes how those decisions can be executed. Together, they create a new paradigm of development finance, where capital allocation and disbursement become - when the right checks and balances are in place - automated, data-driven and verifiable.

From pilots to scale: Successful good practices around the globe

Highly successful pilots around the globe are allowing a glimpse into this near future: For instance, the Central Bank of Brazil has been testing decarbonisation tokens. When satellite data confirms a reduction in CO₂ emissions, these are automatically paid out to smallholder farmers.

In Syria, German international cooperation has launched a successful pilot initiative to digitise salary payments for healthcare workers. Years of political instability have significantly weakened the country’s banking system. Traditional transfer methods are frequently linked to corruption risks, burdensome administrative procedures, lengthy delays and high transaction costs. Payments to more than 900 health professionals via a stable digital currency have proven to be a highly efficient alternative. Moreover, between 2022-2025, UNHCR has supported over 240,000 people, who were forced to flee, with blockchain-based payments, thereby increasing the speed, efficiency and transparency of aid delivery.

How development finance institutions can adapt

Crucially, the dual disruption does not diminish the importance of DFIs, it elevates it. DFIs must govern data, ensure accountability and regulate decentralised systems. This will fundamentally alter the development finance architecture.

Against this backdrop, we make three observations. In terms of organisational design, DFIs will evolve towards platform-based orchestrators connecting data ecosystems, stakeholder and partnership management and decentralised finance infrastructures. Second, human resource management in DFIs is likely to focus on a new skill profile that combines AI for routine tasks with human experience for nuanced-decision-making skills and multidisciplinary connective capacity. Third, DFIs will need to invest in partnership ecosystems and turn increasingly into platform hubs to co-create solutions with tech firms and data providers.

To conclude, development finance is entering a new era defined by a double disruption and this comes, of course, with risks. To name only some: AI can produce biases and hallucinations. This makes human-centered AI (or augmented intelligence) all the more important. When evaluating any blockchain solution (e.g. crypto currency, stable coin, central bank digital currency, smart contract), it is essential to look closely at the entire creation and governance chain: who designed and developed the protocol, who issues the assets built on it, who operates and maintains the network, what externalities are (e.g. energy use) and, ultimately, who uses it.

But we believe the outlook of the digital disruption is positive – if we prepare well: There are substantial gains in terms of efficiency and transparency to be made.

Prof. Clara Brandi is Head of Department at the German Institute of Development and Sustainability (IDOS) and Professor of International Economics / Development Economics at the University of Bonn.

Dr Max Büge is a leading expert for AI and blockchain in finance. At GIZ, he manages an international project on digital finance for sustainability.

The opinions expressed are those of the authors and do not necessarily reflect those of their organisations.