On October 9, Kenya’s Court of Appeal cleared the way, for now, for the government to implement a five-year, approximately $2.5 billion health cooperation framework with the United States, including up to $1.6 billion in US support. The agreement provides for the sharing of aggregated health data and US access to Kenyan health information systems. A legal challenge to the framework, including its data-access provisions, is scheduled for a further hearing on October 30.

Kenya’s deal reflects a broader shift: access to data, digital infrastructure, and technology is becoming part of the bargain between wealthy donors and poorer recipient countries. As traditional aid budgets shrink, AI offers public institutions a way to do more with less, and health is an obvious place to start. Sub-Saharan Africa faces a shortage of nearly 6 million health workers, and Rwanda has one health care worker per 1,000 patients, less than a quarter of the threshold at which the World Health Organization defines a shortage. Tools that help clinicians triage, diagnose, and keep records could help close that gap.

Donors are already investing. In January, the Gates Foundation and OpenAI announced Horizon1000, committing $50 million in funding, technology, and technical support to bring AI tools to 1,000 primary health clinics in Africa by 2028, beginning in Rwanda.

Commitments like this could do real good, and AI’s potential to bring efficiencies, and even breakthroughs, to long-standing gaps in care is significant. To deliver on it, AI partnerships need to avoid the old problems of tied aid: long-term obligations that bind recipients to the priorities, standards, and suppliers of whoever provides the help. Whether health systems keep the freedom to choose their tools will depend on the terms of these deals, and on whether the public can see them.

When AI aid comes with strings

For decades after World War II, wealthy countries routinely attached a condition to international aid: recipients had to spend it on the donor country’s goods and services. Such arrangements became controversial because they restricted competition and undermined recipient choice. Estimates from the Organisation for Economic Cooperation and Development suggest that tied aid can increase project costs by 15 to 30 percent, and its Development Assistance Committee spent decades pressing members to untie their aid, particularly when it flowed to the least developed countries.

AI assistance risks reviving this arrangement in a subtler form, one that creates dependence without any requirement to buy. Free services come first, and the data architecture, workflows, skills, and running costs they leave behind can make switching steadily harder.

Increasingly, development assistance comes not only as financing, but also as technology, technical support, and credits for proprietary services. Horizon1000’s $50 million mixes funding with OpenAI technology. In May, Anthropic and the Gates Foundation pledged $200 million over four years for public goods across health, education and other sectors. Anthropic’s half of the commitment will consist of Claude usage credits and technical support, while the foundation will provide grants and program expertise. Among the initiatives is work to improve AI performance in African languages, with resulting datasets intended for public release. A Gates Foundation director told Reuters that governments’ fears of proprietary lock-in shaped its public-goods focus, and that the language datasets it produces will be released openly.

Those commitments bring important opportunities for support, and free credits do not by themselves bar a ministry from using another model. Still, credits usable with only one supplier can steer public institutions toward that provider, creating a form of de facto tying even without a contractual obligation to stay.

Companies also partner with governments directly. In February, Anthropic signed a three-year memorandum of understanding with Rwanda covering health, education, and the public sector, giving government developer teams Claude and Claude Code along with training and API credits. Tech Policy Press has previously examined accountability concerns in that deal. While the partnership emphasizes capacity building and local autonomy, the public announcement does not specify how the agreement addresses data handling, post-credit pricing, or exit rights—details that would help observers assess its long-term sustainability.

The larger question reaches beyond any one company: development institutions spent decades untying aid from donor-country suppliers, but have yet to develop comparable safeguards against dependency in assistance delivered through proprietary AI, credits, and infrastructure. What will matter is whether the datasets, interfaces, and workflows built with these tools stay useful if a ministry switches providers.

Early lessons from health data deals

Aid organizations and recipient countries should also guard against extractive data practices. Donors are already writing access to health data into aid deals. As of August, 34 countries had signed US health agreements, according to an August Senate letter to Rubio. Uganda’s signed agreement, ProPublica reported, gives US officials direct, real-time access to nine national health data systems for seven years, including the one that manages patients’ electronic medical records, though it calls for shared data to be aggregated with personal identifiers removed. U.S. senators asked whether such data could be shared with US-based third parties, including to train AI models, but no publicly available answers have been released. The State Department declined to discuss the letter, citing a policy against commenting on congressional correspondence, though a spokesperson said neither the US government nor private American companies receive personally identifiable information under the agreements.

In February, meanwhile, a diplomatic cable reported by Reuters instructed US diplomats to lobby against foreign rules on how US tech companies handle data, warning they could interfere with AI services.

These agreements do not establish that health data will be used to train commercial AI models, nor do they demonstrate AI vendor lock-in. What they show is that access to potentially valuable data can become a condition of development assistance, raising questions about who controls that data and who benefits from its future uses.

Together these cases suggest four ways aid that runs on data and AI can bind recipients:

• Vendor tying: one provider’s model embedded in public systems, raising the cost of switching.
• Pilot-to-lock-in: free deployment now, with costs that arrive after the grant ends.
• Data conditions: long-term access to health data as a condition of support.
• Standards pressure: pressure to adopt the donor’s rules on data flows and infrastructure.

These categories stretch the traditional meaning of tied aid, and each can reproduce its central problem: assistance that narrows the recipient’s freedom to choose. Data conditions in US health agreements are documented. Standards pressure shows up in US diplomacy, though the public record does not show it written into agreements. Vendor dependence and rising post-grant costs in AI partnerships remain risks, not established by the available public record. Without public contractual terms, governments and citizens cannot tell whether the technology they receive today will leave them real choices tomorrow.

Open terms make better deals

US government health agreements face statutory disclosure requirements, which senators have argued are not being fully met. As of late September, the US government had released the main texts of 16 of the 36 signed agreements, ten of them only in response to Freedom of Information Act requests from Public Citizen, and had published none of the associated data-sharing agreements; the one that is public, Kenya’s, was obtained and posted by a news outlet. Private and philanthropic AI partnerships are not subject to the same general public-disclosure framework, leaving gaps in what outsiders can assess about their terms.

Consider a hypothetical ministry three years into subsidized AI services for its clinics. Does it own the model adapted to its patients? Can it move their records to another provider? What will inference cost in year four, once the grant ends?

Recipient governments are active partners in these deals. Rwanda aims to use AI to build decision-support tools for its more than 60,000 community health workers, Health Policy Watch reported. Disclosure would strengthen governments like Rwanda’s, and it would help companies too: published terms build trust with ministries, make it easier to extend a partnership from one country to the next, and answer suspicion before it hardens. During the fight over the US health deals, Public Citizen warned they could erode the collective bargaining power African states built in response to COVID-19 vaccine inequities. A ministry that can see what others have negotiated is better placed to bargain for itself, and shared knowledge across countries strengthens their collective hand.

Applying tied aid’s lessons to the AI era

The first step toward a more equitable approach is disclosure. Any government, foundation, or company offering AI tools to public health or education systems as aid should publish the agreement’s material terms, even where the full contract carries narrow redactions for patient privacy, security, or trade secrets. At minimum, that means the model and infrastructure providers; data access, retention, and secondary-use rights; who controls fine-tuned models and other derived assets; post-grant prices; and exit provisions.

The second step is a credible path to exit. Where traditional aid reform sought to give recipients freedom to choose their suppliers, AI partnerships must also preserve their freedom to leave them. This requires three safeguards:

• Portability: a ministry can export its data and configurations in usable formats, get reasonable migration help, and switch models or providers without rebuilding its systems or paying punitive fees.
• Data control: recipient institutions keep enforceable rights to access, control, export, and govern their data, consistent with domestic privacy law, with clear terms on who controls models adapted from it.
• Independent evaluation: recipients can test performance themselves, including in local languages.

Donors and philanthropies could make disclosure and portability conditions of their grants. AI companies could publish standard partnership terms, building on commitments like open datasets, while recipient governments coordinate to demand common protections. The African Union’s Continental AI Strategy offers a framework for doing so, and the civil society coalition already mobilized around the US health deals could extend its scrutiny to AI partnerships.

Donors spent decades learning that aid tied to the giver’s interests undermines what makes development work: solutions that are locally led and locally owned. The AI partnerships now spreading through public health systems should start from that lesson instead of relearning it. AI could be among the most valuable assistance developing countries have received. Whether it lives up to that will depend on its terms, and on whether countries can see them.