In the PFA Accelerator, practitioners supporting children are teaching each other what works. Every Friday, more than 240 education, social work, and health professionals across Ukraine and Europe file reports on the same question: What happened when you tried to help a child this week?

“We are at the beginning of a new epoch,” Eric Schmidt declared at the RAISE Summit in Paris on 9 July 2025. The former Google CEO’s message grounded in what he calls the San Francisco Consensus carries unusual weight—not necessarily because of his past role leading one of tech’s giants, but because of his current one: advising heads of state and industry on artificial intelligence.

María, a pediatrician in Argentina, works with an AI diagnostic system that can identify rare diseases, suggest treatment protocols, and draft reports in perfect medical Spanish. But something crucial is missing. The AI provides brilliant medical insights, yet María struggles to translate them into action in her community. What is needed to realize the promise of the Age of Artificial Intelligence? Then she discovers the missing piece.

Imagine if you could control every device, system, and process in the world simply by talking to it in plain English—or any language you speak. No special commands to memorize. No programming skills required. No technical manuals to study. Just explain what you want in your own words, and it happens. This is the transformation Eric Schmidt described when he spoke about language becoming the “universal interface” for artificial intelligence.

When epidemiologists investigate a disease outbreak, they do not just match symptoms to known pathogens. They work through complex chains of evidence, test hypotheses, reconsider assumptions when data does not fit, and sometimes completely change their approach based on new information.

Imagine hiring an assistant who never sleeps, never forgets, can work on a thousand tasks simultaneously, and communicates with you in your own language. Now imagine having not just one such assistant, but an entire team of them, each specialized in different areas, all coordinating seamlessly to achieve your goals.

“If health workers do not share their challenges and solutions, we are bound to fail.” This declaration from a participant in the Teach to Reach initiative facilitated by The Geneva Learning Foundation (TGLF) cuts to the heart of a crisis that has long plagued global health technical assistance: the persistent gap between what external experts provide and what practitioners actually need.

“I did not realize how much I could do with what we already have.” A Nigerian health worker’s revelation captures what may be the most significant breakthrough in global health implementation during the current funding crisis.

Humanitarian, global health, and development organizations confront an unprecedented crisis. Donor funding is in a downward spiral, while needs intensify across every sector. Organizations face stark choices: reduce programs, cut staff, or fundamentally transform how they deliver results. Traditional capacity building models have become economically unsustainable.

Since 2019, when The Geneva Learning Foundation (TGLF) launched its first AI pilot project, we have been exploring how the Second Machine Age is reshaping learning.

In a climate of funding uncertainty, what if the most cost-effective investments in global health weren’t about supplies or infrastructure, but human networks that turn learning into action? In this short review article, we explore how peer learning networks that connect human beings to learn from and support each other can transform health outcomes with minimal resources.