The rapid diffusion of AI globally and the pace at which AI is developing put us at a societal crossroads. The choices we’re making now will determine who has access to AI and the opportunity it affords. There is perhaps no more important tool than our education systems for addressing the uneven diffusion of AI we’re seeing currently: national adoption runs above 70% in countries such as the United Arab Emirates and falls to low single digits across much of sub-Saharan Africa, close to a tenfold difference. 

The way we teach AI and learn with it will have long-lasting impacts on jobs, the workforce, and our broader economy. Learning Curves: Rethinking Education from Classrooms to Careers in an AI-Driven World is a collection of research-driven essays that can help guide those choices. This book brings together the body of work from the second cohort of Senior Fellows from the Microsoft AI Economy Institute (AIEI), a flagship think tank that produces research and convenes experts to generate insights into how AI is reshaping work, education, and the broader economy. 

In Learning Curves, researchers consider how AI is reshaping the earliest stages of learning and work, from K–12 classrooms and community and technical colleges to college‑to‑work transitions and entry‑level jobs, and what it will take for that transformation to widen opportunity rather than deepen divides across institutions, communities, and countries.  

Below, I share a few of my own reflections that informed my contributions to Learning Curves, a collaborative effort that wouldn’t have been possible without the truly talented, diverse cohort of researchers who contributed to this book.  

The good, the bad, and the ugly: Laying the foundation for AI in K–12 classrooms

Before delving into the specifics of how to incorporate AI in classrooms, we need to ask ourselves whether we’re teaching for knowledge, or for learning.  

The book, the calculator, the personal computer, the internet, the search engine, and the smartphone all arrived in classrooms carrying real promise alongside the worry that they would hollow out young minds. Usually the truth landed somewhere in between, and where it landed depended less on the tool than on the choices the adults made about how to use it. 

The fact is that the AI-native generation is already here, and its members are in classrooms around the world. More than half of US teenagers already use AI chatbots for schoolwork. I’ve personally seen the promise of AI as a force for good in education up close in my own classroom, where I teach a computer science class to elementary school students.  

But of course, the good is only part of the story. We can’t shy away from the downsides of incorporating AI into classrooms. While AI can help with accessibility and individualized learning, it can also create over–reliance, contribute to student cheating, or prevent students from recognizing when information may be false. 

The rapid deployment of AI in K–12 classrooms needs to come with a sober assessment of how that technology impacts all students. This assessment must go beyond the technology itself to encompass how students, teachers, parents, and society use AI.   

AI literacy is only part of the solution

If classrooms are where students learn how to think, teachers are the conduit through which this learning happens. In Learning Curves, Joseph Orero, Senior Researcher in AI at Strathmore University’s School of Computing and Engineering Science, posits that teachers aren’t resistant to AI in classrooms; they just need proper training to help them adequately adapt and teach their students critical skills. His research contribution explores game-based learning with AI-enabled African languages. 

AI usage and adoption worldwide are also dependent on accessibility. Another chapter of the book looks at barriers and opportunities in Arabic education and finds that the primary barrier teachers encountered when introducing AI writing assistants into classrooms was Arabic digital literacy, particularly students’ typing ability.  

Researchers often circled around a similar theme: there is no one-size-fits-all approach to developing AI competency; instead, such an approach must be sensitive to local knowledge and contextualized teaching and learning practices. This means that teachers, in turn, must have training that makes sense for the unique contexts in which their students are learning.  

Frank Nagle, now Microsoft’s Chief AI Economist, Sarah Rodriguez, Associate Professor of Engineering Education at Virginia Tech, and other researchers in Learning Curves make the case that, in many ways, nontraditional pathways such as apprenticeships and two-year-degree programs, particularly those that are virtual, are more nimble and thus better able to adapt their curricula to the rapidly changing field of AI.  

AI Is taught in the classroom, but then what?

Education has never been about simply preparing people for the future of work so they can thrive in the labor market. When considered holistically, it is about teaching students not what to think but how to think. In our AI era, education is one of the core institutions through which the future of work will be built, distributed, and made more (or less) equitable. Successful AI integration remains human-centered by focusing on organizational change, durable human skills like empathy and judgment, strong employer partnerships, and learner needs. 

As research from Laura Nuriski, Head of Program on the Future of Work at the Centre for European Policy Studies (CEPS), highlights, access to AI alone does not ensure successful integration. Technology transfer, data infrastructure, organizational readiness, and a human-centered approach to building and deploying AI are also key indicators of success.  

Whether we’re talking about the classroom or the boardroom, it’s worth keeping in mind that AI tends to do the most good when it strengthens human capability and the most harm when it substitutes for it. That holds as true in a fourth- grade reading lesson as in a software team or a hospital ward. 

The children in our schools deserve tools that serve their learning rather than stand in for it, and that is within our power to provide. Our most successful future with AI will be one that is human-centered, inclusive, and designed to help people thrive. 

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Today we published our latest Global AI Diffusion Report. Global AI diffusion continued to climb in the second quarter of 2026. In June, AI usage was 18.8% of the working-age population worldwide, an increase of about 1% from the first quarter of this year. Growth was broad-based: almost every economy saw increased usage.

Notably, the UAE (73.3%) and Singapore (64.3%) continue to top the leaderboard. South Korea again saw the largest absolute change, with an increase of 3.5 percentage points. Saudi Arabia climbed five places from 30th to 25th, the largest rise in the rankings. Japan recorded the largest relative change, growing by 2.2 percentage points, or roughly 10% relative to its Q1 usage of 22.5%. Worldwide, the quarter brought a continued widening of the AI gap between the Global North and South, with usage now at 28.8% in the North and 16.2% in the South.

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

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. Over the past year, there has been a significant rise in new AI tools and models. For our next report, we will expand our measure to include usage of these new tools and expect this update to increase AI user share across almost all economies, with larger projected increases in China.

In addition to our comparison of AI diffusion between the Global North and the Global South, we also find important differences in how AI is being used. Using consumer Microsoft Copilot usage insights, we examined these differences. The most notable difference is a higher share of education and learning use in the Global South.

This report also includes a substantive discussion of open-weight AI models. Open-weight AI model use is increasingly visible through application programming interface (API) usage of AI, where open models have captured a growing share of token volume. This shift could especially benefit the Global South by lowering access costs and enabling adaptation to local languages and needs, although barriers such as infrastructure, connectivity, and skills remain.

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

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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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