UC Berkeley School of Information

Joshua Blumenstock has spent much of his career focused on easing the pain of poverty, project by project, in countries such as Togo, Afghanistan and Bangladesh. But with the advance of AI-driven machine learning, the Berkeley economist has set his sights on a more holistic goal: calculating what it would cost to eradicate extreme poverty worldwide.

The answer, according to a new working paper: $318 billion per year, or 0.3% of global gross domestic product, would be enough to bring hundreds of millions of people out of extreme poverty. For an American earning the median annual income of $45,000, the cost of ending extreme poverty this way works out to about $135 per year. 

At the beginning of 2025, UC Berkeley’s School of Information and Goldman School of Public Policy announced the launch of the Executive Fellowship in Applied Technology Policy, a pioneering program designed to empower senior leaders at the intersection of technology and public policy. The eight-month non-residential fellowship sought to provide a dynamic platform for accomplished public sector technology leaders and distinguished academic thought leaders to reflect on their experiences, mentor the next generation, and document their contributions to digital transformation in government.