Why the skill-inflation panic is aimed at the wrong thing, and what it costs to make agent knowledge a build artifact instead of a file.
The post From Static to Dynamic Skills: A Different Model for Agent Knowledge appeared first on Towards Data Science.
A study from researchers at Princeton University and UC San Diego finds that so-called skills make AI agents better mainly through structured workflows, not through added knowledge. But as the skill library grows, agents have a harder and harder time finding the right set of instructions.
The article Study explains why AI agents benefit from "skills" and when they fail appeared first on The Decoder.
Tracking the uptake of artificial intelligence (AI) poses a fundamental measurement challenge. Recent data, such as Eurostat’s survey, are useful but often limited in geographic and chronological coverage and can be difficult to adjust as technologies change. Given that AI usage is evolving rapidly, frequently updated data sources can help us understand the dynamics and address data gaps.
To measure AI usage in real time, the OECD.AI team has developed an approach that leverages web traffic data. Platforms such as Similarweb provide estimates of website and app visitors, offering a window into how users interact with digital services at scale. This web traffic data enables researchers to track usage trends for AI chatbot interfaces and services.
While these data should be interpreted with appropriate methodological caveats, they offer unique insights into real-time dynamics of how the public is using AI.
To build a measure of monthly usage of GenAI chatbots, we focus on direct user engagement through AI web interfaces. It uses the number of unique, deduplicated visitors over a two-year period between 1 February 2024 and 30 March 2026 to the three main AI chatbot websites, ChatGPT, Claude and Gemini. According to the data, these three sites account for essentially all web traffic to chatbot interfaces in GPAI countries, as users of other sites will almost always also use one of the three leading sites and would therefore be removed during the deduplication process anyway.
With the number of unique visitors across the three chatbots, divided by population from IMF projections, the new measure of GenAI chatbot usage is computed.
There are a few methodological limitations to this approach as a proxy for broader AI adoption. This metric focuses on consumers, capturing only AI chatbot usage, and therefore excludes embedded or API-based uses of AI, which are increasingly central to enterprise applications and productivity-enhancing workflows. Additionally, web traffic data may be difficult to estimate for smaller jurisdictions and may introduce some bias into the country-level data. The estimates rely entirely on SimilarWeb data collection, which is subject to its own panel and estimation biases.
Across GPAI countries, this new measure of GenAI chatbot usage has surged over the past year, from roughly 18% of the population, on average, in January 2025 to 28% in January 2026. The countries seeing the highest growth in this measure in 2025 were Japan and Türkiye, where usage rates more than doubled. Singapore is consistently the top user per capita, and also experienced the largest increase in absolute terms, rising from 36% to 63% of the population using AI chatbots in 2025. You can explore the data for yourself in (Figure 1).
https://chart.oecd.ai/29736
Notes: Costa Rica, Estonia, Iceland, Latvia, Lithuania, Luxembourg, Malta, Senegal, Slovenia missing due to insufficient data. GenAI usage rates use deduplicated unique visitors from ChatGPT, Claude, and Gemini. Sources: OECD.AI calculations using data from SimilarWeb and the IMF.
The intensity of use among GenAI chatbot users has also grown. Figure 2 shows that in mid-2024, the average visit length to AI chatbots across GPAI countries was about 4.5 minutes. This grew to over 5.5 minutes in early 2026, signalling more intensive consumer use.
Younger audiences use AI chatbots the most. A deeper look at the data provides a better understanding of the demographics of AI users. First, GenAI chatbot usage remains much higher among younger consumers. According to the data, over half of people aged 25-34 in February 2026 used GenAI chatbots across GPAI countries, while only about 8% of people aged 65 and over were chatbot users (Figure 3).
Usage among older consumers accounts for much of the recent growth. While individuals under 35 still account for more than half of all GenAI chatbot users in 2026, their share has gradually declined. Between February 2024 and February 2026, the share of visitors aged 35 and over increased from 38% to 48%. This suggests that much of the recent growth in AI chatbot adoption has been driven by older age groups, indicating that usage is becoming more mainstream and widespread across the broader population (Figure 4).
Recreational AI use and younger populations. Looking beyond the three most popular AI chatbots, the data show that usage varies widely across age groups. Figure 5 shows that people under the age of 25 are much more likely to use Character.AI (a free chatbot that lets you create digital characters and interact with them via text, voice messages and calls) than any other chatbot. This indicates more recreational use among that age group. Older age groups, by contrast, are much more likely to use Microsoft Copilot, perhaps due to built-in referrals and employer-funded subscriptions.
Browsing patterns also indicate differences in how AI chatbots are used. Users of ChatGPT and Gemini are more likely to also visit recreational-oriented platforms such as YouTube, Instagram, and Facebook. In contrast, users of Claude and Microsoft Copilot more frequently visit sites such as LinkedIn, cloud platforms, Notion, and GitHub. This suggests that Claude and Copilot users are concentrated in professional settings, particularly for workplace productivity and coding-related tasks (Table 1). The use of Copilot, in particular, seems to be largely driven by Microsoft’s own ecosystem.
Table 1. Most relevant domains for users of top chatbots for 2025
| Relevance score ranks | chatgpt.com | gemini.google.com | copilot.microsoft.com | claude.ai |
| 1 | google.com | reddit.com | bing.com | github.com |
| 2 | youtube.com | instagram.com | login.live.com | stackoverflow.com |
| 3 | instagram.com | youtube.com | msn.com | notion.so |
| 4 | reddit.com | github.com | m365.cloud.microsoft | aistudio.google.com |
| 5 | facebook.com | facebook.com | github.com | linkedin.com |
Men are more likely to use GenAI chatbots, but numbers differ by country. For the most part, men use GenAI chatbots at a higher rate than women across GPAI countries, although the gap is relatively small (Figure 6). Over time, the gap has fluctuated: decreasing in 2025 before expanding again in 2026. These variations are largely due to country-specific changes: throughout 2024, there was a rapid increase in female adoption in Finland, the Czech Republic, New Zealand, Denmark and Hungary, while in 2025, there was a relatively faster increase in male adoption in Korea, the United States, Norway, Germany and Türkiye.
However, some countries display more pronounced differences: Korea shows the largest gender gap, with male usage rates nearly double those of women. Germany and Italy also show significantly higher usage among men. By contrast, Singapore, the GPAI country with the highest overall usage rate, and Colombia, the Czech Republic, Finland, Hungary, Ireland, Mexico, New Zealand, and Saudi Arabia are the countries where female users adopt AI chatbots at a higher rate than male users (Figure 7).
GenAI usage rates by country in this analysis are broadly in line with other measures of GenAI tool usage, such as the Eurostat survey and the Microsoft AI Economy Institute (Figure 8). In 2025, Eurostat estimated that just under 33% of people aged 16-74 in the EU had used generative AI tools in the past 3 months. Microsoft’s AI Economy Institute found an average usage rate of 30% over GPAI countries for which data are available. In our estimates, we find that 27% of the total population has used AI on average over the last quarter of 2025. Overall, our measure is strongly aligned with these benchmarks, with a statistically significant correlation of 0.63 with Eurostat estimates and an even stronger correlation of 0.78 with Microsoft’s estimates.
Taken together, these results give us a clearer view of GenAI usage and how the picture is evolving. Usage is no longer niche. In just a year, GenAI chatbot use jumped from under a fifth to almost a third of the population across GPAI countries, with countries like Singapore and the Netherlands already seeing usage rates close to or above 50%. At the same time, differences in aggregate use between countries remain significant and warrant closer attention.
AI is also moving beyond its early adopters. While younger users still lead, much of the recent growth is coming from people over 35, signalling that chatbots are becoming mainstream. At the same time, usage is not uniform: some users engage with AI for recreational purposes, while others integrate it into productivity, coding, and workplace tasks. Gender gaps exist as well, but vary widely across countries, highlighting the importance of local context.
Traditional surveys remain essential but provide snapshots and are difficult to adjust to the pace of AI change. Web traffic data helps fill that gap by showing real-time GenAI usage data, how it shifts over time, and how patterns differ across countries and groups. Combined with other indicators on OECD.AI, these data provide a more complete and timely picture of AI diffusion and uptake.
SimilarWeb’s data comes from several sources:
Our analysis relies largely on SimilarWeb’s “unique visitors” metric, defined as the total number of distinct users visiting a domain in each month. This approach involves a deduplication process that accounts for the shared visitors from each of the three most popular chatbots. ChatGPT unique visitors were spliced to account for the domain transition from openai.com. The estimates cover the vast majority of users, as users of other chatbots typically also visit one of the three captured here, and would therefore almost always be removed during the deduplication process.
These data cover the period from February 2024 to March 2026, a timeframe that captures significant developments in the AI landscape. The analysis is also further scoped down to the 37 Global Partnership on AI (GPAI) countries available on the SimilarWeb platform (see https://oecd.ai/en/about/about-gpai for more information on GPAI). As of this analysis, these countries are Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, Colombia, Czechia, Denmark, Finland, France, Germany, Greece, Hungary, India, Ireland, Israel, Italy, Japan, Korea, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Saudi Arabia, Serbia, Singapore, Slovak Republic, Spain, Sweden, Switzerland, Türkiye, United Kingdom, and the United States.
The lower adoption rates in the SimilarWeb estimates when compared to other estimates mainly reflect differences in how each source defines an AI user. First, in the Eurostat data, the distinction is not made on how the user is accessing AI and is therefore considered a broader indicator than our SimilarWeb approach, which focuses solely on AI chatbots accessed via the website (i.e. not including App users). For example, respondents to the Eurostat survey may consider the AI summaries given after Google queries to be a use of generative AI, but is not captured in our measure. Secondly, someone counts as an AI user if they used AI at least once in the past three months in the Eurostat survey, while Microsoft uses a six-month window. Our measure takes the monthly average, which means that a person who visits a chatbot only once during the three-month period (to align with Eurostat) contributes less than someone who uses AI regularly.
Ultimately, each source comes with its own strengths and limitations:
Taken together, the differences highlight why no single measure currently captures AI adoption on its own. Our new GenAI usage estimates provide a valuable perspective: a timely, internationally comparable indicator that helps track users, their demographics, and how regularly they engage with AI chatbots in practice.
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AI agents and agentic AI based on large language models are becoming more autonomous and capable of interacting with both physical and virtual environments. As the capabilities of these AI systems grow, they are gaining visibility, and with reason. It is reaching a point where they could become the driving force behind innovation, investment and improved productivity across sectors by streamlining processes and enabling more efficient operations.
While ideas related to agency have long been explored in academic research in fields such as philosophy, economics and computer science, recent advances in AI are stretching conceptual boundaries. As AI’s capabilities evolve, so do our shared understanding of what qualifies as AI agent and agentic AI.
The OECD report, The agentic AI landscape and its conceptual foundations, developed by the OECD.AI Expert Group on Agentic AI, helps clarify what AI agents and agentic AI are and how they differ. Grounded in the OECD AI system definition, the analysis examines how these terms are defined and used across the literature. By analysing key features, overlaps and distinctions and mapping them to the core elements of the OECD definition of an AI system, the report helps to establish more precise and consistent terminology. And in a rapidly evolving field, conceptual precision is essential for effective, well-informed governance.
Three key messages stand out in the report:
Our analysis shows that AI agents and agentic AI share foundational characteristics. Both involve systems with a degree of autonomy that pursue goals and can perceive and act within physical and virtual environments.
However, there are differences that mean these terms are not interchangeable.
In short, agentic AI is more complex, as it can co-ordinate multiple agents, perform task decomposition and delegation, and sustain operations over longer periods. It can also operate in more complex, less predictable environments with limited human oversight.
Agentic AI systems are not isolated technical artefacts. They are frequently embedded in social contexts and interactions and operate within a socio-technical paradigm.
Their value lies not only in autonomous action, but in interaction with other AI agents, humans and institutional processes. Co-ordination and negotiation across these actors require advanced reasoning capabilities, robust infrastructure and reliable communication protocols.
This relational perspective is an essential part of what agentic AI is. This means that understanding how they interact within broader ecosystems is essential to designing agentic AI systems that function responsibly and effectively, particularly in open or high-stakes environments.
The report also presents descriptive evidence on trends in AI agent adoption. Many developers have already integrated them into their toolkits, and survey data indicate that nearly half of respondents on Stack Overflow use them or plan to do so.
To be clear, adoption should not be confused with maturity. Developers highlight opportunities to further strengthen the security, privacy and accuracy of AI agents. These concerns underscore an important point: as the capabilities of agentic AI advance rapidly, progress in robust, trustworthy AI systems must keep pace.
Overall, the report provides a descriptive overview of the agentic AI landscape, clarifying key concepts and characteristics and establishing a shared analytical foundation. By anchoring the discussion in the OECD AI system definition, it aims to promote coherence across technical and policy communities.
Looking ahead, an improved understanding of real-world use will be essential to identify where safeguards, standards, and governance mechanisms will be most effective. Policy-relevant typologies that build upon this work could help guide governance efforts to distinguish systems by level of autonomy, degree of adaptiveness, domain of operation and scale of impact. Evidence-based policymaking will require more empirical data on how AI agents and agentic AI are being adopted and used across sectors, as well as clearer evidence of their broader implications and impacts.
This report contributes to a clearer, shared understanding of agentic AI and provides a basis for thoughtful, forward-looking policy grounded in conceptual clarity. As agentic AI systems become more capable of coordinating multiple AI agents, taking action and operating over longer periods, governance conversations have to keep pace.
The post Can we create a clear understanding of what agentic AI is and does? appeared first on OECD.AI.
Artificial intelligence is changing fast, and the world is feeling both excited and uneasy about it. People use AI tools every day in hospitals, classrooms, companies and public services, yet the rules that guide these tools are still developing. Many governments are trying to find a balance between innovation and safety. Others are trying to make sure that AI actually improves people’s lives without widening gaps.
This is the backdrop against which the India AI Impact Summit 2026 will take place in New Delhi in February. Earlier global AI meetings, including the 2023 gathering at Bletchley Park and subsequent summits in Asia and Europe, helped define the risks and push for action.
These summits did not occur in isolation but are part of broader global efforts to coordinate responsible approaches to AI. The G7 Hiroshima Process in 2023–24 established a shared commitment to trustworthy, human-centric AI, leading to the adoption of the Hiroshima Declaration, which calls for international cooperation on safety, transparency, and risk mitigation.
Building on that, the Paris AI Summit in 2025 moved the conversation toward implementation, with an early agreement on safety evaluations, incident-reporting mechanisms and commitments to support countries with limited technical capacity. The India AI Action Summit represents the next step in this progression: translating these collective principles into measurable on-the-ground outcomes.
In recent months, people have repeatedly asked me two questions. Why should India host such a major global meeting? And is this summit actually useful for India and the world?
The simple answer here is that the next phase of AI will not be decided by a small number of companies or countries. It will depend on whether billions of people, especially in the Global South, can use AI safely, affordably and accountably. India, with its linguistic diversity, strong digital public infrastructure and experience deploying technology at a population scale, is well positioned to help shape this practical phase.
However, AI comes with challenges related to privacy, digital exclusion and the balance between innovation and oversight. But these very tensions make India’s experience pertinent to other countries facing the same trade-offs.
This blog post explains why that matters, what the international community can expect in Delhi, and how we should measure progress at the end of the summit.
Much of the world’s discussion on AI has focused on frontier models, international competition and long-term safety. These debates are important, but AI’s greatest impact will be felt in how it reaches ordinary people. From farmers and students to small businesses, frontline health workers and local governments.
More than half of the world’s population lives in countries categorised as the Global South — a term first popularised in the late 1960s to describe post-colonial economies, and one I don’t fully agree with, as it often flattens diverse countries into a single broad category.
If AI is to be truly global, it must work for multilingual, resource-constrained and diverse environments. This includes reliable translations, culturally grounded datasets, accessible interfaces and low-cost deployments. It also means designing systems that respect human rights and democratic norms even in places with limited regulatory capacity.
India sits at the intersection of these challenges. With over a billion people, 22 official languages and thousands of dialects, any technology deployed at scale must be inclusive by design. India’s experience offers lessons for many other countries navigating the same realities.
India’s digital public infrastructure, or DPI, is one of the most widely referenced success stories of how technology can enable access and accountability. Systems like Aadhaar, UPI and DigiLocker have helped millions access identification, financial services and digital records. These platforms were built with interoperability and openness in mind, which has led to a wave of public and private innovations.
At the same time, these systems have also raised important questions about privacy, data security, and exclusion of marginalised communities who lack documentation or digital access. India’s ongoing work to address these concerns—through data protection legislation, improved grievance mechanisms, and efforts to reach the digitally excluded—provides practical lessons about implementation challenges that other countries will inevitably face.
The India AI Impact Summit is expected to draw on this experience, including both successes and areas for improvement. The global community is watching to see how India will frame the link between AI and digital public goods, and how these tools can be used responsibly in sectors such as education, healthcare and social protection.
The earlier global AI safety and governance summits created momentum. They helped identify risks, promote transparency and encourage cooperation. But now, many countries and organisations want clarity on what should happen next.
The India summit is an opportunity to shift the conversation from what AI might do to what it should deliver. This includes measurable improvements in public services, clearer accountability mechanisms and more inclusive access to AI tools. By focusing on implementation, India can complement the work of the OECD-GPAI, UNESCO and other international bodies.
The summit should begin by asking a straightforward question: What changes on the ground when AI is deployed responsibly at scale? To answer it, discussions need to move beyond broad aspirations and focus on concrete domains like public healthcare triage, classroom support tools, agricultural advisory systems, and other public-sector applications where impact can be seen and measured.
Government delegates should be encouraged to present evidence, not statements of intent. That means clear baselines, transparent evaluation methods, and metrics that reflect real improvements: higher diagnostic accuracy, increased crop yields and shorter benefit-processing times all achieved without compromising fairness or human oversight.
If the summit succeeds, it will shift the global conversation toward what works, for whom, and under what conditions.
A meaningful summit requires a wide range of voices—especially from regions where AI deployment will shape social and economic outcomes for decades to come. Countries across Asia, Africa, Latin America and the Middle East bring their lived experiences of linguistic diversity, data scarcity, affordability constraints and uneven digital access.
The summit should create space for these countries to set priorities rather than simply respond to frameworks developed elsewhere. Their perspectives are vital for building governance models that reflect the realities of low-resource contexts, rather than idealised assumptions from high-income environments.
A more pluralistic conversation would reinforce a simple principle: responsible AI cannot be universal if it is not also contextual.
One of the most important roles the summit can play is to broaden the global AI discourse. Today, existential risk narratives dominate many international forums, often overshadowing more immediate and systemic issues. The India AI Impact Summit should refocus attention on the present: AI’s energy footprint, labour displacement, rising misinformation, digital exclusion and the growing pressure on public institutions to oversee algorithmic systems they are not adequately equipped to oversee.
The environmental dimension deserves particular attention. Training and deploying large AI models require significant energy resources, disproportionately affecting the Global South. Many of these countries face climate vulnerability, fragile grids and competing development priorities. For regions already grappling with heatwaves, droughts and energy shortages, the cost of “AI at scale” cannot be separated from broader planetary concerns. If AI is to be deployed responsibly, discussions must also consider energy and natural resource efficiency and equitable access to compute.
These issues determine how people experience AI today and whether they trust it tomorrow. Giving them equal weight would help correct the imbalance in global discussions and lead to governance that addresses risks people actually face, not only those imagined at the far horizon.
Across sectors, governments are eager to use AI to strengthen healthcare, expand access to education and streamline welfare delivery. Yet many lack clarity on how to procure, evaluate or oversee these systems responsibly. A meaningful outcome of the summit would be simple, actionable pathways that public agencies can adopt without specialised expertise. These might take the form of evaluation checklists with acceptable error and bias thresholds, procurement templates with human oversight requirements, or clear guidance on when and how officials should override an AI recommendation. Transparent case studies and training for civil servants would also help countries move from hesitation to informed, confident experimentation.
Concerns about misinformation, bias, privacy and security continue to rise, and many countries, particularly those with limited technical capacity, need practical tools to manage these risks. The summit could make a real contribution by advancing shared approaches to incident reporting, auditing and assurance, as well as safety testing methods that work across varied deployment contexts. Small pilot frameworks would help establish a common baseline of accountability. Such efforts would not only support global cooperation but also build public trust at a time when many citizens and policymakers remain uncertain about the reliability of AI systems.
Many countries struggle with adapting AI to their unique linguistic profiles. India’s long-standing work in language technologies positions it to convene collaborations on multilingual and inclusive AI. New partnerships on datasets, dialect-specific models, local-first interfaces and research on linguistic bias could meaningfully expand access for millions of people worldwide.
But inclusion must be matched with safeguards. As AI tools become more widely available, countries will need parallel investments in risk-assessment expertise, regional coordination on harmful content and support for the development of first-generation regulatory frameworks. Striking the right balance between openness and safety would reinforce core OECD AI Principles and help ensure expanded access does not bring greater vulnerability.
Research on frontier AI risks must continue, but the India AI Impact Summit signals an important rebalancing of global attention, as mentioned before. It asks policymakers to look beyond hypothetical future scenarios to acknowledge how AI is already shaping critical aspects of our daily lives, from healthcare triage and classroom instruction to welfare delivery, agricultural advice and urban mobility.
For most people, the urgent question is not whether AI poses an existential threat, but whether the systems they encounter today are reliable, safe and genuinely useful. The summit’s focus on practical impact aligns global governance with lived reality.
A second implication is the reaffirmation that inclusion is not a downstream concern but a prerequisite for responsible AI. Global conversations often gravitate toward powerful models built in highly resourced environments, yet billions of people rely on limited connectivity, low digital literacy and minority-language interfaces.
India’s leadership places these conditions at the centre of the global agenda. It broadens the imagination of what “good AI” must account for, reminding the world that both equitable deployment and cutting-edge capability are essential to whether AI helps or harms societies.
The summit also nudges the world toward a more open and cooperative model of AI development. Some countries can share tools, datasets, and governance mechanisms to help each other build their own capabilities rather than remain passive consumers. Openness here is not about lowering standards; it is about raising the global floor and ensuring that safety capacity grows alongside access. Many countries want to participate meaningfully in the AI economy, and the summit offers a platform to explore practical pathways for doing so.
The India AI Impact Summit 2026 is more than just another international meeting because it represents a shift from abstract debates to concrete action. Its core question — how to make AI useful, safe and inclusive at scale — goes to the heart of global governance. If the summit delivers practical tools, clearer deployment pathways and stronger cross-regional collaboration, it will set a new benchmark for what international coordination on AI can achieve.
The world is watching India, not because it claims to have all the answers, but because it has repeatedly demonstrated the ability to turn large-scale ideas into real-world outcomes. And it has done so while openly confronting the tensions and trade-offs that accompany such efforts. In a period of rapid technological change, this experience is invaluable.
As AI evolves, the global community will increasingly need countries that can translate principles into practice at a population scale. The India AI Impact Summit is a chance to advance that work. If successful, its influence will extend far beyond India, shaping how the world understands and pursues responsible AI in the years ahead.
The post The Global South can shape AI in practical terms: Why the India AI Impact Summit Matters appeared first on OECD.AI.
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