The AI Economy Institute (AIEI) is launching its third cohort of researchers, advancing our mission to understand the adoption of artificial intelligence across economies, industries, and communities. 

We launched the AI Economy Institute because AI’s economic impact is not predetermined. Though AI is being rapidly adopted, the evidence base for understanding its impact on work, jobs, education, productivity, and opportunity is still too thin. By increasing the scholarship around the AI economy and producing it in a timely and accessible way, we can help ensure that as AI transforms our world, we’re equipping people with the knowledge and tools they need to make decisions and succeed with AI.

Our 2026 AI Economy Institute Cohort

The AI Economy Institute convenes outside experts and researchers to share their perspectives and advance the body of knowledge on topics related to AI, work, and education. Our third global research call centered on understanding how frontier firms are reshaping work and the broader economic landscape.  

Representing a diverse group of institutions worldwide, our cohort brings together subject matter experts and researchers to explore how AI is reshaping the workforce, organizations, and the broader economy. The cohort consists of the following individuals, representing the following institutions:    

• Brian Jabarian, Carnegie Mellon University 
• Caspar David Peter, Erasmus University, Rotterdam, Netherlands 
• Christoph Siemroth, University of Essex, England 
• Daniel Yue, Georgia Institute of Technology 
• Edoardo Maria Acabbi, University of Mannheim, Germany 
• Frank Nagle, Massachusetts Institute of Technology (Advising Fellow and Cohort 2) 
• Friederike Mengel, University of Essex, England; Erasmus University Rotterdam, Germany 
• Gianmarco Ottaviano, Bocconi University, Italy 
• Ilan Strauss, AI Disclosures Project 
• Johannes Wachs, Corvinus University, Budapest, Hungary 
• Luca Henkel, Erasmus University, Rotterdam, Netherlands 
• Luca Mazzone, University of Montreal, Canada 
• Laura Nurski, Centre for European Policy Studies (CEPS), Belgium (Cohort 2) 
• Meeyoung (Mia) Cha, Korea Advanced Institute of Science and Technology (KAIST), South Korea 
• Mustafa Afacan, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), United Arab Emirates; Sabancı University, Turkey (World Bank Affiliated Senior Fellow) 
• Nataliya Wright, Columbia University 
• Nuriye Melisa Bilgin, Koç University, Turkey 
• Pëllumb Reshidi, Florida State University 
• Pierre-Alexandre Balland, Centre for European Policy Studies (CEPS), Belgium (Advising Fellow and Cohort 2) 
• Salman Khan, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), United Arab Emirates (World Bank Affiliated Senior Fellow) 
• Serena Booth, Brown University 
• Wesley Rosslyn-Smith, University of Pretoria, South Africa (Advising Fellow) 
• Yingfei Wang, Foster School of Business, University of Washington 

Cohort members will analyze frontier firms to examine both upstream, firm-level transformations and downstream, economy-wide impacts. Researchers will also explore how AI changes job design, skill demands, productivity, and regional economic development.  

AIEI’s first two cohorts explored how AI is reshaping the talent pipeline, from higher education and skills to K-12, community colleges, and early-career pathways, so that we could understand and inform the early changes to the labor market. What we learned from that point of inquiry shifted the focus; this year’s cohort moves further into the economy itself, focusing on frontier firms and how leading organizations are adopting AI, redesigning work, and creating the conditions for productivity, diffusion, and human agency at scale.

Interpreting the frontier: What this means for policy and strategy  

Since its launch, the AI Economy Institute has fielded more than 800 responses to our calls for research proposals. The gap between what AI systems can do and what organizations can actually deploy will shape the pace of adoption. Gains in productivity may come alongside organizational shifts as firms adapt their workflows, teams, and decision-making processes.

At the same time, the expansion of automation raises a parallel question of whether systems are enhancing human learning or displacing it. Underlying all of this is a broader uncertainty about the extent to which AI will diffuse widely across economies or concentrate in a narrow set of firms and regions. 

Cohort 3 moves beyond identifying these tensions and toward generating the empirical evidence needed to navigate them, providing policymakers, firms, and institutions with a clearer basis for decision-making in a rapidly evolving AI economy. 

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Today we published our latest Global AI Diffusion Report. The global adoption of artificial intelligence continued to rise in the first quarter of 2026. During the quarter, AI usage increased by 1.5 percentage points from 16.3% to 17.8% of the world’s working age population. Intensity of use among economies with the highest rates of AI diffusion also increased, with 26 economies now exceeding 30% of the working age population using AI.

At the top of Microsoft’s National AI Leaderboard, the UAE continued to lead global AI diffusion at 70.1%. The United States finally started to move up the national rankings, albeit only from 24th to 21st based on a 31.3% usage rate by the working age population.

Notable developments in the quarter included accelerating AI adoption in Asia driven in part by improving AI capabilities in Asian languages. South Korea, Thailand, and Japan saw the greatest movement. More broadly, the quarter brought continued widening of the AI gap between the Global North and South, with usage now at 27.5% in the North and 15.4% in the South. These trends are discussed below, including a deeper dive on the positive impact of enhanced multilingual AI capabilities in Japan.

To track all these trends, we continue to measure AI diffusion as the share of people worldwide between ages 15 and 64 who have used a generative AI product during the reported period. This measure is derived from aggregated and anonymized Microsoft telemetry and adjusted to reflect differences in OS and device-market share, internet penetration, and country population. Additional details on the methodology are available in our AI Diffusion technical paper.^[1]

No single metric is perfect, and this one is no exception. Through the Microsoft AI Economy Institute, we continue to refine how we measure AI diffusion globally, including how adoption varies across countries in ways that best advance priorities such as scientific discovery and productivity gains. For this report, we rely on the strongest cross-country measure available today, and we expect to complement it over time with additional indicators as they emerge and mature.

Sectorally, the quarter saw strengthened AI coding capabilities leading to a dramatic increase in production of software code. This was reflected in production by Anthropic’s Claude Code, the OpenAI’s Codex, and Microsoft’s GitHub Copilot. Git pushes – through which software developers put coding changes online – increased 78% year over year globally. Interestingly, the quarter brought added evidence that, at least for now, AI coding capabilities may be increasing demand for the employment of software developers.

As discussed in more detail in the report, when developer productivity increases, the cost of building software declines. If demand for software is elastic, organizations can respond by building more software across a wider range of use cases. It is still too early to know the full labor-market impact of AI-assisted coding, but the available data shows that in 2025, total U.S. software developer employment reached approximately 2.2 million, rising 8.5% year over year and marking a record high for the profession. Early data for the first quarter of 2026 shows that software developer employment in March 2026 was about 4% higher than in March 2025.

Download the latest Global AI Diffusion report. and explore the data here.

[1] A. Misra, J. Wang, S. McCullers, K. White, and J., L. Ferres, “Measuring AI Diffusion: A Population Normalized Metric for Tracking Global AI Usage,” Nov. 04, 2025, arXiv: arXiv:2511.02781. doi: 10.48550/arXiv.2511.02781. 

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Around the world, the dangers of extreme weather are a daily reality. In 2024, extreme weather displaced or disrupted the lives of more than 800,000 people worldwide —a reminder that accurate, timely forecasts aren’t just about data; they’re about people. From farmers deciding when to plant to coastal communities preparing for hurricanes, better forecasting can save lives, protect infrastructure, and support economies. 

That is why Microsoft remains deeply committed to Aurora, an AI model designed to help scientists understand Earth systems in new ways. Trained on vast amounts of data, it’s tuned to model the Earth’s systems. Aurora has already shown promise across multiple scenarios, including predicting the weather, tracking hurricanes and air quality, and modeling ocean waves and energy flows. 

Today, we are reaffirming our commitment: keep Aurora open, collaborative, and impactful so researchers can innovate faster and deliver solutions that help communities prepare, adapt, and thrive. Scientific progress depends on openness and a strong global community, which is why Aurora will progress as an open-source platform, enabling scientists everywhere to contribute and apply it to new climate and weather challenges. 

The next phase: Fueling innovation through research partnerships

We’re collaborating with Professor Rich Turner, a leader in machine learning research, and his lab at the University of Cambridge through a Microsoft AI for Good grant and research scientists to continue development of Aurora. Originally developed by Microsoft Research AI for Science, with collaboration from Professor Turner, we believe Aurora has the potential to change the way scientists around the world can use AI for weather and climate science. 

Building on our SPARROW initiative, we’re also investing in research of open-source weather stations that can expand access to high-quality environmental data. These affordable, community-deployable systems are designed to help fill critical observation gaps and strengthen the dependability of weather predictions where they matter most. 

Making Aurora available to scientists everywhere

Aurora’s source code and model weights are already open—but we’re going further. Together with Turner and Cambridge, our AI for Good team will open-source future releases of Aurora and new models that are built upon it, including training pipelines. By making Aurora open and free to build upon, we’re enabling researchers and developers everywhere to collaborate, contribute, and drive innovation together. 

Empowering national meteorological services

As with any technology, the measure of success for tools like Aurora is to have a positive impact on the lives of people. Empowering national meteorological services across the Global South, along with the Global North, is a priority.  We’re particularly focused on the application of Aurora to help meteorological services develop and strengthen their own forecasting systems that are tailored to their own local environments. This will allow them to adapt, extend, and innovate on top of Aurora, improving the accuracy, reliability, and reach of their forecasts. 

Enabling a cross-industry ecosystem

Aurora is trained on one of the largest collections of atmospheric data ever assembled to develop an AI forecasting model. It’s then fine-tuned to perform a variety of specific tasks, like predicting wave height or air quality, using modest amounts of additional data.  

The application of such a model could unlock innovation across all kinds of other industries. For example, energy companies and commodity traders have expressed interest, particularly in seeing how Aurora can be adapted to better predict renewable power generation, anticipate extreme weather events, and help protect energy grids. 

We are excited to see our work on Aurora graduate from a research project into a truly collaborative, open-source effort. By opening Aurora to the global community, we’re enabling breakthroughs in scientific understanding that we hope will transform humanitarian aid, optimize energy systems, advance sustainability, and even reshape financial services. 

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