GENEVA, Switzerland, 23 July 2025 (The Geneva Learning Foundation) –Today, The Geneva Learning Foundation (TGLF) announces the launch of “Learning to lead change on the frontline of climate change and health,” the inaugural course in a new certificate programme designed by and for professionals facing climate change impacts on health.
Enrollment is now open. The course will launch on 11 August 2025.
Two years ago today, nearly 5,000 health professionals from across the developing world gathered online for an unprecedented conversation. They shared something most climate scientists had never heard: detailed, firsthand accounts of how rising temperatures, extreme weather, and environmental changes were already devastating the health of their communities.
The stories were urgent and specific. A nurse in Ghana described managing surges of malaria after unprecedented flooding. A community health worker in Bangladesh explained how cholera outbreaks followed every major storm. A pharmacist in Nigeria watched children suffer malnutrition as crops failed during extended droughts.
“I can hear the worry in your voices,” one global health partner told participants during those historic July 2023 events, “and I really respect the time that you are giving to tell us about what is happening to you directly.”
Connecting the dots from individual impact to systemic crisis
While climate change dominates headlines for its environmental and economic impacts, a parallel health crisis has been quietly unfolding in clinics and hospitals across Africa, Asia, and Latin America. Health workers have become first-hand witnesses to climate change’s human toll.
Dr. Seydou Mohamed Ouedraogo from Burkina Faso described devastating floods that “really marked the memory of the inhabitants” and led to cascading health impacts.
Felix Kole from Gambia reported that “wells have turned to salty water” due to rising sea levels, while extreme heat meant “people are no longer sleeping inside their houses,” creating new security and health complications.
Rebecca Akello, a public health nurse from Uganda, documented malnutrition impacts directly: “During dry spells where there is no food, children come and their growth monitoring shows they really score low weight for age.”
Health professionals like Dr. Iktiyar Kandaker from Bangladesh already get that this is a systemic challenge: “Our health system is not prepared to actually address these situations. So this is a combined challenge… but it requires a lot of time to fix it.”
These health workers serve as what TGLF calls “trusted advisors”—over half describe themselves as being like “members of the family” to the populations they serve. Yet until now, they have had no structured way to learn from each other’s experiences or develop coordinated responses to climate health challenges.
Learning from those who know because they are there every day
“It is something that all of us have to join hands to be able to do the most we can to educate our communities on what they can do,” said Monica Agu, a community pharmacist from Nigeria who participated in the founding 2023 events. Her words captured the collaborative spirit that has driven the programme’s development.
The new certificate programme employs TGLF’s proven peer learning methodology, recognizing that health workers are already implementing life-saving climate adaptations with limited resources. During the 2023 events, participants shared examples of modified immunization schedules during heat waves, cholera outbreak management after flooding, and maintaining health services during extreme weather events.
“We believe that investing in health workers is one of the best ways to accelerate and strengthen the response to climate change impacts on health,” explains TGLF Executive Director Reda Sadki.
The programme has been developed from comprehensive analysis of health worker experiences documented since 2023. Most observations come from small and medium-sized communities in the most climate-vulnerable countries.
For health, a different kind of climate action
Unlike traditional climate programmes focused on policy or infrastructure, this initiative recognizes that effective climate health responses must be developed by those experiencing the impacts firsthand. The course enables health workers to share their own experiences, learn from colleagues facing similar challenges, and develop both individual and collective responses.
Dr. Eme Ngeda from the Democratic Republic of Congo captured this approach during the 2023 events: “We are all responsible for these climate disruptions. We must sensitize our populations in waste management and sensitize how to reform our healthcare providers to face resilience, face disasters.”
The programme connects leaders from more than 4,000 locally-led health organizations through TGLF’s REACH network, enabling them to become programme partners supporting their health workers in developing climate-health leadership skills.
Building global solutions by connecting local, indigenous knowledge and expertise
The inaugural course offers health professionals worldwide the opportunity to learn from documented experiences of colleagues who are facing unprecedented consequences of climate change on health. Rather than lectures or theoretical frameworks, the programme employs structured reflection and peer feedback cycles, enabling participants to develop actionable implementation plans informed by peer knowledge and global guidance.
The course covers four key areas based on health worker experiences:
Climate and environmental changes: Recognizing connections between climate and health in local communities.
Health impacts on communities: Understanding direct health impacts, food security, and mental health effects.
Community responses and adaptations: Implementing local solutions and innovations from peer experiences.
Participants earn verified certificates aligned to professional development competency frameworks. Upon completion, they join TGLF’s global community of health practitioners for ongoing peer support and collaboration.
The urgency of now
The programme launches at a critical moment. Climate change impacts on health are accelerating, particularly in low- and middle-income countries where health systems are least equipped to respond. Yet these same regions are producing innovative, resource-efficient solutions that could benefit communities worldwide.
As one health worker reflected during the 2023 events: “Although climate change is a global phenomenon, it is affecting very, very locally people in very different ways.” The new programme acknowledges this reality while creating pathways for local solutions to inform global responses.
The course is available in English and French, designed to work on mobile devices and basic internet connections. It is free for health workers in participating countries.
For health workers who have been managing climate impacts in isolation, the programme offers something unprecedented: the chance to learn from colleagues who truly understand their challenges and to contribute their own expertise to a growing global knowledge base.
As the climate health crisis deepens, the solutions may well come from those who have been living with its impacts longest—if we finally give them the platforms and recognition they deserve.
After the World Health Assembly’s adoption of ambitious global plan of action for climate and health, global and country stakeholders are meeting in Brasilia for the Global Conference on Climate and Health, ahead of COP30. Three critical observations emerged that illuminate why conventional global health approaches may be structurally inadequate for the challenges resulting from climate change impacts on health.
These observations carry particular significance for global health leaders who now possess a WHA-approved strategy and action plan, but lack proven mechanisms for rapid, community-led implementation in the face of an unprecedented set of challenges. They also matter for major funders whose substantial investments in policy and research have yet to be matched by commensurate support for the communities and health workers who will be the ones to translate better science and policy into action.
Signal 1: When funding disappears and demand explodes
Seventy percent of global health funding vanished, virtually overnight. This collapse comes precisely when the World Health Organization projects a shortage of 10 million health workers by 2030—six million in climate-vulnerable sub-Saharan Africa.
The World Bank calculates that climate change will generate 4.1-5.2 billion disease cases and cost $8.6-20.8 trillion by 2050 in low- and middle-income countries alone. Health systems must simultaneously manage unprecedented demand with drastically reduced resources.
Traditional technical assistance—flying experts to conduct workshops, cascade training through hierarchies—is more difficult to resource. By comparison, peer learning networks can reduce costs by 86 percent while achieving implementation rates seven times higher than conventional methods. Furthermore, 82 percent of participants in such networks continue independently after formal interventions end. Peer learning is especially well-suited to include health workers in conflict zones, refugee settings, and remote areas where climate vulnerability peaks—precisely the locations where traditional expert-led capacity building proves most difficult and expensive.
The funding crisis makes it more of an imperative than ever before to examine which approaches can scale effectively when resources contract. Organizations that recognize this shift early could achieve breakthrough results as traditional approaches become unaffordable.
Signal 2: Global expertise meets local reality
The World Health Assembly continues producing comprehensive action plans backed by thousands of expert hours. The climate and health action plan represents the pinnacle of this approach—technically excellent, evidence-based, globally applicable.
Yet the persistent implementation gap reflects deeper challenges about how knowledge flows between institutions and communities. Current theories of change assume that technical expertise, properly communicated, will lead to improved outcomes. Local knowledge gets framed as “barriers to implementation”, rather than recognized as essential intelligence for adaptation.
This creates a paradox. The WHO recognizes that “community-led initiatives that harness local knowledge and practices” are “fundamental for creating interventions that are both culturally appropriate and effective.” Health workers possess sophisticated understanding of how global frameworks must adapt to local realities. But systematic mechanisms for capturing and integrating knowledge and action remain underdeveloped.
Climate change manifests differently in each community—shifting disease patterns in Kenya differ from changing agricultural cycles in Bangladesh, which differ from altered water availability in Morocco. Health workers witness these changes daily, developing contextual responses that often remain invisible to global institutions. The question becomes whether global frameworks can evolve to recognize and systematically integrate this distributed intelligence rather than treating it as anecdotal evidence.
Signal 3: The policy-people gap widens if field-building ignores communities and is disconnected from local action
Substantial philanthropic funding is flowing toward climate and health policy and evidence generation. Some funders call this “field-building”. Research institutions develop sophisticated models. Policy frameworks become more comprehensive. Scientific understanding advances rapidly. These investments are producing genuinely better science and more effective policies—essential progress that must continue.
Yet investment in communities and health workers—the people who must implement policies and apply evidence—remains disproportionately small. This disparity creates concerning dynamics where knowledge advances faster than the capacity to apply it meaningfully in communities.
The risk extends beyond implementation gaps. When sophisticated policies and evidence develop without commensurate investment in community relationships, communities may reject even superior science and policies—not because they are irrational or too ignorant to recognize the benefits, but because the effort to accompany communities through change has been insufficient. Health workers, as trusted advisors within their communities, are uniquely positioned to bridge this gap by helping communities make sense of new evidence and adapt policies to local realities.
Health workers serve as trusted advisors within communities facing climate impacts. When investment patterns overlook this relationship, sophisticated policies risk becoming irrelevant to the people they aim to help. The trust networks essential for translating evidence into community action – and ensuring that evidence is relevant and useful – receive less attention than the evidence itself.
The pathway forward: Health workers as knowledge creators and leaders of change
These three signals point toward a fundamental misalignment between how global institutions approach climate and health challenges and how communities experience them. The funding crisis makes traditional expert-led approaches unsustainable. Implementation gaps persist because local knowledge remains systematically undervalued. Investment patterns favor sophisticated frameworks over the human relationships needed to apply them effectively.
When a community health worker in Nigeria notices malaria cases appearing earlier each season, or a nurse in Bangladesh observes heat-related illness patterns in specific neighborhoods, they are detecting signals that epidemiological studies might take years to document formally. This represents a form of “early warning system” that current approaches tend to overlook.
Recent innovations demonstrate different possibilities. Networks connecting health practitioners across countries through digital platforms treat health workers as knowledge creators rather than knowledge recipients. Such approaches have achieved, in other fields, implementation rates seven times higher than conventional technical assistance while reducing costs by 86 percent. There is no reason why applying these approaches would not result in similar results.
For the World Health Organization, such approaches could offer pathways to operationalize the Global Plan of Action through the very health workers the organization recognizes as “uniquely positioned” to champion climate action while building essential community trust.
For major funders, these models represent opportunities to complement policy and research investments with approaches that strengthen community capacity to apply sophisticated knowledge to local realities.
The evidence suggests that failure to bridge these gaps could prove more costly than the investment required to close them. But the returns—measured in communities reached, knowledge applied, and trust maintained—justify treating health worker networks as essential infrastructure for climate and health response rather than optional additions.
Three questions for leaders
As leaders prepare for the Global Climate Change and Health conference in Brasilia and begin work to implement climate and health commitments, three questions emerge from the World Health Assembly observations:
For institutions with comprehensive plans: How will technical excellence translate into community-level implementation when traditional capacity building approaches have become economically unsustainable?
For funders investing in research and policy: How can sophisticated evidence and frameworks reach the health workers and communities who must apply them to local realities?
For all climate and health leaders: What happens when policies advance faster than the trust relationships and implementation capacity needed to apply them effectively?
The signals from the World Health Assembly suggest that conventional approaches face structural constraints that incremental improvements cannot address. The funding crisis, implementation gaps, and investment disparities require responses that recognize health workers as partners in creating climate and health solutions rather than merely implementing plans created elsewhere.
The choice is not whether to transform approaches—resource constraints and community realities make transformation inevitable. The choice is whether leaders will direct that transformation toward approaches that strengthen both global knowledge and local capacity, or risk watching sophisticated frameworks fail for lack of community connection and trust.
References
Miller, J., Howard, C., Alqodmani, L., 2024. Advocating for a Healthy Response to Climate Change — COP28 and the Health Community. N Engl J Med 390, 1354–1356. https://doi.org/10.1056/NEJMp2314835
Sanchez, J.J., Gitau, E., Sadki, R., Mbuh, C., Silver, K., Berry, P., Bhutta, Z., Bogard, K., Collman, G., Dey, S., Dinku, T., Dwipayanti, N.M.U., Ebi, K., Felts La Roca Soares, M., Gudoshava, M., Hashizume, M., Lichtveld, M., Lowe, R., Mateen, B., Muchangi, M., Ndiaye, O., Omay, P., Pinheiro Dos Santos, W., Ruiz-Carrascal, D., Shumake-Guillemot, J., Stewart-Ibarra, A., Tiwari, S., 2025. The climate crisis and human health: identifying grand challenges through participatory research. The Lancet Global Health. https://doi.org/10.1016/S2214-109X(25)00003-8
Insights report about Nigeria’s Immunization Agenda 2030 Collaborative surfaces surprising solutions for both demand- and supply-side immunization challenges
When 4,434 practitioners from all 36 states asked why children in their communities remained unvaccinated, the problems they thought they understood often had entirely different root causes.
“I ended up being surprised at the answer I got,” said one health worker.
Half of the health workers who participated in Nigeria’s largest-ever peer learning exercise in July 2024 discovered that their initial assumptions about local immunization challenges were wrong. The six-week programme generated 409 detailed analyses of local immunization challenges, with each reviewed by peers across the country.
One year after The Geneva Learning Foundation launched the first Immunization Agenda 2030 Collaborative, in partnership with UNICEF and Gavi, under the auspices of the Nigeria Primary Health Care Development Agency (NPHCDA), a comprehensive insights report documents findings that illuminate persistent gaps between health system planning and community realities.
How to access the Nigeria Immunization Collaborative’s first insights report:
Download the report, as a comprehensive PDF for detailed reference and sharing;
Health workers report being asked for insights for first time
A recurring theme emerged from participant feedback that surprised programme organizers. “Many said no one has ever asked us what we think should happen or why do you think that is,” said TGLF’s Charlotte Mbuh, during the February 2025 presentation of the findings to NPHCDA and the country’s immunization partners.
This potential for linking community experience with formal planning processes became evident when systematic analysis revealed that participants consistently identified practical solutions—many of which they could implement with existing resources.
“Through my participation in the immunization Collaborative, I learned the critical value of root cause analysis,” reported one participant from Apo Resettlement Primary Health Centre in Abuja. “I applied this approach to uncover that insufficient manpower was the primary issue limiting vaccine coverage”—not the community resistance initially assumed.
Dr. Akinpelu Adetola, a government public health specialist in Lagos State, exemplified this pattern. Her investigation of declining vaccination rates revealed poor scheduling that created both overcrowded and quiet clinic days. “A register and scheduling system were introduced to address this issue,” she shared with colleagues from across the country.
Implementation gaps – not knowledge gaps – in the Nigeria Immunization Collaborative
The Collaborative’s most significant finding challenges a common assumption in global health programming. Participants consistently proposed solutions that were “already well-known, suggesting that progress is limited by implementation issues rather than a lack of solutions,” according to the evaluation report.
This pattern appeared across diverse contexts and challenge types. When health workers applied root cause analysis to local problems, they frequently identified straightforward interventions that had been overlooked by previous efforts focused on changing community attitudes or providing additional training.
The evaluation found that 42% of participating health workers identified zero-dose challenges as their top local priority—aligning with national strategy priorities while providing granular intelligence about how these challenges manifest in specific communities.
Nigeria Immunization Agenda 2030 Collaborative: Reconnecting data collection with local problem-solving
A striking finding illuminated a fundamental disconnect in Nigeria’s health information systems: only 25% of participants knew their local coverage rates for key vaccines, despite many being responsible for collecting and reporting these figures at the local levels.
“Many said, well, I collect these numbers, pass them on, but I didn’t know I could actually use them. They could actually help me in my work,” Mbuh explained, describing how participants began analyzing data they were already gathering within the first four weeks of the programme.
While participants initially focused on demand-side issues—why communities do not seek vaccination services—systematic investigation often revealed supply-side problems underlying apparent “hesitancy.”
Six primary supply-side challenges consistently undermine immunization delivery: poor data quality hampering service planning; vaccine stockouts due to inadequate inventory management; non-functional cold chain equipment; missed opportunities for catch-up vaccination; service quality issues that deter families; and systematic exclusion of hard-to-reach populations.
Scale, speed, and sustainability across a complex federal system
Launched by The Geneva Learning Foundation on 22 July 2024 in partnership with NPHCDA with support from UNICEF and Gavi, the Nigeria Immunization Agenda 2030 Collaborative connected health workers and other immunization stakeholders from more than 300 local government areas – with most based in northern States – within two weeks. Over 600 government facilities, private sector providers, and civil society organizations then signed on as organizational partners. Participants included 65% from local government and facility levels—both the community health workers who directly deliver immunization services and the LGA managers who support them.
The initiative achieved this scale while operating at faster speed and significantly lower cost than conventional technical assistance and capacity-building approaches.
The programme supported participants in using a simple, practical “five-whys” root cause analysis methodology, with each analysis reviewed by three peers across Nigeria’s diverse contexts. This peer review process provided depth to complement scale: it improved analytical quality regardless of participants’ initial skill levels.
“The peer review was another mind-blowing innovation where intellect from other parts of Nigeria viewed your work and made constructive input,” noted one reviewer. “It made me realize I can be a team player.”
Rapid implementation documented within weeks
Within six weeks, health workers began reporting connections between new activities based on their root cause analyses and improved health outcomes.
“During the Collaborative, we discussed successful case studies from other regions. Inspired by these stories, I have strengthened partnerships with local health authorities and other stakeholders to deepen immunization coverage, especially among under-fives. This collaboration has resulted in a significant increase in childhood vaccination rates in my community,” reported one participant from Ebonyi State.
Unlike conventional training programs that end with certificates, evidence emerged that participants were applying insights within their ongoing work responsibilities and sustaining collaboration independently.
Evidence of sustained networks and application one year later
In fact, evidence one year on points to surprising sustainability, as the network continues to function without any external support.
Four months after the programme concluded, TGLF organized a Teach to Reach session with 24,610 health workers participating, featuring Collaborative participants sharing early outcomes from the Nigeria initiative. This session revealed participants maintaining connections and applying methodologies in new contexts.
“When we applied the root cause analysis, the five ‘whys’, this opened our eyes to see that it was not all about community members alone,” reported Uyebi Enosandra, a disability specialist working in Delta State. “We have challenges with the primary health workers, not knowing how to incorporate children with disability in the immunization programme.”
Her account exemplified the pattern documented across participant testimonials: systematic analysis revealed different root causes than initially assumed, leading to more targeted solutions.
Gregory, a retired professional who participated in outbreak response work in Borno State, described encountering Collaborative participants in the field: “I was pleased to hear that they participated in the Collaborative. And whatever step I wanted to take, they were almost ahead of me to say, sir, we have learned this and we are going to apply it.”
“In my everyday activities at work I use this ‘5 whys’ to get to the root cause of any complaint and in my own little space make an impact on the patient,” one participant reported in follow-up feedback.
The methodology’s application extended beyond immunization contexts. Participants reported using the analytical framework for disability inclusion, malaria programming, and broader health system challenges, suggesting the transferable value of structured problem-solving approaches.
The December 2024 Teach to Reach session revealed ongoing demand for the methodology. Despite significant connectivity challenges affecting West Africa during the session, participants expressed eagerness to share the approach with colleagues. “Presently I’m even encouraging my colleagues to join,” one participant noted. “They’ve been asking me, how do I join, when will this come and all that.”
The most significant sustainability indicator, according to Mbuh, appeared in widespread participant feedback: “I did not realize how much I could do with what we already have.” This response gained particular relevance as Nigeria and other countries navigate current funding constraints affecting global health programming.
Potential to strengthen existing systems
For NPHCDA and international partners, the Collaborative provided intelligence typically unavailable through conventional assessments. The analysis of root cause analyses offers detailed insights into how challenges manifest across Nigeria’s diverse geographic and cultural contexts.
The approach demonstrated potential to complement existing training, supervision, and technical assistance systems by harnessing health workers’ practical experience and problem-solving capacity. The model addresses real-world challenges participants can immediately influence while building professional networks alongside technical competencies.
“This pilot programme has demonstrated demand for peer learning, and the feasibility of running a national peer learning programme that brings together the strengths of a national immunization programme, a global partner and an educational organization,” the evaluation concludes.
For Nigeria’s work toward Zero-Dose Immunization Recovery Plan goals through 2028, the Collaborative provides an innovative approach for translating national strategies into local action while building health worker capacity for continuous adaptation and problem-solving.
The programme has evolved into what participants describe as a self-sustaining platform that continues operating independent of formal support, suggesting potential for integration with existing health system structures and processes in a true “sector-wide” approach.
Reference
Jones, I., Sadki, R., Sequeira, J., & Mbuh, C. (2025). Nigeria Immunization Agenda 2030 Collaborative: Piloting a national peer learning programme (1.0). Nigeria Immunization Agenda 2030 Collaborative (IA2030), Nigeria. The Geneva Learning Foundation (TGLF). https://doi.org/10.5281/zenodo.14167168
Imagine a social worker in Ukraine supporting children affected by the humanitarian crisis. Thousands of kilometers away, a radiation specialist in Japan is trying to find effective ways to communicate with local communities. In Nigeria, a health worker is tackling how to increase immunization coverage in their remote village. These professionals face very different challenges in very different places. Yet when they joined their first “Impact Accelerator”, something remarkable happened. They all found a way forward. They all made real progress. They all discovered they are not alone.
The Impact Accelerator is a simple, practical method developed by The Geneva Learning Foundation that helps professionals turn intent into action, results, and outcomes. It has worked equally well in every country where it has been tried. It has helped people – whatever their knowledge domain or context – strengthen action and accelerate progress to improve health outcomes. Each time, in each place, whatever the challenge, it has produced the same powerful results.
The social worker joins other professionals facing similar challenges. The radiation specialist connects with safety experts dealing with comparable concerns. The health worker collaborates with others working to improve immunization. Each group shares a common purpose.
What makes the Impact Accelerator different?
Most training programs teach you something and then send you away. You return to your workplace full of ideas but face the same obstacles. You have new knowledge but struggle to apply it. (Some people call this “knowledge transfer” but it is not only about knowledge. Others call this the “applicability problem”.) You feel alone with your challenges.
The Impact Accelerator works differently. It stays with you as you implement change. It connects you with others facing similar challenges. It helps you take small, concrete steps each week toward your bigger goal.
Each Impact Accelerator brings together professionals working on the same type of challenge. Social workers who support children join with others who do the same – but the group may also include teachers and psychologists they do not usually work with. Safety specialists connect with safety specialists, but also people in other job roles. It is their shared purpose that makes this diversity productive: every discussion, every shared experience, every piece of advice directly applies to their work.
Think of it like learning to ride a bicycle. Traditional training is like someone explaining how bicycles work. The Impact Accelerator is like having someone run alongside you, keeping you steady as you pedal, cheering when you succeed, and helping you get back on when you fall. Everyone learns to ride, together. And everyone is going somewhere.
Every Monday, you decide on one specific action you will complete by Friday. Not a vague hope or a grand plan. One concrete thing you can actually do.
For example:
“I will create a safe space activity for five children showing signs of trauma.”
“I will develop a visual guide for the new radiation monitoring procedures.”
“I will meet with three community leaders to discuss vaccine concerns.”
You share this goal with others in the Accelerator. This creates accountability. You know that on Friday, your peers will ask how it turned out.
Wednesday: Check in with peers
Midweek, you connect with others in your group who face the same type of challenges. You share what is working, what is difficult, and what you are learning.
This is where magic happens. Someone else tried something that failed. Now you know to try differently. Another person found a creative solution. Now you can adapt it for your situation. You realize you are part of something bigger than yourself.
Friday: Report and reflect
On Friday, you report on your progress. Did you achieve your goal? What happened when you tried? What did you learn?
This is not about judging success or failure. Sometimes the most valuable learning comes from things that did not work as expected. The important thing is that you took action, you reflected on what happened, and you are ready to try again next week.
Monday again: Build on what you learned
The next Monday, you set a new goal. But now you are not starting from zero. You have the experience from last week. You have ideas from your peers. You have momentum.
Week by week, action by action, you make progress toward your larger goal.
The power of structured support in the Impact Accelerator
The Impact Accelerator provides several types of support to help you succeed.
Peer learning networks
You join a community of professionals who understand your challenges because they face similar ones.
Each Impact Accelerator brings together people working on the same type of challenge. This shared purpose means that every suggestion, every idea, every lesson learned is likely to be relevant to your work. The learning comes not from distant experts but from people doing the same work you do. Their solutions are practical and tested in real conditions like yours.
Guided structure
While you choose your own goals and actions, the Accelerator provides a framework that keeps you moving forward. The weekly rhythm creates momentum. The reporting requirements ensure reflection. The peer connections prevent isolation.
This structure is like the banks of a river. The water (your energy and creativity) flows freely, but the banks keep it moving in a productive direction.
Expert guidance when needed
Sometimes you need specific technical input or help with a particular challenge. The Accelerator provides “guides on the side” – experts who offer targeted support without taking over your process. They help you think through problems and connect you with resources, but you remain in charge of your own change effort.
What participants achieve
Across different countries and different challenges, Impact Accelerator participants report similar outcomes.
Increased confidence
“Before, I knew what should be done but felt overwhelmed about how to start. Now I take one step at a time and see real progress.” This confidence comes from successfully completing weekly actions and seeing their impact.
Tangible progress
Participants do not just learn about change; they create it. A vaccination program reaches new communities. Safety procedures actually get implemented. Children receive support when they need it. The changes may start small, but they are real and they grow.
Expanded networks
“I used to feel like I was the only one facing these problems. Now I have colleagues across my country who understand and support me.” These networks last beyond the Accelerator, providing ongoing support and collaboration.
Enhanced problem-solving
Through weekly practice and peer exchange, participants develop stronger skills for analyzing challenges and developing solutions. They learn to break big problems into manageable actions and to adapt based on results.
Resilience in facing obstacles
Every change effort faces barriers. The Accelerator helps participants expect these obstacles and work through them with peer support rather than giving up when things get difficult.
How can the same methodology work everywhere?
The Impact Accelerator has succeeded across vastly different contexts – from supporting children in Ukrainian cities to enhancing radiation safety in Japanese facilities to improving immunization in Nigerian villages. Each Accelerator focuses on one specific challenge area, bringing together professionals who share that common purpose. Why does the same approach work for such different challenges?
The answer lies in focusing on universal elements of successful change:
Breaking big goals into weekly actions;
Learning from peers who understand your specific context and challenges;
Reflecting on what works and what does not;
Building momentum through consistent progress; and
Creating accountability through a community united by shared purpose.
Each group focuses on their specific challenge and context, but the process of creating change remains remarkably similar.
A typical participant journey in the Impact Accelerator
Let us follow Yuliia, a social worker in Ukraine helping children affected by the humanitarian crisis.
Week 1: Getting started
Yuliia joins the Impact Accelerator after developing her action plan. Her big goal: establish effective psychological support for 50 displaced children in her community center within three months.
On Monday, she sets her first weekly goal: “During daily activities, I will observe and document how 10 children are affected.”
By Friday, she has detailed observations. She notices that loud noises sometimes cause reactions in most children, and several withdraw completely during group activities. This gives her concrete starting points.
Week 2: Building on learning
Based on her observations, Yuliia sets a new goal: “I will create a quiet corner with calming materials and test it with three children who are withdrawn.”
During the Wednesday check-in, another social worker shares how she uses art therapy for non-verbal expression with traumatized children. A colleague working in a different city describes success with sensory materials. Yuliia incorporates both ideas into her quiet corner.
The quiet corner proves successful – two of the three children spend time there and begin to engage with the materials. One child draws for the first time since arriving at the center.
Week 3: Creative solutions
Yuliia’s new goal: “I will develop a simple ‘feelings chart’ with visual cues and introduce it during morning circle time.”
Her peers from Ukraine and all over Europe – all working with children – help refine the idea. A psychologist from another region shares that abstract emotions are hard for traumatized children to identify. She suggests using colors and weather symbols instead of facial expressions. Another colleague recommends making the chart interactive rather than static.
The feelings chart becomes a breakthrough tool. Children who never spoke about their emotions begin pointing to images. Yuliia’s colleagues can better understand and respond to children’s needs.
Week 4: Scaling what works
Energized by success, Yuliia aims higher: “I will train two other staff members to use the quiet corner and feelings chart, and create a simple guide for these tools.”
By now, Yuliia has concrete evidence that these approaches work. She documents specific examples of children’s progress. Her guide is so practical that the center director wants to share it with other locations.
The ripple effect
Yuliia’s tools spread throughout the network of centers supporting displaced children. Through the Accelerator network, colleagues adapt her approaches for different age groups and settings. Soon, hundreds of children across Ukraine benefit from these simple but effective interventions.
The evidence of impact
The true test of any approach is whether it creates lasting change. Impact Accelerator participants consistently report:
Specific improvements in their work that they can measure and document;
Sustained changes that continue after the Accelerator ends;
Solutions that others adopt and spread;
Professional growth that enhances all their future work; and
Networks that provide ongoing support and learning.
These outcomes appear whether participants work on mental health support in Ukraine, radiation safety in Japan, or immunization in Nigeria. The challenges differ, but the pattern of success remains consistent.
How we prove the Accelerator makes a difference
In global health, the biggest challenge is proving that your intervention actually caused the improvements you see. This is called “attribution.” How do we know that better health outcomes happened because of the Impact Accelerator and not for other reasons?
The Geneva Learning Foundation solves this challenge through a three-step process that connects the dots between learning, action, and results.
Step 1: Measuring where we start
Before participants begin taking action, they document their baseline – the current situation they want to improve. For example:
A social worker records how many children show severe trauma symptoms.
A radiation specialist documents current safety incident rates.
A health worker notes the vaccination coverage in their area.
These starting numbers give us a clear picture of where improvement begins.
Step 2: Tracking progress and actions
Every week, participants complete “acceleration reports” that capture two things:
The specific actions they took; and
Any changes they observe in their measurements.
This creates a detailed record connecting what participants do to what happens as a result. Week by week, the picture becomes clearer.
Step 3: Proving the connection
Here is where the Impact Accelerator becomes special. When participants see improvements, they must answer a crucial question: “How much of this change happened because of what you learned and did through the Accelerator?”
But they cannot just claim credit. They must prove it to their peers by showing:
Exactly which actions led to which results;
Why the changes would not have happened without their intervention; and
Evidence that their specific approach made the difference.
This peer review process is powerful. Your colleagues understand your context. They know what is realistic. They can spot when claims are too bold or when someone is being too modest. They ask tough questions that help clarify what really caused the improvements.
After the first-ever Accelerator in 2019, we compared the implementation progress after six months between those who joined this final stage and a control group that also developed action plans, but did not join.
Why this method works
This approach solves several problems that make attribution difficult:
Traditional studies often cannot capture the complexity of real-world change. The Impact Accelerator’s method shows not just that change happened, but how and why it happened.
Self-reporting can be unreliable when people work alone. But when you must convince peers who understand your work, the reports become more accurate and honest.
Numbers alone do not tell the whole story. By combining measurements with detailed descriptions of actions and peer validation, we get a complete picture of how change happens.
The invitation to act
Around the world, professionals like you are transforming their work through the Impact Accelerator. They start with the same doubts you might have: “Can I really create change? Will this work in my context? Do I have time for this?”
Week by week, action by action, they discover the answer is yes. Yes, they can create change. Yes, it works in their context. Yes, they can find time because the Accelerator fits into their real work rather than adding to it.
The Impact Accelerator does not promise overnight transformation. It offers something better: a proven process for creating real, sustainable change through your own efforts, supported by peers who understand your journey.
If you work in a field where you seek to make a difference, the Impact Accelerator can help you move from good intentions to meaningful impact. The same process can work for you.
The question is not whether the Impact Accelerator can help you create change. The question is: What change do you want to create?
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?
Their answers – grounded in their daily work – are creating new insights into how Psychological First Aid (“PFA”) works in active conflict zones, displacement centers, and communities hosting Ukrainian families. These practitioners implement practical actions with children each week, then share what they learn with colleagues from all over Europe who face similar challenges.
The tracking reveals stark patterns. More than half work with children showing anxiety, fear, and stress responses triggered by air raids, family separation, or displacement. Another 42% focus on children struggling to connect with others in unfamiliar places—Ukrainian teenagers isolated in Polish schools, families in Croatian refugee centers, children moved from eastern Ukraine to western regions.
“We have a very unique experience that you cannot get through lectures,” said PFA practitioner and Ukrainian-language facilitator Hanna Nyzkodobova during Monday’s session, speaking to over 200 of her peers. “The Ukrainian context is not comparable to any other country.”
Locally-led organizations leading implementation
The programme’s most striking feature is its reach into organizations operating closest to active hostilities—precisely where support needs are most acute and convention training programs may not operate. For example, the charitable foundation “Everything will be fine Ukraine” implements approaches within 20 kilometers of active fighting, supporting 6,000 children across Donetsk, Dnipropetrovsk, and Kharkiv regions. Weekly reports from their participants document how psychological first aid help when air raid sirens interrupt sessions or when families face repeated displacement.
Posmishka UA, Ukraine’s largest participating organization with over 400 staff members, demonstrates how peer learning can support local actors directly at scale. During Monday’s learning session, Posmishka participants shared experiences from work in local communities that would be difficult to capture through conventional research or training approaches.
South Ukrainian National Pedagogical University has integrated the program across 339 faculty and 3,783 students, bringing PFA into the work of its Mental Health Center. Youth Platform is now offering PFA to 600 young people aged 14-35 across five Ukrainian regions, while the All-Ukrainian Public Center “Volunteer” scales implementations to over 10,000 children nationwide.
These partnerships reveal something crucial: when crisis response is most urgent, peer learning between local actors may prove more effective and sustainable than waiting for external expertise and costly training to develop solutions.
Learning what works through implementation
The Geneva Learning Foundation (TGLF) and the International Federation of Red Cross and Red Crescent Societies (IFRC), within the project Provision of quality and timely psychological first aid to people affected by Ukraine crisis in impacted countries, supported by the European Union, created what they call the PFA Accelerator—a component of a broader certificate program reaching over 330 organizations supporting more than 1 million children affected by the humanitarian crisis in Ukraine. This “Accelerator” methodology emerged from recognizing that new approaches are necessary in unprecedented crises. When children face trauma from active conflict, family separation, and repeated displacement simultaneously, guidelines can help but cannot tell you how to adapt to your specific situation.
The breakthrough lies in turning scale from an obstacle into an advantage. Rather than trying to train individuals who then work in isolation, the programme creates learning networks where practitioners immediately share what works, what doesn’t, and why.
Analysis of the first 60 action plans shows PFA Accelerator participants setting specific, measurable goals: 88% of those working with anxious children plan concrete emotional regulation activities rather than vague “support” approaches.
Iryna from Kryvyi Rih reported that schools actively sought partnerships after her initial outreach succeeded: “They wanted us to come to them,” she said, describing how her mobile facilitation team exceeded the goal she set for herself in the Accelerator – because she managed to help school administrators recognize the value of Psychological First Aid (PFA) for children.
Practical innovations emerge from necessity
The weekly implementation requirement forces creative problem-solving with limited resources. Mariya from Zaporizhzhia described combining parent and child sessions: “We conducted joint sessions with psychosocial support, where together we learned calming techniques and did exercises oriented toward team building.” This approach addressed both parent stress and child needs while optimizing scarce time and space resources.
In the PFA Accelerator, other participants can then share their feedback – or realize that Mariya’s local solution can help them, too. “The exchange of experience that happens on this platform is very important because someone is more experienced, someone less experienced,” noted participant Liubov during the Ukrainian session.
Such practical adaptations become documented knowledge shared across the network. However, in the first week, although 82% identify colleague support as their primary resource, only 49% initially planned collaborative approaches involving other adults. The peer feedback process helps participants recognize such patterns and adjust their methods accordingly.
Defying distance to solve problems together
What emerges is not only better implementation of existing approaches—it’s new knowledge about how psychological support works under difficult conditions. The weekly reports create rapid feedback loops showing which approaches help children cope with ongoing uncertainty, how to maintain therapeutic relationships during displacement, and which interventions remain effective when basic safety cannot be guaranteed.
The programme operates across Ukraine and 27 European countries, supported by over 80 European focal points and more than 20 organizational partners. This enables pattern recognition impossible without scale. Practitioners can better discern which approaches work across different contexts, how cultural differences affect intervention effectiveness, and which methods prove most adaptable to rapidly changing circumstances.
The larger significance extends beyond Ukraine. By demonstrating how local actors can rapidly develop and refine effective practices when given proper structure for peer learning, the programme offers a model for responding to other crises where traditional expert-led approaches prove too slow or disconnected from local realities. Sometimes the most valuable expertise exists not in training manuals but in the accumulated experience of practitioners working directly with affected populations.
This project is funded by the European Union. Its contents are the sole responsibility of TGLF and IFRC, and do not necessarily reflect the views of the European Union.
“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.
“When I talk to governments, what I tell them is, one, ChatGPT is great, but that was two years ago. Everything’s changed again. You’re not prepared for it. And two, you better get organized around it—the good and the bad.”
At the Paris summit, he shared what he calls the “San Francisco Consensus”—a convergence of belief among Silicon Valley’s leaders that within three to six years AI will fundamentally transform every aspect of human activity.
Whether one views this timeline as realistic or delusional matters less than the fact that the people building AI systems—and investing hundreds of billions in infrastructure—believe it. Their conviction alone makes the Consensus a force shaping our immediate future.
“There is a group of people that I work with. They are all in San Francisco, and they have all basically convinced themselves that in the next two to six years—the average is three years—the entire world will change,” Schmidt explained. (In the past, he initially referred to the Consensus as a kind of inside joke.)
He carefully framed this as a consensus rather than fact: “I call it a consensus because it’s true that we agree… but it’s not necessarily true that the consensus is true.”
Schmidt’s own position became clear as he compared the arrival of artificial general intelligence (“AGI”) to the Enlightenment itself. “During the Enlightenment, we as humans learned from going from direct faith in God to using our reasoning skills. So now we have the arrival of a new non-human intelligence, which is likely to have better reasoning skills than humans can have.”
The three pillars of the San Francisco Consensus
The Consensus rests on three converging technological revolutions:
1. The language revolution
Large language models like ChatGPT captured public attention by demonstrating AI’s ability to understand and generate human language. But Schmidt emphasized these are already outdated. The real transformation lies in language becoming a universal interface for AI systems—enabling them to process instructions, maintain context, and coordinate complex tasks through natural language.
2. The agentic revolution
“The agentic revolution can be understood as language in, memory in, language out,” Schmidt explained. These are AI systems that can pursue goals, maintain state across interactions, and take actions in the world.
His deliberately mundane example illustrated the profound implications: “I have a house in California, I want to build another one. I have an agent that finds the lot, I have another agent that works on what the rules are, another agent that works on designing the building, selects the contractor, and at least in America, you have an agent that then sues the contractor when the house doesn’t work.”
The punchline: “I just gave you a workflow example that’s true of every business, every government, and every group human activity.”
3. The reasoning revolution
Most significant is the emergence of AI systems that can engage in complex reasoning through what experts call “inference”—the process of drawing conclusions from data—enhanced by “reinforcement learning,” where systems improve by learning from outcomes.
“Take a look at o3 from ChatGPT,” Schmidt urged. “Watch it go forward and backward, forward and backward in its reasoning, and it will blow your mind away.” These systems use vastly more computational power than traditional searches—”many, many thousands of times more electricity, queries, and so forth”—to work through problems step by step.
The results are striking. Google’s math model, says Schmidt, now performs “at the 90 percentile of math graduate students.” Similar breakthroughs are occurring across disciplines.
What is the timeline of the San Francisco Consensus?
The Consensus timeline seems breathtaking: three years on average, six in Schmidt’s more conservative estimate. But the direction matters more than the precise date.
“Recursive self-improvement” represents the critical threshold—when AI systems begin improving themselves. “The system begins to learn on itself where it goes forward at a rate that is impossible for us to understand.”
After AGI comes superintelligence, which Schmidt defines with precision: “It can prove something that we know to be true, but we cannot understand the proof. We humans, no human can understand it. Even all of us together cannot understand it, but we know it’s true.”
His timeline? “I think this will occur within a decade.”
The infrastructure gamble
The Consensus drives unprecedented infrastructure investment. Schmidt addressed this directly when asked about whether massive AI capital expenditures represent a bubble:
“If you ask most of the executives in the industry, they will say the following. They’ll say that we’re in a period of overbuilding. They’ll say that there will be overcapacity in two or three years. And when you ask them, they’ll say, but I’ll be fine and the other guys are going to lose all their money. So that’s a classic bubble, right?”
But Schmidt sees a different logic at work: “I’ve never seen a situation where hardware capacity was not taken up by software.” His point: throughout tech history, new computational capacity enables new applications that consume it. Today’s seemingly excessive AI infrastructure will likely be absorbed by tomorrow’s AI applications, especially if reasoning-based AI systems require “many, many thousands of times more” computational power than current models.
The network effect trap
Schmidt’s warnings about international competition reveal why AI development resembles a “network effect business”—where the value increases exponentially with scale and market dominance becomes self-reinforcing. In AI, this manifests through:
More data improving models;
Better models attracting more users;
More users generating more data; and
Greater resources enabling faster improvement.
“What happens when you’ve got two countries where one is ahead of the other?” Schmidt asked. “In a network effect business, this is likely to produce slopes of gains at this level,” he said, gesturing sharply upward. “The opponent may realize that once you get there, they’ll never catch up.”
This creates what he calls a “race condition of preemption”—a term from computer science describing a situation where the outcome depends critically on the sequence of events. In geopolitics, it means countries might take aggressive action to prevent rivals from achieving irreversible AI advantage.
The scale-free domains
Schmidt believes that some fields will transform faster due to their “scale-free” nature—domains where AI can generate unlimited training data without human input. Exhibit A: mathematics. “Mathematicians with whiteboards or chalkboards just make stuff up all day. And they do it over and over again.”
Software development faces similar disruption. When Schmidt asked a programmer what language they code in, the response—”Why does it matter?”—captured how AI makes specific technical skills increasingly irrelevant.
Critical perspectives on the San Francisco Consensus
The San Francisco Consensus could be wrong. Silicon Valley has predicted imminent breakthroughs in artificial intelligence before—for decades, in fact. Today’s optimism might reflect the echo chamber of Sand Hill Road. Fundamental challenges remain: reliability, alignment, the leap from pattern matching to genuine reasoning.
But here is what matters: the people building AI systems believe their own timeline. This belief, held by those controlling hundreds of billions in capital and the world’s top technical talent, becomes self-fulfilling. Investment flows, talent migrates, governments scramble to respond.
Schmidt speaks to heads of state because he understands this dynamic. The consensus shapes reality through sheer force of capital and conviction. Even if wrong about timing, it is setting the direction. The infrastructure being built, the talent being recruited, the systems being designed—all point toward the same destination.
The imperative of speed
Schmidt’s message to leaders carried the urgency of hard-won experience: “If you’re going to [invest], do it now and move very, very fast. This market has so many players. There’s so much money at stake that you will be bypassed if you spend too much time worrying about anything other than building incredible products.”
His confession about Google drove the point home: “Every mistake I made was fundamentally one of time… We didn’t move fast enough.”
This was not generic startup advice but specific warning about exponential technologies. In AI development, being six months late might mean being forever behind. The network effects Schmidt described—where leaders accumulate insurmountable advantages—are already visible in the concentration of AI capabilities among a handful of companies.
For governments crafting AI policy, businesses planning strategy, or education institutions charting their futures, the timeline debate misses the point. Whether recursive self-improvement arrives in three years or six, the time to act is now. The changes ahead—in labor markets, in global power dynamics, in the very nature of intelligence—demand immediate attention.
Schmidt’s warning to world leaders was not about a specific date but about a mindset: those still debating whether AI represents fundamental change have already lost the race.
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. Through a peer learning network—where health workers develop projects addressing real challenges, review each other’s work, and engage in facilitated dialogue—she connects with other health professionals across Latin America who are learning to work with AI as a collaborative partner. Together, they discover that AI becomes far more useful when combined with their understanding of local contexts, cultural practices, and community dynamics.
This speculative scenario, based on current AI developments and existing peer learning successes, illuminates a crucial insight as we ascend into the age of artificial intelligence. Eric Schmidt’s San Francisco Consensus predicts that within three to six years, AI will reason at expert levels, coordinate complex tasks through digital agents, and understand any request in natural language.
Understanding how peer learning can bridge AI capabilities and human thinking and action is critical to prepare for this future.
Collaboration in the Age of Artificial Intelligence
The three AI revolutions—language interfaces, reasoning systems, and agentic coordination—will offer unprecedented capabilities. If access is equitable, this will be available to any health worker, anywhere. Yet having access to these tools is just the beginning. The transformation will require humans to learn together how to collaborate effectively with AI.
Consider what becomes possible when health workers combine AI capabilities with collective human insight:
AI analyzes disease patterns; peer networks share which interventions work in specific cultural contexts.
AI suggests optimal treatment protocols; practitioners adapt them based on local resource availability.
AI identifies at-risk populations; community workers know how to reach them effectively.
The magic happens in integration of AI and human capabiltiies through peer learning. Think of it this way: AI can analyze millions of health records to identify disease patterns, but it may not know that in your district, people avoid the Tuesday clinic because that is market day, or that certain communities trust traditional healers more than government health workers.
When epidemiologists share these contextual insights with peers facing similar challenges—through structured discussions and collaborative problem-solving—they learn together how to adapt AI’s analytical power to local realities.
For example, when an AI system identifies a disease cluster, epidemiologists in a peer network can share strategies for investigating it: one colleague might explain how they gained community trust for contact tracing, another might share how they adapted AI-generated survey questions to be culturally appropriate, and a third might demonstrate how they used AI predictions alongside traditional knowledge to improve outbreak response.
This collective learning—where professionals teach each other how to blend AI’s computational abilities with human understanding of communities—creates solutions more effective than either AI or individual expertise could achieve alone.
Understanding peer learning in the Age of Artificial Intelligence
Peer learning is not about professionals sharing anecdotes. It is a structured learning process where:
Participants develop concrete projects addressing real challenges in their contexts, such as improving vaccination coverage or adapting AI tools for local use.
Peers review each other’s work using expert-designed rubrics that ensure quality while encouraging innovation.
Facilitated dialogue sessions help surface patterns across different contexts and generate collective insights.
Continuous cycles of action, reflection, and revision transform individual experiences into shared wisdom.
Every participant becomes both teacher and learner, contributing their unique insights while learning from others.
This approach differs fundamentally from traditional training because knowledge flows horizontally between peers rather than vertically from experts. When applied to human-AI collaboration, it enables rapid collective learning about what works, what fails, and why.
Why peer networks unlock the potential of the Age of Artificial Intelligence
Contextual intelligence through collective wisdom
AI systems train on global data and identify universal patterns. This is their strength. Human practitioners understand local contexts intimately. This is theirs. Peer learning networks create bridges between these complementary intelligences.
When a health worker discovers how to adapt AI-generated nutrition plans for local food availability, that insight becomes valuable to peers in similar contexts worldwide. Through structured sharing and review processes, the network creates a living library of contextual adaptations that make AI recommendations actionable.
Trust-building in the age of AI
Communities often view new technologies with suspicion. The most sophisticated AI cannot overcome this alone. But when local health workers learn from peers how to introduce AI as a helpful tool rather than a threatening replacement, acceptance grows.
In peer networks, practitioners share not just technical knowledge but communication strategies through structured dialogue: how to explain AI recommendations to skeptical patients, how to involve community leaders in AI-assisted health programs, how to maintain the human touch while using digital tools. This collective learning makes AI acceptable and valuable to communities that might otherwise reject it.
Distributed problem-solving
When AI provides a diagnosis or recommendation that seems inappropriate for local conditions, isolated practitioners might simply ignore it. But in peer networks with structured review processes, they can explore why the discrepancy exists and how to bridge it.
A teacher receives AI-generated lesson plans that assume resources her school lacks. Through her network’s collaborative problem-solving process, she finds teachers in similar situations who have created innovative adaptations. Together, they develop approaches that preserve AI’s pedagogical insights while working within real constraints.
The new architecture of collaborative learning
Working effectively with AI requires new forms of human collaboration built on three essential elements:
Reciprocal knowledge flows
When everyone has access to AI expertise, the most valuable learning happens between peers who share similar contexts and challenges. They teach each other not what AI knows, but how to make AI knowledge useful in their specific situations through:
Structured project development and peer review;
Regular assemblies where practitioners share experiences;
Documentation of successful adaptations and failures;
Continuous refinement based on collective feedback.
Structured experimentation
Peer networks provide safe spaces to experiment with AI collaboration. Through structured cycles of action and reflection, practitioners:
Try AI recommendations in controlled ways;
Document what works and what needs adaptation using shared frameworks;
Share failures as valuable learning opportunities through facilitated sessions;
Build collective knowledge about human-AI collaboration.
Continuous capability building
As AI capabilities evolve rapidly, no individual can keep pace alone. Peer networks create continuous learning environments where:
Early adopters share new AI features through structured presentations;
Groups explore emerging capabilities together in hands-on sessions;
Collective intelligence about AI use grows through documented experiences;
Everyone stays current through shared discovery and regular dialogue.
Evidence-based speculation: imagining peer networks that include both machines and humans
While the following examples are speculative, they build on current evidence from existing peer learning networks and emerging AI capabilities to imagine near-future possibilities.
The Nigerian immunization scenario
Based on Nigeria’s successful peer learning initiatives and current AI development trajectories, we can envision how AI-assisted immunization programs might work. AI could help identify optimal vaccine distribution patterns and predict which communities are at risk. Success would come when health workers form peer networks to share:
Techniques for presenting AI predictions to community leaders effectively;
Methods for adapting AI-suggested schedules to local market days and religious observances;
Strategies for using AI insights while maintaining personal relationships that drive vaccine acceptance.
This scenario extrapolates from current successes in peer learning for immunization in Nigeria to imagine enhanced outcomes with AI partnership.
Climate health innovation networks
Drawing from existing climate health responses and AI’s growing environmental analysis capabilities, we can project how peer networks might function. As climate change creates unprecedented health challenges, AI models will predict impacts and suggest interventions. Community-based health workers could connect these ‘big data’ insights with their own local observations and experience to take action, sharing innovations like:
Using AI climate predictions to prepare communities for heat waves;
Adapting AI-suggested cooling strategies to local housing conditions;
Combining traditional knowledge with AI insights for water management.
These possibilities build on documented peer learning successes in sharing health workers observations and insights about the impacts of climate change on the health of local communities.
Addressing AI’s limitations through collective wisdom
While AI offers powerful capabilities, we must acknowledge that technology is not neutral—AI systems carry biases from their training data, reflect the perspectives of their creators, and can perpetuate or amplify existing inequalities. Peer learning networks provide a crucial mechanism for identifying and addressing these limitations collectively.
Through structured dialogue and shared experiences, practitioners can:
Document when AI recommendations reflect biases inappropriate for their contexts;
Develop collective strategies for identifying and correcting AI biases;
Share techniques for adapting AI outputs to ensure equity;
Build shared understanding of AI’s limitations and appropriate use cases.
This collective vigilance and adaptation becomes essential for ensuring AI serves all communities fairly.
What this means for different stakeholders
For funders: Investing in collaborative capacity
The highest return on AI investment comes not from technology alone but from building human capacity to use it effectively. Peer learning networks:
Multiply the impact of AI tools through shared adaptation strategies;
Create sustainable capacity that grows with technological advancement;
Generate innovations that improve AI applications for specific contexts;
Build resilience through distributed expertise.
For practitioners: New collaborative competencies
Working effectively with AI requires skills best developed through structured peer learning:
Partnership mindset: Seeing AI as a collaborative tool requiring human judgment.
Adaptive expertise: Learning to blend AI capabilities with contextual knowledge.
Reflective practice: Regularly examining what works in human-AI collaboration through structured reflection.
Knowledge sharing: Contributing insights through peer review and dialogue that help others work better with AI.
For policymakers: Enabling collaborative ecosystems
Policies should support human-AI collaboration by:
Funding peer learning infrastructure alongside AI deployment;
Creating time and space for structured peer learning activities;
Recognizing peer learning as essential professional development;
Supporting documentation and spread of effective practices.
AI-human transformation through collaboration: A comparative view
Working with AI individually
Working with AI through structured peer networks
Powerful tools but limited adaptation Insights remain isolated Success depends on individual skill
Continuous adaptation through structured sharing Insights multiply across network through peer review Collective wisdom enhances individual capability
AI recommendations may miss local context Trial and error in isolation Slow spread of effective practices
Context-aware applications emerge through dialogue Structured experimentation with collective learning Rapid diffusion through documented innovations
Overwhelmed by rapid AI changes Struggling to keep pace alone Uncertainty about appropriate use
Collective sense-making through facilitated sessions Shared discovery in peer projects Growing confidence through structured support
The collaborative future
As AI capabilities expand, two paths emerge:
Path 1: Individuals struggle alone to make sense of AI tools, leading to uneven adoption, missed opportunities, and growing inequality between those who figure it out and those who do not.
Path 2: Structured peer networks enable collective learning about human-AI collaboration, leading to widespread effective use, continuous innovation, and shared benefit from AI advances.
What determines outcomes is how humans organize to learn and work together with AI through structured peer learning processes.
María’s projected transformation
Six months after her initial struggles, we can envision how María’s experience might transform. Through structured peer learning—project development, peer review, and facilitated dialogue—she could learn to see AI not as a foreign expert imposing solutions, but as a knowledgeable colleague whose insights she can adapt and apply.
Based on current peer learning practices, she might discover techniques from colleagues across Latin America and the rest of the world:
Methods for using AI diagnosis as a conversation starter with traditional healers;
Strategies for validating AI recommendations through community health committees;
Approaches for using AI analytics to support (not replace) community knowledge.
Following the pattern of peer learning networks, Maríawould begin contributing her own innovations through structured sharing, particularly around integrating AI insights with indigenous healing practices. Her documented approaches would spread through peer review and dialogue, helping thousands of health workers make AI truly useful in their communities.
Conclusion: The multiplication effect
AI transformation promises to augment human capabilities dramatically. Language interfaces will democratize access to advanced tools. Reasoning systems will provide expert-level analysis. Agentic AI will coordinate complex operations. These capabilities are beginning to transform what individuals can accomplish.
But the true multiplication effect will come through structured peer learning networks. When thousands of practitioners share how to work effectively with AI through systematic project work, peer review, and facilitated dialogue, they create collective intelligence about human-AI collaboration that no individual could develop alone. They transform AI from an impressive but alien technology into a natural extension of human capability.
For funders, this means the highest-impact investments combine AI tools with structured peer learning infrastructure. For policymakers, it means creating conditions where collaborative learning flourishes alongside technological deployment. For practitioners, it means embracing both AI partnership and peer collaboration through structured processes as essential to professional practice.
The future of human progress may rest on our ability to find effective ways to build powerful collaboration in networks that combine human and artificial intelligence. When we learn together through structured peer learning how to work with AI, we multiply not just individual capability but collective capacity to address the complex challenges facing our world.
AI is still emergent, changing constantly and rapidly. The peer learning methods are proven: we know a lot about how humans learn and collaborate. The question is how quickly we can scale this collaborative approach to match the pace of AI advancement. In that race, structured peer learning is not optional—it is essential.
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. To understand why this matters, we need to step back and see how radically this changes everything.
The old way: A tower of Babel
Today, interacting with technology requires learning its language, not the other way around. Consider what you need to know:
To use your smartphone, you must understand apps, settings, swipes, and taps
To search the internet effectively, you need the right keywords and search operators
To work with a spreadsheet, you must learn formulas, functions, and formatting
To program a computer, you need years of training in coding languages
To operate specialized software—from medical systems to industrial controls—requires extensive training
Each system speaks its own language. Humans must constantly translate their intentions into forms machines can understand. This creates barriers everywhere: between people and technology, between different systems, and between those who have technical skills and those who do not.
The new way: Natural language as universal interface
What changes when AI systems can understand and act on natural human language? Everything.
Instead of learning how to use technology, you simply tell it what you want:
“Find all our customers who haven’t ordered in six months and draft a personalized re-engagement email for each”
“Look at this medical scan and highlight anything unusual compared to healthy tissue”
“Monitor our factory equipment and alert me if any patterns suggest maintenance is needed soon”
“Take this contract and identify any terms that differ from our standard agreement”
The AI system translates your natural language into whatever technical operations are needed—database queries, image analysis, pattern recognition, document comparison—without you needing to know how any of it works.
Why a universal interface changes everything
1. Democratization of capability
When language becomes the interface, advanced capabilities become available to everyone who can explain what they want. A small business owner can perform complex data analysis without hiring analysts. A teacher can create customized learning materials without programming skills. A farmer can optimize irrigation without understanding algorithms.
The divide between technical and non-technical people begins to disappear. What matters is not knowing how to code but knowing what outcomes you want to achieve.
2. System integration without friction
Today, making different systems work together is a nightmare of APIs, data formats, and compatibility issues. But when every system can be controlled through natural language, integration becomes as simple as explaining the connection you want:
“When a customer complains on social media, create a support ticket, alert the appropriate team based on the issue type, and draft a public response acknowledging their concern”
The AI handles all the technical complexity of connecting social media monitoring, ticketing systems, team communications, and response generation.
3. Context that travels
Unlike traditional interfaces that reset with each interaction, language-based AI systems can maintain context across time and tasks. They remember previous conversations, understand ongoing projects, and track evolving situations.
Imagine telling an AI: “Remember that analysis we did last month on customer churn? Update it with this quarter’s data and highlight what’s changed.” The system knows exactly what you’re referring to and can build on previous work.
4. Coordination at scale
When AI agents can communicate through natural language, they can coordinate complex operations without human intervention. Schmidt’s example of building a house illustrates this—multiple AI agents handling different aspects of a project, all coordinating through language:
The land-finding agent tells the regulation agent about the plot it found
The regulation agent informs the design agent about building restrictions
The design agent coordinates with the contractor agent on feasibility
Each agent can explain its actions and reasoning in plain language
Real-world implications
For business
Companies can automate complex workflows by describing them in natural language rather than programming them. A marketing manager could say: “Monitor our competitor’s pricing daily, alert me to any changes over 5%, and prepare a report on their promotional patterns.” No need for programmers, database experts, or data analysts.
For healthcare
Doctors can interact with AI diagnostic tools using medical terminology they already know, rather than learning proprietary interfaces. “Compare this patient’s symptoms with similar cases in our database and suggest additional tests based on what we might be missing.”
For education
Teachers can create personalized learning experiences by describing what they want: “Create practice problems for my students who are struggling with fractions, make them progressively harder as they improve, and let me know who needs extra help.”
For government
Policy makers can analyze complex data and model scenarios using plain language: “Show me how proposed changes to tax policy would affect families earning under $50,000 in rural areas versus urban areas.”
Five challenges ahead
This transformation is not without risks and challenges:
Accuracy: Natural language is ambiguous. Ensuring AI systems correctly interpret intentions requires sophisticated understanding of context and nuance.
Security: If anyone can control systems through language, protecting against malicious use becomes critical.
Verification: When complex operations happen through simple commands, how do we verify the AI did what we intended?
Dependency: As we rely more on AI to translate our intentions into actions, what happens to human technical skills?
The bottom line
Language as a universal interface represents a fundamental shift in how humans relate to technology. Instead of humans learning to speak machine languages, machines are learning to understand human intentions expressed naturally.
This is not just about making technology easier to use. It is about removing the barriers between human intention and digital capability. When that barrier falls, we enter Eric Schmidt’s “new epoch”—where the distance between thinking something and achieving it collapses to nearly zero.
The implications ripple through every industry, every job, every aspect of daily life. Those who understand this shift and adapt quickly will find themselves with almost magical capabilities. Those who do not may find themselves bypassed by others who can achieve in minutes what once took months.
The universal interface is coming. The question is not whether to prepare, but how quickly you can begin imagining what becomes possible when the only limit is your ability to describe what you want.
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. This deeply human process of systematic reasoning is what artificial intelligence systems are now learning to do.
This capability represents a fundamental shift from AI that recognizes patterns to AI that can work through complex problems the way a skilled professional would. For those working in global health and education, understanding this transformation is essential.
The difference between answering and reasoning
To understand this revolution, consider how most AI works today versus how reasoning AI operates.
Traditional AI excels at pattern recognition. Show it a chest X-ray, and it can identify pneumonia by matching patterns it learned from millions of examples. Ask it about disease symptoms, and it retrieves information from its training data. This is sophisticated, but it is fundamentally different from reasoning.
Consider this scenario: An unusual cluster of respiratory illness appears in a rural community. The symptoms partially match several known diseases but perfectly match none. Environmental factors are unclear. Some patients respond to standard treatments. Others do not.
A pattern-matching AI might list possible diseases based on symptom similarity. But a reasoning AI would approach it like an epidemiologist:
“Let me examine the symptom progression timeline.”
“The geographic clustering suggests environmental or infectious cause. Let me investigate both paths.”
“Wait, these treatment responses do not align with any single pathogen. Could this be co-infection?”
“I need to reconsider. What if the environmental factor is not the cause but is affecting treatment efficacy?”
The AI actually works through the problem, forms hypotheses, recognizes when evidence contradicts its assumptions, and adjusts its approach accordingly.
How reasoning AI thinks through problems
Advanced AI systems now demonstrate visible thinking processes. When analyzing complex health data, they might:
“First, let me identify the key variables affecting disease transmission in this population.”
“I will start by calculating the basic reproduction number using standard methods.”
“These results seem inconsistent with the observed spread pattern. Let me check my assumptions.”
“I may have overlooked the role of asymptomatic carriers. Let me recalculate.”
“This aligns better with observations. Now I can project intervention outcomes.”
This is not scripted behavior. The AI works through problems, recognizes errors, and corrects its approach—much like a researcher reviewing their analysis.
Why reasoning requires massive computational power
Reasoning AI systems require thousands of times more computational resources than traditional AI. Understanding why helps explain both their power and limitations.
Think about the difference between recognizing a disease from symptoms versus investigating a novel outbreak. Recognition happens quickly: an experienced clinician identifies malaria almost instantly. But investigating an unusual disease cluster requires sustained analysis, exploring multiple hypotheses, checking each against evidence.
The same applies to AI. Traditional pattern-matching AI makes a single pass through its neural network. But reasoning AI must:
Explore multiple hypotheses simultaneously;
Check each reasoning step for logical consistency;
Backtrack when evidence contradicts assumptions;
Verify conclusions against all available data; and
Consider alternative explanations.
Each step requires intensive computation. The AI might explore hundreds of reasoning paths before reaching sound conclusions.
Matching expert performance
AI systems in mid-2025 perform at the level of graduate students in mathematics and other fields. For global health, this means AI that can:
Design epidemiological studies with appropriate controls;
Identify confounding variables in complex datasets;
Recognize when standard statistical methods do not apply; and
Develop novel approaches to emerging health challenges.
This is not about calculating faster—computers have done that for decades. It is about understanding concepts, recognizing which analytical techniques to apply, and working through novel problems.
Applications in global health
Reasoning AI transforms multiple aspects of global health work:
Outbreak investigation: AI that can integrate diverse data sources—clinical reports, environmental data, travel patterns, genetic sequences—to identify outbreak sources and transmission patterns.
Treatment optimization: Systems that reason through drug interactions, comorbidities, and local factors to recommend personalized treatment protocols.
Resource allocation: AI that understands trade-offs between prevention and treatment, immediate needs and long-term capacity building, to optimize limited resources.
Research design: Systems that can identify weaknesses in study designs, suggest improvements, and recognize when findings may not generalize to other populations.
Policy analysis: AI that reasons through complex interventions, anticipating unintended consequences and identifying implementation barriers.
What makes AI reasoning different
Five capabilities distinguish reasoning AI from pattern-matching systems:
Working memory: Reasoning AI holds multiple pieces of information active while working through problems, like a human tracking several hypotheses simultaneously.
Logical consistency: Each conclusion must follow logically from evidence and prior reasoning steps.
Error recognition: When results do not make sense, the system recognizes the problem and adjusts its approach.
Abstraction: The AI recognizes general principles and applies them to specific situations, not just memorizing solutions.
Explanation: Reasoning AI can explain its logic, making its conclusions verifiable and trustworthy.
The path forward
The reasoning revolution does not replace human expertise but augments it in powerful ways. For global health professionals, this means:
AI partners that can work through complex epidemiological puzzles;
Systems that help design culturally appropriate interventions;
Tools that identify patterns humans might miss while respecting local knowledge.
Understanding reasoning AI is no longer optional for those shaping global health. These systems are becoming intellectual partners capable of working through complex problems alongside human experts. The question is not whether to engage with this technology but how to use it effectively while maintaining human agency, judgment, and values in decisions that affect human lives.
The ability to reason—to work systematically through complex problems—has always been central to advancing human health and knowledge. Now that machines are learning this capability, we must thoughtfully consider how to harness it for global benefit while ensuring human wisdom guides its application.
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. This is the “agentic AI revolution” —a transformation where AI systems become agents that can understand objectives, remember context, plan actions, and work together to complete complex tasks. It represents a shift from AI as a tool you use to AI as a workforce that you collaborate with.
Understanding AI agents: More than chatbots
When most people think of AI today, they think of ChatGPT or similar systems—you ask a question, you get an answer. That interaction ends, and the next time you return, you start fresh. These are powerful tools, but they are fundamentally reactive and limited to single exchanges.
AI agents are different. They work on a principle of “language in, memory in, language out.” Let’s break down what this means:
Language in: You describe what you want in natural language, not computer code. “Find me a house in California that meets these criteria…”
Memory in: The agent remembers everything relevant—your preferences, previous searches, budget constraints, past interactions. It maintains this memory across days, weeks, or months.
Language out: The agent reports back in plain language, explains what it did, and asks for clarification when needed. “I found three properties matching your criteria. Here’s why each might work…”
But here is the crucial part: between receiving your request and reporting back, the agent can take actions in the world. It can search databases, fill out forms, make appointments, send emails, analyze documents, and coordinate with other agents.
The house that agentic AI built
The example of building a house perfectly illustrates how agents transform complex projects. In the traditional approach, you would:
Spend weeks searching real estate listings yourself.
Hire a lawyer to research zoning laws and regulations.
Work with an architect to design the building.
Interview and select contractors.
Manage the construction process.
Deal with disputes if things go wrong.
Each step requires your active involvement, coordination between different professionals, and enormous amounts of time.
In the agentic model, you simply state your goal: “I want to build a house in California with these specifications and this budget.” Then:
Agent 1 searches for suitable lots, analyzing thousands of options against your criteria.
Agent 2 researches all applicable regulations, permits, and restrictions for each potential lot.
Agent 3 creates design options that maximize your preferences while meeting all regulations.
Agent 4 identifies and vets contractors, checking licenses, reviews, and past performance.
Agent 5 monitors construction progress and prepares documentation if issues arise.
These agents do not work in isolation. They communicate constantly:
The lot-finding agent tells the regulation agent which properties to research.
The regulation agent informs the design agent about height restrictions and setback requirements.
The design agent coordinates with the contractor agent about feasibility and costs.
All agents update you on progress and escalate decisions that need human judgment.
Why agentic AI changes everything
This workflow example is true of every business, every government, and every group human activity. In other words, this transformation has universal relevance.
Every complex human endeavor involves similar patterns:
Multiple steps that must happen in sequence;
Different types of expertise needed at each step;
Coordination between various parties;
Information that must flow between stages; and
Decisions based on accumulated knowledge.
Today, humans do all this coordination work. We are the project managers, the communicators, the information carriers, the decision makers at every level. The agentic revolution means AI agents can handle much of this coordination, freeing humans to focus on setting goals and making key judgments.
The memory advantage
What makes agents truly powerful is their memory. Unlike human workers who might forget details or need to be briefed repeatedly, agents maintain perfect recall of:
Every interaction and decision;
All relevant documents and data;
The complete history of a project; and
Relationships between different pieces of information.
This memory persists across time and can be shared between agents. When you return to a project months later, the agents remember exactly where things stood and can continue seamlessly.
Agentic AI from individual tools to digital teams
The revolutionary aspect is not just individual agents but how they work together. Like a well-functioning human team, AI agents can:
Divide complex tasks based on specialization;
Share information and coordinate actions;
Escalate issues that need human decision-making;
Learn from outcomes to improve future performance; and
Scale up or down based on workload.
But unlike human teams, they can:
Work 24/7 without breaks;
Handle thousands of tasks in parallel;
Communicate instantly without misunderstandings;
Maintain perfect consistency; and
Never forget critical details.
The new human role as co-worker to agentic AI
In this world, humans do not become obsolete—our role fundamentally changes. Instead of doing routine coordination and information processing, we:
Set goals and priorities;
Make value judgments;
Handle exceptions requiring creativity or empathy;
Build relationships and trust;
Ensure ethical considerations are met; and
Provide the vision and purpose that guides agent actions.
Challenges and considerations
The agentic revolution raises important questions:
Trust: How do we verify agents are acting in our interest?
Control: What happens when agents make decisions we did not anticipate?
Accountability: Who is responsible when an agent makes an error?
Privacy: What data do agents need access to, and how is it protected?
Employment: What happens to jobs based on coordination and information processing?
What can agentic AI do in 2025?
Early versions of these agents already exist in limited forms. Organizations and individuals who understand this shift early will have significant advantages. Those who continue operating as if human coordination is the only option may find themselves struggling to compete with those augmented by agentic AI teams.
Where do we go from here?
The agentic revolution represents something humanity has never had before: the ability to multiply our capacity for complex action without proportionally increasing human effort. It is as if every person could have their own team of tireless, brilliant assistants who understand their goals and work together seamlessly to achieve them.
This is not about replacing human intelligence but augmenting human capability. When we can delegate routine coordination and information processing to agents, we can focus on what humans do best: creating meaning, building relationships, making ethical judgments, and pursuing purposes that matter to us.
The world we imagine—where building a house or running a business or navigating healthcare becomes as simple as stating your goal clearly—represents a fundamental shift in how complex tasks get accomplished. Whatever the timeline for this transformation, understanding how AI agents work and what they make possible has become essential for anyone trying to make sense of where our societies are heading.
The concept is clear: AI systems that can understand goals, remember context, and coordinate actions to achieve complex outcomes. What we do with this capability remains an open question—one that will be answered not by the technology itself, but by how we choose to use it.
