The Applied Research Training in Evaluation, Methods, and Impact Studies (ARTEMIS) program, launched by J-PAL North America, equips early-career researchers with high-level technical training and practical skills to design randomized evaluations (also referred to as a randomized controlled trials, or RCTs) on issues of poverty in the U.S. context. Fully funded and highly rigorous, the program's inaugural cohort of 20 participants ' including PhD students through pre-tenure faculty ' is receiving training, mentorship, and proposal development support through December. ARTEMIS is the newest offering within J-PAL North America's Economics Pathways program, which broadens and strengthens the pipeline of economics researchers by providing support to aspiring scholars at every stage ' from undergraduate interns to research staff and, now, academic researchers preparing to launch their first randomized evaluations. ARTEMIS programming commenced this past June with the Summer Institute ' an intensive weeklong course at MIT Endicott House in Dedham, Massachusetts, that focused on delivering technical content and building participants' practical skills and confidence to design and pursue funding for rigorous evaluations to address poverty in the United States. During the Summer Institute, participants had the opportunity to learn from researchers in the J-PAL network, including Gerald Daniels, associate professor of economics at Howard University, who served as a lecturer for the program. 'ARTEMIS is an amazing program that bridges the gap for researchers who haven't previously had the opportunity to work with randomized evaluations,' notes Daniels. 'It takes rigorous methodologies, like randomized evaluations, and makes them approachable, helping scholars translate their research ideas into high-impact evaluation designs."...

Five MIT faculty members and two additional alumni were recently named to the 2026 cohort of AI2050 Fellows. The honor is announced annually by Schmidt Sciences, Eric and Wendy Schmidt's philanthropic initiative that aims to accelerate scientific innovation. Conceived and co-chaired by Eric Schmidt and James Manyika, AI2050 is a philanthropic initiative aimed at helping to solve hard problems in artificial intelligence. Within their research, each fellow will contend with the central motivating question of AI2050: 'It's 2050. AI has turned out to be hugely beneficial to society. What happened' What are the most important problems we solved and the opportunities and possibilities we realized to ensure this outcome'' Sendhil Mullainathan is the Peter de Florez Professor at MIT with dual appointments in the departments of Economics and Electrical Engineering and Computer Science who is also a principal investigator at the MIT Laboratory for Information and Decision Systems. His current research is at the intersection of algorithms and people, with a central focus on launching The Bike Shop@MIT, a new research center that works toward more human-centered AI through scalable bicycles for the mind, and innovative new gears for machine learning algorithms. Mullainathan is the co-founder of ideas42, a nonprofit to apply behavioral science; MIT's Abdul Latif Jameel Poverty Action Lab, which promotes the use of randomized control trials in development; and Dandelion Health, which combines proprietary AI tools with real-world patient data. He's the co-author of "Scarcity: Why Having Too Little Means So Much."Among other awards, he is a recipient of the MacArthur 'Genius Grant'; has been designated a 'Young Global Leader' by the World Economic Forum; was labeled a 'Top 100 Thinker' by Foreign Policy Magazine; and was named to the 'Smart List: 50 people who will change the world' by Wired Magazine (U.K.). He serves on the board of the MacArthur Foundation, is affiliated with the NBER and BREAD, and is a member of the American Academy of Arts and Sciences. His AI2050 project focuses on the translation of patterns detectable by AI into testable theories.AI2050 Early Career FellowsAlex Damian is an assistant professor in the Department of Mathematics, holding an MIT Schwarzman College of Computing shared position with the Department of Electrical Engineering and Computer Science, who is also a principal investigator at the MIT Laboratory for Information and Decision Systems. His research focuses on the mathematical foundations of deep learning, with an emphasis on optimization dynamics and representation learning. He was previously a research fellow at the Kempner Institute at Harvard University. He received his PhD in applied and computational mathematics from Princeton University and his BS in mathematics from Duke University. His AI2050 project focuses on effective AI model training....

Over the past two weeks, Alphabet, Amazon, Meta, and Microsoft have outlined plans to spend more than $740 billion on capital expenditures this year, which Wall Street is reading as AI infrastructure spending, including data centers and chips. Earlier in the AI boom, investors largely rewarded such big outlays, viewing them as a sign of confidence in the technology's growth potential. But as we enter the fourth quarter, that enthusiasm seems to be giving way to concerns about negative free cash flow (spending more cash than a company brings in), mounting debt, and whether these enormous investments will ever generate enough returns. The anxiety spilled across the market on Friday. The Nasdaq fell 2.9%, and chip stocks took a drubbing, too: Nvidia dropped 4%, Broadcom more than 5%, and Qualcomm 11%. Alphabet, which plummeted 12% on Thursday, its worst day on record, wiping out $500 billion in market value, fell again. A big part of the concern is that companies that used to have overflowing free cash flow from their core businesses are getting hit as AI threatens the economics of those businesses. Alphabet provided the clearest example of that this week. Google's parent company reported negative free cash flow of $9.1 billion in the third quarter, its second consecutive quarter in the red, even as quarterly capex grew to $51.4 billion. Alphabet expects to spend up to $205 billion this year, and expects that spending to keep rising in 2027....

The hub ' a public repository of evergreen multimedia content developed with a grant from Google.org ' aims to equip decision-makers with foundational tools on how to leverage evidence to reduce homelessness and improve housing stability. Homelessness represents both a cause and a consequence of poverty and inequality. Rigorous evidence is critical in identifying effective solutions to eliminating homelessness, but rising constraints on time, funds, and other resources can create difficult barriers to developing and accessing high-quality evidence. J-PAL North America aims to increase access to evidence-informed paths to housing stability by providing essential tools for researchers, policymakers, and practitioners to evaluate the impact of their programs, expand the body of literature on what works to reduce homelessness, and leverage existing evidence to inform programs and decision-making. David Phillips, director of research at the Wilson Sheehan Lab for Economic Opportunities (LEO) and research professor of economics at the University of Notre Dame, served as the scientific advisor on the hub....