February 19th, 2025
AI Hubs for Development
The emergence of generative AI (GenAI) technologies in the mainstream over the past few years have propelled the development and deployment of artificial intelligence (AI) across the globe. Market researchers point to the immense potential of AI in boosting industry growth from the business perspective to the point of a USD 15 trillion prospective rise in global GDP by 2030. It also portrays an increasing trend on government investment in the AI ecosystem, with AI-related investment likely to peak as high as 2.5 to 4% of GDP in the U.S. by 2026.
Considering AI’s vast and global supply chain with complex logistical networks through which AI products are sourced, developed, and delivered, initiatives to realise the potential of AI for growth emerge from all over the world. However, there is a need to consider the parallel value chain that encompasses deeper externalities caused by the inter-relationships across processes, actors, and stages of AI development. This invites inquiry on how AI development is organised, how responsibility is distributed among different actors, and the need to understand AI systems as not just a technology but a source of political and economic power. With this article, we attempt to identify the contributions to the AI ecosystem from global north and south countries, and point to the prevalence of big tech players as major actors in the ecosystem as partners for public-private initiatives on AI development.
To unpack these dynamics, we focus on the various “AI Hubs”, which have emerged to enable trans-national efforts with multi-stakeholder involvement for realising the AI opportunity. These Hubs often leverage private sector capacities and contributions by supporting societal needs and equitable growth to achieve national and global developmental goals.
Section 2: The movement around AI Hubs
Unbundling the Hub Model
AI Hubs, such as the one recently announced to be built in Africa as a part of Italy’s G7 presidency agenda, typically perform the role of a facilitator or orchestrator of multi-stakeholder groups and catalyse the strengthening of AI ecosystems. In effect, these Hubs convene resources, funding, talent, and expertise that connect private sector and philanthropic investments to the needs of an identified local or regional ecosystem with internally and globally aligned developmental priorities. These hubs typically create avenues for shared learning collaborations across sectors, actors, and often multiple countries, in a manner that fosters a conducive environment for shared growth and societal benefit. Unpacking major trends
Trend observed 1: Emergence of the public-private partnership models
The AI Hub models enable private-public partnerships and create space for collaboration. By aligning on aspects such as state capacity and digital upskilling, private actors leverage resources to strengthen the ecosystem holistically.
For instance, the Vector Institute was established by the Government of Canada, Ontario, and a consortium of private partners including Google, NVIDIA, and Deloitte among others. The institute focuses on building state capacity through various programs aimed at raising the AI fluency of public employees, with private actors providing mentorship and enabling the public sector’s in creating safeguards.
Similarly, the Brazilian government and Microsoft São Paulo collaboration focus on improving public sector efficiency, national resilience and fostering sustainability. Microsoft aims to boost cloud and AI infrastructure and set up data centres. Collaborating with different government bodies, the partnership strengthens to improve AI fluency and digital skilling of labour through the licensed courses and training programs built by Microsoft.
Trend Observed 2: Global knowledge transfer to enable capacity and growth
Such Hubs drive concrete and achievable goals and milestones that focus on making AI development more responsible, holistic, and equitable. Relying on varied partnership models, the transfer of knowledge is prioritised to invigorate local and regional economies.
For instance, the EU Hub, a collaboration between KPMG, Cranium and Microsoft, is focused on strengthening organisational readiness for the EU AI Act and engages with regulators and stakeholders in the ecosystem to ensure effective transference of capacity and knowledge. This makes the Hub a pivotal platform for shaping the future of AI in Europe.
The Innovation hub in Tunisia, built in collaboration with the NVIDIA Deep Learning Institute (DLI), offers the training, technologies, and business networks needed to help drive AI adoption across the continent, part of NVIDIA’s efforts to train 100,000 developers across Africa through the DLI over the next three years.
Trend observed 3: Alignment with national priorities
National governments are creating AI Hubs as part of their AI strategies to align with their national strategies and policy frameworks.
The AI Standards Hub, led by The Alan Turing Institute aligns with the UK’s National AI Strategy, to advance trustworthy and responsible AI focusing on the role that standards can play for governance and innovation. Furthermore, it focuses on informing AI governance practices domestically and internationally, promoting the development of standards that are sound, coherent, and effective.
Another example is the United Arab Emirates Government partnership with Mastercard to set up a Global AI Hub, aligning with the UAE’s Vision 2031, the Hub aims to integrate AI across various sectors, enhancing the nation’s digital infrastructure, promoting innovation, and positioning the UAE as a global leader in AI-driven solutions.
Section 3: The potential impact of AI Hubs
The current moment in the global AI ecosystem development is marked by what has been identified by experts as ‘AI Nationalism’, where major countries are aiming to position themselves as critical leaders in global AI innovation – centering their capacities and market as the focal point for investments and growth. This could manifest in the form of restrictive regulations around flow of data, export controls over manufacturing equipment, or other forms of assertion of control over AI related digital infrastructures. To counter the risks of a completely fragmented global ecosystem, the Hub model displays the emergence of multilateral initiatives, and global public-private partnerships that promote an ecosystem approach for AI development and deployment. This enables a unique opportunity to converge seemingly diverse priorities of different actors and nations. For instance, while some appear to be primarily government led or supported national initiatives, Hubs prioritise broader convening opportunities for cross-country exchanges, enable local voices in the regional and global discourse, and promote private sector collaborations across the value chain. To realise the benefits of this framing in a sustained and meaningful way, the following initiatives can be studied to signal to the areas that require continued emphasis within existing and new Hubs that emerge.
- Emphasising Responsible AI Frameworks in private sector collaborations: The role of the private sector is critical to such forums as they can contribute significantly to the capacities and associated infrastructures around development of AI systems. In such a context, it is essential to account for the risk of power concentration favouring Big Tech companies with monopolies around the foundational infrastructures of compute, cloud, or models. To that end, the collaborative space offered by such Hubs should prioritise providing pathways to counter the economic and profit-driven motives of such entities, and partnerships focused on research and advocacy on the need for responsible AI frameworks in the specific Hub’s context.
- Prioritising local ecosystems in translating global AI ecosystem related opportunities and capacities: Prioritising growth and access for the local small-scale start-up ecosystem in addition to larger public-private partnerships is pivotal within the Hub model. Without active support from the state to channel the high-cost resource intensive foundational infrastructure from large global private sector players, innovation and equitable growth in the local start-up ecosystem could appear limited. Efforts similar to the Hub model are undertaking initiatives addressing these challenges, focusing on building infrastructural access and global political alignment.
For instance, the GIZ Fairforward, along with the entrepreneurship initiative Make-IT and experts from the Ghana Tech Lab and IBM, began the Africa AI Accelerator in order to support start-ups in the fields of AI and machine learning.
- Enabling discoverability and targeted interventions via dedicated scoping: In the absence of specific scoping, the grand visions of Hubs often run the risk of not addressing the ground realities of practitioners. A clear articulation on the sector or value chain focus can help identify the unique offerings of the Hub and improve discoverability and access to the initiatives that best speak to the needs of practitioners.
The Green-AI Hub for SMEs, one component of the Federal Ministry‘s five-point programme “Artificial Intelligence for the Environment and Climate”, supports awareness and capacity building in companies specifically for the development and testing of resource-saving AI technologies, thus ensuring holistic support on advancing green AI via 20 pilot programs.
- Unifying parallel global and local agendas to collectively actualise impact: Isolated efforts attempting to convene the region around their contributions to the larger AI ecosystem while maintaining diverse priority could perpetuate fragmented discourse. Such efforts risk performing a counter-productive function of complicating access to knowledge, resources, and infrastructures in an already skewed ecosystem, where people, small start-ups, and local bodies tend to be exploited and remain distanced from opportunities and alignment with the larger global conversation. The need of the hour is an aligned political will and resource sharing to align existing localised efforts with the regional/global agendas.
The Digital Transformation Centre Rwanda makes this case by aligning itself with the regional digital policy needs and the Rwandan development agenda, and collaborating with Smart Africa to create a unified African digital single market.
Conclusion
With unified agendas and cohesive efforts on AI deployment, the Hub model presents an opportunity to realise a beneficial exchange of insights, share a global market equitably, and build cohesive strategies on boosting and centering the local AI ecosystem with responsible frameworks around people’s data and digital interactions. This model is also uniquely positioned to further the growing emphasis on supporting global AI development and deployment, as is reflected within the priorities of the French AI Action Summit this year. With a collaborative model on sharing knowledge, resources, and infrastructures, AI Hubs effectively align with priorities on public interest AI innovation, access, trust, and global governance.
To that end, the Hub’s multi-stakeholder approach can not only help in responsible AI development but also become a space for addressing AI’s potential benefits and concerns, especially surrounding ethical impacts on the environment, human biases, as well as trust and safety of such systems. Finally, more robust global AI governance and cooperative action, as signalled within global action priorities, are imperative to maximising the benefits and mitigating risks that could emerge from frenzied AI development.
Check/update stats in para 1 + have any hubs that we have mentioned shut down or have changed their main objectives
