Agriculture Program Lead, The AI Access Initiative

About The AI Access Initiative We're at an inflection point in artificial intelligence – presenting both tremendous potential opportunity and risk for people in developing countries: rather than allowing the poverty gap to expand, low-income countries have an opportunity to leapfrog development milestones with AI-powered innovations. Incubated at Evidence Action , we’re launching The AI Access Initiative, an organization focused on scaling AI-enabled ‘big bets’ to benefit tens or hundreds of millions of people in poverty in low- and middle-income countries (LMICs). We will operate at the intersection of global development actors, top AI labs, and leading researchers to drive meaningful access to the benefits of AI for the 3.5 billion people living in poverty globally. We will create “public good,” open-sourced playbooks, toolkits, and insights that define how to design and launch tractable and impactful AI-enabled programs. Given the scale of opportunity, we expect our portfolio to expand substantially, but to start, we’re scaling two programs focused on AI in Agriculture and AI in Health. We are led by former Evidence Action CEO Kanika Bahl, a founding member of Anthropic’s Long-Term Benefit Trust, and advised by Nobel Laureate Michael Kremer; Dario Amodei, CEO of Anthropic; and Kent Walker, President, Global Affairs for Alphabet and Google. This work builds on Evidence Action ’s track record reaching 530M+ people with cost-effective, evidence-based programs across 9 countries in Africa and Asia, with a focus on last-mile delivery. Our Approach We’re scaling AI-enabled big bets to benefit tens or hundreds of millions of individuals living in poverty. We will focus both on delivering near-term, tractable direct impact and supporting governments to establish an enabling environment for the ‘AI for Good’ ecosystem, including: Scaling “big bet” AI interventions to impact tens or hundreds of millions, such as delivering AI-enabled forecasts to smallholder farmers to imp