Anthropic News

A research agenda for the Economic Futures Research Fund

Tue, 21 Jul 2026 16:00:00 GMT

We’re sharing the research agenda for the Anthropic Economic Futures Research Fund. We’re committing $200 million to the fund to support ambitious external research on interventions to prepare society for the economic impacts of AI.

With the research the Fund supports, we want to study what programs could make the economy more flexible and resilient, ensure the benefits of AI are shared, and minimize the harm that AI-driven disruption could cause.

In the fund, we’ll prioritize five research areas:

Shaping AI’s impact on workers at the firm and workplace level
Equipping people to navigate AI-driven transitions
Modernizing income support for AI-driven displacement
Building worker stakes in AI-driven growth before disruption arrives
Generating new evidence on public investments.

What is the fund?

AI capabilities continue to improve. But we don’t yet know how quickly AI will diffuse throughout the economy while also becoming more capable, and what the economic effects will be. In our Economic Policy Framework (EPF), published in June, we proposed programs and policies for a range of scenarios. But we need more empirical evidence on which interventions might actually work in an AI-transformed economy—which ones make the economy more flexible and resilient, and spread the gains broadly. In the face of this uncertainty, our aim is to build this evidence base so that workers, firms, and governments have room to adapt. This $200 million fund will support external research on interventions proposed in the EPF and on other open questions. We may be entering a moment without historical precedent, where the most promising solutions are ones nobody has tried yet. We’re willing to fund creative and ambitious pilots that can provide guidance on questions where randomized control trials alone might only provide incremental evidence.

This is a significant evolution of our Economic Futures program, launched a year ago. We’re updating our focus to ambitious projects and large grants, because we think it’s where we can have the highest impact. We’ve always thought that it’s important to fund big external research bets; this shift will let us do so. We also learned through the Economic Futures program that it’s hard for us to scale capacity to manage many small grants at once. In addition to large-scale RCTs and pilots, we’re also interested in working with partners that could scale up a program of effective small-scale pilots.

The kinds of research we’re funding

Fundamentally, we want to fund the most ambitious proposals possible. We aim to fund large-scale RCTs or ambitious, creative pilots or program evaluations that expand our shared understanding of what shows promise and in which contexts, fill gaps where evidence is thin, and inspire new solutions. The fundable directions below lay out a set of possibilities, but we know that we have not come up with all the good ideas in this space. We welcome proposals that may not be captured below.

AI could transform society faster than traditional research funding and publication cycles can keep pace with. We’re looking to partner with research organizations that are willing to share what they’re learning publicly at key milestones because a signal that arrives early enough to act on can be worth more than an answer that arrives too late. We're especially interested in pilots that can be scaled up dramatically if they show promise.

This is a global fund. The funding directions that we’ve outlined below are somewhat US-centric, in part because we’re headquartered in San Francisco, and Claude is used more in the US than any other country. But the need to prepare for disruption will be necessary worldwide, and we expect to fund projects in a way that reflects that.

We plan to primarily fund projects in the $5-30 million range, though we’re flexible upward for well-scoped, high-potential-impact projects. Based on what we learned from the Economic Futures program, and the ambition and scale we’re seeking in proposals, we won’t directly fund anything below $1 million from this fund.

We’ll accept proposals from accredited universities and other degree-granting institutions, from independent research institutes and policy research organizations, and from nonprofits with a track record of running field experiments at scale. Individual researchers may serve as principal investigators on proposals made by their institutions, but we won’t consider proposals from individuals applying in their personal capacity.

We’re more likely to fund projects that fit one of our research priorities, but we welcome ambitious proposals outside them, as long as they’re calibrated to the scale of the problem and opportunity. See our request for proposals and apply here.

Our five research priorities

1. Shaping AI’s impact on workers at the firm and workplace level

AI’s impact on the labor market depends on the systems, workplaces, training protocols, and institutional choices that are built around it. The existing evidence on AI’s integration in the workplace is observational and short-term. Field experiments can help us understand which collaborative patterns develop human expertise alongside AI, how organizational design choices affect both productivity and who captures the gains, and what difference worker voice makes in those design choices.

Without this evidence, both firm-level decisions and policy levers like incentives for worker augmentation, retention tax credits, employer co-investment requirements, or apprenticeship programs will be poorly informed.

Fundable directions include:

Field experiments randomizing AI systems and AI integration designs at the firm or team level, including comparisons of designs co-developed with workers and worker organizations against top-down approaches.
Estimates of the impact of organizational choices around AI workplace integration and usage on the incidence of AI productivity gains.
Evaluations of retention tax credits and employer co-investment requirements.

2. Equipping people to navigate AI-driven transitions

The evidence on retraining and job placement is mixed, and it may not generalize to AI-induced economic disruption and rapid structural transformation.

Fundable directions include:

Evaluations of innovative skill retraining, job placement, licensing reform, and sectoral transition packages, including newer AI-enabled matching, credentialing, and learning models, and the bundling of income support with intensive reemployment services, retraining, and relocation assistance.
Field experiments on the early-career and professional pipeline, e.g., what apprenticeship, mentorship, or rotational models can build expertise if junior tasks are absorbed by AI.
Evaluations of curriculum and educational delivery models in K-12 and higher education that aim to prepare students for a transformed labor market, including longitudinal pilots that link educational interventions to later labor market outcomes.
Tests of ambitious mobility instruments, for example paid leave tied to retraining programs and portable benefits that follow workers across employers.

There’s existing evidence on many such efforts, including some especially effective sectoral training programs. We want to find out whether promising programs could scale quickly across a broader population. For example, a large-scale “fire drill” where selected programs are scaled up rapidly for job seekers in a given state could provide evidence on how well these programs work in the face of major disruption.

3. Modernizing income support for AI-driven displacement

Like similar insurance programs around the world, the US system for supporting displaced workers is built almost entirely around the assumption that joblessness is temporary. AI may lead to displacement that is broader and more persistent. In that scenario, we’ll need instruments calibrated to a new equilibrium, one with no modern precedent.

Fundable directions include:

Unemployment Insurance (UI) reforms suited to AI-driven displacement, including alternative eligibility thresholds, automatic extension triggers linked to industry or occupation, and integration of UI with wage insurance, retraining, or other transition supports.
Basic needs relief for workers who exhaust UI, never qualified, or are persistently underemployed.
Longer-duration unconditional income pilots at livable levels, designed to speak to scenarios where income and work are decoupled for sustained periods of time, with analyzed outcomes spanning not only labor supply and consumption but also wellbeing, family stability, child development, civic participation, and how recipients structure their time.

4. Building worker stakes in AI-driven growth before disruption arrives

In unprecedented scenarios where AI delivers large aggregate gains, those gains may not be broadly shared by default. In the EPF, we discuss universal pre-distributive capital accounts and adjacent mechanisms, like equity-sharing, AI-sector dividends, and public ownership stakes. But these mechanisms have limited direct empirical precedent at scale, and they also need a funding source. Many proposals to generate revenue exist, including taxing AI-driven returns through corporate, capital gains, or token taxes. But we lack evidence on who would bear the economic incidence of such taxes, and how different designs would affect collected revenue and adoption.

Fundable directions include:

RCTs testing the design of pre-distributive capital accounts at scale.
Pilots testing equity-sharing or dividend-style mechanisms, including community-level pilots where AI infrastructure or AI-using firms generate direct, ongoing returns to local residents.
Evaluations comparing different mechanisms for raising and distributing revenue—which tax base (corporate profits, capital gains, compute, automation taxes, etc.) and which mechanism (pre-distributive accounts, equity stakes, dividends, or equivalent direct transfers) lead to the best labor market and household outcomes.

5. Generating new evidence on public investments

The EPF calls for both modernizing the income safety net and substantially expanding public investment in human- and community-facing work. Policymakers need a consistent way to compare these instruments against one another, and against direct transfers. This research would generate evidence on what forms of spending generate the most public benefit, especially in sectors that might be undervalued by the private market.

Fundable directions include:

Large-scale pilots that directly fund human- and community-facing service positions (in e.g., teaching, after-school programming, libraries, community health, parks, infrastructure, the arts), measuring outcomes including employment levels, educational attainment, crime, and wellbeing.
Pilots broadening access to AI-enabled public services (legal aid, medical guidance, financial advice) for underserved populations, testing whether such investments can narrow the divide in access.
Guaranteed-jobs pilots for displaced or long-term unemployed workers, in which participants are offered employment in public good roles in the spirit of the Civilian Conservation Corps but spanning a broader range of roles.
Place-based interventions in communities most exposed to AI-driven displacement or hosting major AI infrastructure build-outs, including bundled investments in workforce, public services, infrastructure, and amenities, and pilots of regional development authorities that coordinate these investments under unified governance.

Learn more about the RFP and apply here.

Ask Claude about the Anthropic Economic Index

Tue, 21 Jul 2026 16:00:00 GMT

People have hard questions about AI and work: which jobs will change, which tasks are being automated, and what it means for their own field. The Anthropic Economic Index exists to help answer them with real data. Today we're launching the Anthropic Economic Index connector for Claude, which lets anyone explore that data directly.

The Anthropic Economic Index measures how AI is actually being used in the economy. The Index’s data has been useful to researchers, journalists, and policymakers, but we want it to be just as accessible to anyone curious about how AI fits into their field or day-to-day life. Now you can ask Claude questions like:

“Which occupations use AI the most?”
“What are the most common ways people in Colorado use Claude?”
“What sorts of tasks do teachers use Claude for?”
“What kinds of tasks are people automating with AI? How has that changed over the past year?”

You’ll get answers grounded directly in the Index data.

Getting started takes about a minute. In claude.ai, open the connectors menu, find the Anthropic Economic Index in the directory, and enable it—it works in any conversation with any Claude model, and there's nothing to install. From there, just ask questions the way you'd ask a colleague: start broad (“What does the Index say about my industry?”), then drill into specifics, and ask Claude to show you the underlying data behind any answer.

As always, the Index reflects patterns in Claude usage rather than the labor market as a whole, and Claude will point you back to the source data and its limitations as you explore. You can find the connector in claude.ai today, and the full datasets remain freely available on our website.

Anthropic is donating another $20 million to Public First Action

Mon, 20 Jul 2026 16:00:00 GMT

We're contributing an additional $20 million to Public First Action, bringing our total support to $40 million. Public First Action is a nonpartisan organization that educates the public about AI and works with Republicans, Democrats, and Independents who are serious about putting sensible AI safeguards in place. Both of our donations were made exclusively to support Public First Action’s public education and policy mission, and cannot be used to influence the election of any candidate for federal, state, or local office.

Our first donation in February 2026 was made to help promote policies that will maintain meaningful safeguards, sustain America’s AI leadership, and demand transparency from the companies building the most powerful AI models. In the months since, the case for these policies has only gotten stronger.

Why now?

AI models continue to improve at a rapid pace. Earlier this year, Claude Mythos Preview discovered thousands of high-severity software vulnerabilities, including some in every major operating system and browser. We chose to release it to a limited set of cyber defenders through Project Glasswing to enable trusted actors to find and fix those weaknesses before anyone could exploit them. In the wrong hands, models like this could threaten the critical systems the country relies on, from hospitals to the energy grid.

More capable models will bring benefits—like compressing drug discovery, developing treatments for diseases we’ve never been able to treat, accelerating science to deliver a century of progress in a decade, extending human lifespans, and driving the kind of economic growth that lets everyone share in the prosperity—but we need to make sure we are protected against the risks first in order to realize those benefits.

Governments need time to build the capacity to capture benefits while containing risks, which is why they need to start now.

Policy that meets the moment

We’ve long argued that frontier AI companies should be transparent about what their models can do and how they’re managing the risks. We’ve supported newly passed laws in several states that require greater transparency for AI developers. But given how fast the capabilities of the most powerful models are advancing, transparency alone is insufficient.

We need policymakers and candidates to put forward measures that mitigate risks. This is the core of what we’ve laid out in Anthropic's Advanced AI Framework, which is the strongest policy proposal from any frontier lab or policymaker to date. Governments should be able to verify companies’ safety claims, enforce safe practices through civil penalties, and ultimately have a way to slow or block the deployment of AI models that pose a serious risk of catastrophic harm. Frontier AI developers should have to test models that pose catastrophic risk, be transparent to the public about their findings, submit them to independent evaluation, and maintain a robust security program.

As the pace of AI advances accelerates, the national security stakes of AI are growing, as we outlined in 2028: Two Scenarios for Global AI Leadership. America and our democratic allies have the advantage today, but that lead is tenuous. To ensure continued American AI leadership, we support policy efforts to tighten export controls on advanced chips and semiconductor manufacturing equipment and to curb illicit model access and distillation attacks, so that democracies’ best technologies are not used to advance authoritarian AI.

What comes next?

These policy frameworks are just a starting point. Our donation to Public First Action is one way in which we’re trying to raise the salience of this urgent policy debate.

Apply for Anthropic’s AI for Science rare disease research grants

Sun, 19 Jul 2026 16:00:00 GMT

Last spring, we announced Anthropic’s AI for Science program, an initiative designed to accelerate scientific research and discovery through access to our API. Since launching, we have supported researchers working on a variety of high-impact projects, ranging from drug repurposing to quantum simulation. Throughout this initiative, we have found that projects are more generative when multiple AI for Science grantees are working on related questions and exchanging tips. So we now plan to launch thematic calls for projects within the broader AI for Science program.

Today, we are sharing a focused call for applications centered specifically on rare genetic diseases. Accepted applicants will receive up to $50,000 in Claude credits over six months, with the goal of building a community of researchers looking into how AI can reshape our understanding of rare disease. This program has two tracks: one for scientists doing basic research, and another for early-stage biotechs working on speeding up clinical development for rare diseases.

Rare disease research is an area where knowledge of fundamental science is limited. In aggregate, rare diseases are among the most prevalent conditions on the planet (an estimated 400 million people live with one of more than 7,000 rare diseases).¹ But these conditions are scattered across small populations, making it challenging for clinicians to build patient registries, identify promising therapeutic targets, and design clinical trials. Moreover, rare diseases are typically characterized by their unique features (such as a specific genetic variation or combination of symptoms) that are often studied in isolation, making it nearly impossible to spot mechanisms shared across diseases. Finally, rare diseases face a challenge endemic to all drug development: the time it takes to move promising drug candidates into patient trials.

We think AI can help with these and related challenges. AI makes it possible to accurately model rare genetic diseases and detect patterns across them. It also helps researchers synthesize findings across a large corpus of literature, quickly extract information from limited datasets, and create shared terminology, all of which informs how researchers can better use the information they do have, even as more work is done to generate more data and address challenges pertaining to access and geography. To explore where AI can be most helpful, we’ve made rare diseases the focus of our current call for AI for Science projects.

Track one: Scaling our basic science partnerships

The first track of our rare disease research grants program aims to foster collaboration between clinical researchers, patient organizations, and data scientists to increase the pace of progress in basic science and the discovery of the mechanisms underlying rare diseases.

An early partner in this effort is the Monarch Initiative, an international consortium working to improve diagnosis and mechanism discovery for patients with rare diseases. Monarch develops standards and resources such as the Mondo Disease Ontology, a computational framework and coding system that reconciles disease definitions scattered across OMIM, Orphanet, ICD, and dozens of other sources; as well as the Monarch Knowledge Graph, which integrates genotype-phenotype data across species to aid diagnostics and mechanism discovery.

Most recently, Monarch contributors have been stitching data and knowledge together in a new agent-friendly mechanistic disease classification library called DisMech, where Claude can read case reports, variant databases, registry schemas, raw public data, and more, and point out mechanistic similarities between diseases at an unmatched pace and scale. Monarch is inviting our AI for Science grantees to use and contribute to its resources, such as Mondo and DisMech, to reveal new mechanistic hypotheses that will support developing treatments.

Monarch’s work on improving the interoperability of rare disease data and knowledge is a place where Claude can already have a major impact. However, there’s more work to be done to gather better and more data, improve diagnostic infrastructure, and promote patient-led approaches across the rare disease ecosystem, and make the information accessible to agentic science. We will continue to partner with Monarch and others to approach this problem from the angles where AI is less obviously applicable, and we’ll share what we learn as we do.

Track two: Scaling our biotech partnerships

The second track of our rare disease research grants program will support biotechnologists and early-stage biotechs working to accelerate drug development for rare diseases. Today, it takes one to two years to move from a confirmed genetic diagnosis to a treatment available to patients, with much of this time spent waiting in queues for certified manufacturing slots, running safety studies sequentially instead of in parallel, and hand-assembling the thousands of pages of chemistry and regulatory documentation required for in-patient testing.

We think it is possible to radically compress phases of this process with Claude—in particular, by making it easier to complete documentation (for example, drafting and reviewing the regulatory dossier), but also by speeding up therapeutic strategy selection (for example, by analyzing whether a target is druggable across a suite of modalities, such as small molecules, antibodies, genetic medicines, and so on) and looking for shared mechanisms across individual genetic therapies, which could allow them to be approved under a single “basket trial” instead of requiring a separate IND for each patient.

Although many aspects of drug development are difficult to expedite because of manufacturing constraints or safety testing, we believe much can be done to move more quickly. By granting API credits and Claude Science access to the many biotechnologists and startups working in this space, we hope to encourage the experiments necessary to explore and identify such solutions.

We also hope grantees will amplify and emulate the efforts of our other partners working in rare disease therapeutics. For example, Every Cure, one of our existing AI for Science grantees, is using Claude to identify drug repurposing opportunities across millions of candidates ; the Centre for Population Genomics, a collaboration between the Garvan Institute and the Murdoch Children’s Research Institute, is building a Claude-based system that drafts variant classifications for expert review, one of the biggest bottlenecks in diagnosing rare genetic conditions; and the Violet Research Institute, a small nonprofit researching ultra-rare genetic diseases (defined as extremely rare genetic disorders affecting fewer than 1 in 50,000 births), is using Claude to navigate FDA guidelines, run bioinformatics pipelines, analyze experimental data, draft regulatory filings, and more.

Next steps

To apply to either track of the AI for Science rare disease research program, fill out this application. We will be accepting applications through August 2, 2026 at 11:59 PM PST. Accepted applicants can use their credits to access Claude Opus or other generally available models approved for use in biology. Projects that may run up against our bio classifiers may be eligible for exemptions. Examples of track one projects include:

Propose and rank mechanistic links between distinct rare diseases that share a gene or pathway, suggesting candidate disease relationships with evidence an expert can validate in Monarch’s DisMech.
Curate and summarize patient organization data to conduct or improve existing natural history studies.
Build evaluations that measure how well models handle rare disease tasks—such as revealing candidate mechanisms for variants of unknown significance, phenotype-to-disease matching, and mechanism prediction—including an honest accounting of where they fail.

Outputs from this track will be made publicly available at Monarchinitiative.org. The program will be augmented by additional community-building efforts, such as future rare disease hackathons. Stay up to date with the Monarch Initiative at monarchinitiative.org/community/get-involved.

Examples of track two projects include:

Justify starting doses from sparse data: synthesize PK/PD modeling, allometric scaling, and precedent from related modalities to build first-in-human dose rationales for bespoke therapies where traditional dose-ranging studies are impossible.
Mine natural history data and case reports to identify measurable biomarkers and functional endpoints sensitive enough to show a response within the timeframe an N-of-1 or ultra-rare program can afford.
Draft, cross-check, and precedent-mine regulatory documentation (IND sections, investigator brochures, CMC modules), compressing months of dossier assembly into days of expert review.

This rare disease grant program ties directly into our mission and work in beneficial deployments to extend the benefits of AI to areas that might not emerge naturally through market forces. However, rare disease is simply too big a problem for one organization or one approach. We also want to be honest about AI’s limitations in this space. Although Claude may help shorten therapeutic development timelines and curate biological data more efficiently than human teams alone, it cannot help in areas where the data is too paltry or too poorly organized for agents to reach. It may also struggle to address the aspects of the “diagnostic odyssey” that relate to challenges like insurance authorization or access to diagnostic facilities and infrastructure. We hope this program will be complemented by efforts by other organizations and research institutions to generate more high-quality, longitudinal data, as well as those that encourage robust public-private partnerships.

We're looking forward to seeing how this new group of grantees will work together and with our other AI for Science partners to advance basic science and discovery in rare diseases, and how these projects can contribute to broader scientific initiatives.

Footnote

Other sources place the number of rare diseases as high as 10,000. There is also no agreed-upon definition of what a rare disease even is, despite the oft-cited claim that as many as 1 in 10 people in the US have a rare disease. Various terminologies (Orphanet, OMIM, GARD, ICD, the NCI Thesaurus, and dozens more) each define “disease” differently; some exclude chromosomal disorders (such as conditions like Pallister-Killian syndrome); some ignore diseases with environmental causes (such as congenital Zika syndrome); and some require a single anatomical system to classify them, ignoring the many multi-system rare diseases (such as Fanconi anemia, with its mix of bone marrow failure, congenital malformations, and cancer risk).

Introducing Claude for Teachers

Mon, 13 Jul 2026 16:00:00 GMT

We're introducing Claude for Teachers, providing verified K-12 educators in the US free access to premium Claude capabilities, a library of teaching skills, and a direct connection to evidence-based curricula, mapped to academic standards in all 50 states.

Why we’re building for teachers

Decades of research show that practices like differentiation, mastery-based learning, and small group instruction reliably improve student achievement, but teachers are often short on time and resources to implement them. Budgets are stretched, classes may be too large to meet every student's individual needs, and planning often spills into evenings. This strain is heaviest in under-resourced schools. Claude for Teachers is designed to close the gap between educational best practices and what a teacher's week allows.

Early evidence suggests that while the impact of AI tools for students is mixed and depends on the implementation, AI tools for teachers can strengthen instructional practice and improve student outcomes. This is the aim of Claude for Teachers: support the craft behind great teaching and protect what teachers value most—time with their students.

Connected to evidence-based curricula and the K-12 ecosystem

Claude for Teachers connects to Learning Commons, giving Claude access to academic standards across all 50 states—and beneath each standard, the smaller learning competencies it's built from and the order students typically learn them. So when Claude drafts a lesson plan, it’s scaffolded and aligned to teaching standards. Claude for Teachers also brings in trusted curricular resources like OpenSciEd and IM v.360 from Illustrative Mathematics.

As of today, educators can now connect Claude across an entire ecosystem of K-12 tools.

ASSISTments: Generate auto-scored, standards-aligned math problems for practice and assessment.
Brisk Teaching: Create interactive student activities and standards-aligned lessons, turning your ideas into classroom-ready materials in seconds.
Canva Education: Turn lesson materials into classroom-ready designs and interactive learning experiences.
Coteach: Create high-quality math diagrams grounded in K-12 curriculum.
Diffit: Create and adapt instructional materials for every student.
Eedi: Generate diagnostic questions that go beyond right and wrong to reveal student thinking and deepen insights, in both English and Spanish.
MagicSchool: Make instructional content classroom-ready.
Snorkl: Get insights about your classes, assignments, and student progress to inform your instruction.
TeachFX: Get personalized instructional feedback grounded in real classroom talk.

How it works

Once verified, K-12 educators in the US get access to Claude for Teachers with the Learning Commons connector and a set of tailored teaching skills grounded in learning science. The skills, co-developed with Learning Commons, were designed around the tasks teachers told us mattered most. These were evaluated to ensure rigor, pedagogical alignment, and classroom usability, and refined through early feedback from classroom teachers, including those in schools like Prospect Schools in Brooklyn.

Plan a lesson from high-quality instructional materials. Ask for a lesson and Claude draws on widely used curricula mapped to your state's standards, as well as fine-grained learning components and progressions beneath them, then drafts a plan and student-facing materials you can revise and take into class.
Differentiate for every learner in your room. Ask Claude to adapt materials for students at different readiness levels and it builds a differentiation plan for you, plus personalized student-facing materials for each proficiency level. The scaffolds make materials accessible to students, while students who are ready can be further challenged.

Claude for Teachers includes Claude Code and Cowork, which means Claude can carry work forward on its own. Some examples:

Analyze class data to plan instruction. Hand Claude a folder of data—roster, diagnostics, attendance, your own notes—and it builds a clear picture of where every student is, allowing teachers to better tailor instruction. You control what data is shared; nothing you share is used in model training.
Schedule repeated tasks. Hand off a task once—like reviewing each day's exit tickets to see what students mastered and adapting tomorrow's plan to match—and it runs every school day at 4pm. Claude works while you drive home.

Learn more from two teachers, Zac and Karina, explaining how they use Claude for Teachers in their own classrooms.

Built for educators, safe with student data

Claude for Teachers is for educators only, consistent with Claude's 18-and-over policy. It comes with its own teacher terms, built for K-12 privacy. Claude for Teachers data is not used for model training purposes, and student information is protected by our K-12 Data Processing Addendum, written to comply with FERPA.

We’re also working with the American Federation of Teachers to align our terms and privacy practices with gold standards they’re developing:

“We've been working with Anthropic on a Gold Standard that sets out industry best practices for safety and privacy in K-12 education,” said Randi Weingarten, President of the American Federation of Teachers. “It’s important that Anthropic is committing to these principles in their new Claude for Teachers — a tool designed by and for educators to assist them instructionally and hopefully give them more time for the human relationships at the heart of learning.”

Whether and how educational data may be used in Claude for Teachers is determined by your district and state policies. You can read more about our K-12 data privacy standards for Claude for Teachers here.

AI fluency for teachers

Teachers can also access our newly released AI Fluency for PK-12 Teachers, a course co-created with Teach For America, and a train-the-trainer module co-created with the American Federation of Teachers. The guidance is model-agnostic, Creative Commons-licensed, and practical, covering which classroom tasks AI is suited for and how to use it responsibly with students.

This product stems from Anthropic's broader efforts in education, focused on supporting teachers and improving learning outcomes for students. As part of our Beneficial Deployments mission, we’re releasing a set of public goods to support the community focused on building for teachers.

This includes the launch of new connectors in Anthropic’s directory, an open-source repository of the teaching skills, and a technical write-up of how we evaluated these skills and how other builders in education can use them. We’ll pilot an evaluation of Claude for Teachers in the Detroit Public Schools Community District, working closely with teachers to study the impact on educator wellbeing and practice. Together, these efforts support the goals of our partnership with the Gates Foundation to co-develop tools that improve educational outcomes for K-12 students. In addition, Playlab will support a national network of lab schools in implementing AI, helping educators become builders of the AI tools used in their classrooms.

Getting started

Once verified, educators can access Claude for Teachers entirely free. Sign up by June 30, 2027, for a full year of access.

Claude for Teachers is for individual educators. A dedicated offering for schools and districts is coming soon. In the meantime, districts interested in Claude can continue using Claude for Nonprofits.

Updated July 21, 2026: Added a note to emphasize that whether and how educational data may be used in Claude for Teachers is determined by district and state policies.

Anthropic commits $10 million to Canadian AI research

Mon, 13 Jul 2026 16:00:00 GMT

Le français suit.

Canadian institutions and researchers have played a critical role in the modern AI era. During a period of broad skepticism about research into neural networks, the University of Toronto and the Université de Montréal were two of only a handful of institutions that incubated this crucial line of work, while researchers at the University of Alberta did pioneering work on reinforcement learning. And in the early 2010s, Canadian research institutions led the way in demonstrating that with the arrival of powerful new computing resources—in particular, general-purpose GPUs—deep neural networks could succeed at scale, kicking off the modern era.

Today, Canadians both at home and abroad continue to play leading roles in AI research, safety, and policy—including at Anthropic. That’s why we’re committing $10 million CAD to Canadian research institutions to fund the next generation of this work. We’re also publishing our first Canadian country brief based on the Anthropic Economic Index, which provides a snapshot of how Canadians are putting Claude to work.

Investing in Canadian research

The $10 million we’re committing will fund research into beneficial and responsible applications of AI. As part of this, we’re announcing partnerships with Canada’s three leading regional AI institutes—Alberta Machine Intelligence Institute (Amii) in Edmonton, Mila in Montréal, and the Vector Institute in Toronto—along with CHEO, The Centre for Addiction and Mental Health (CAMH), Université Laval, University of Toronto, and the University of Saskatchewan, with more partnerships to follow in the months ahead:

Amii, based in Edmonton, will provide Claude credits to its research and engineering teams to further their work in areas like reinforcement learning and AI trust and safety, as well as to increase AI adoption across Canada’s key econoxmic sectors.
Mila, the Quebec AI institute home to the world’s largest concentration of academic deep learning researchers, will make Claude available to its community to support their research in areas including responsible AI, health, sustainability, multi-agent systems, and robotics. Mila will also use Claude to develop AI assistants that help researchers discover and assess scientific breakthroughs.
The Vector Institute in Toronto will use Claude credits to advance AI research in trust and safety, health and science, and the broader challenges that AI is uniquely positioned to help solve for Canadians.
CHEO and the CHEO Research Institute will use Claude credits to develop and evaluate AI-enabled approaches aimed at improving health outcomes and the care experience for children, youth, and families, and to study how AI can be most responsibly applied in children's health.
CAMH will make use of Claude credits across its research, education initiatives, and clinical projects. For example, CAMH’s Krembil Centre for Neuroinformatics will use Claude to conduct computational mental health research, including developing and evaluating predictive models of treatment for people with mental health conditions, and running large-scale evaluations of fairness in psychiatric AI systems. Credits will also be used to help scale the impact of the CAMH Global Learning Academy, which is developing multilingual, AI-enabled mental health education.
Université Laval’s Institute for Intelligence and Data will work with Claude to deepen researchers’ understanding of how LLMs behave in varied cultural contexts, as well as their understanding of low-resource languages and dialects, such as Quebec French and Indigenous languages.
The University of Saskatchewan will use Claude to further its research in areas including biomedical advancements, food and water security, public health, quantum computing, and public service.
The University of Toronto Data Sciences Institute will support a range of research projects through a scientific review-based process to access Claude API credits.

In addition to these donations, this summer Anthropic will add Amii, Mila, and Vector to the Anthropic for Startups program, which gives founders building on Claude access to a community and resources to support their growth. Hundreds of Canadian startups affiliated with these institutions will receive at least $5,000 USD each in API credits to continue to develop their businesses.

“Some of the foundations of modern AI came out of Toronto, Montréal, and Edmonton— and so, strikingly, did many of the researchers most committed to making it safe,” said Chris Olah one of Anthropic’s co-founders. “I was formed by that culture, and I’m proud Anthropic can support the next chapter.”

How Canada is using Claude

We’re also sharing data that looks at how people across Canada are using Claude. This is drawn from the March 2026 Anthropic Economic Index, our ongoing analysis of how AI is being incorporated into work tasks across the economy. Our economic research is based on data from real-world Claude usage, using a privacy-preserving analysis tool that identifies patterns in use while keeping all user information anonymous.

We find that Canada ranks eighth worldwide in Claude.ai use. Per person, Canadians use Claude at more than four times the rate the population predicts, and among the 10 countries where Claude is used most, only the US ranks higher.

Usage share and per-capita adoption among the top 10 countries by global Claude.ai use.
Bars show each country’s share of global AI usage (left panel) and the Anthropic AI Usage Index (right panel) based on 1M conversations sampled from Claude.ai in February 2026. Canada is highlighted in blue. The Anthropic AI Usage Index (AUI) measures whether Claude usage is over- or underrepresented in a country relative to its working-age population. Canada accounts for 2.6% of global Claude.ai consumer use, ranking eighth globally, and second by AUI. Sources: Anthropic Economic Index, February 2026; World Bank.

Within Canada, Claude adoption tracks the kind of work people do. Per-person usage is higher in provinces where professional, scientific, and technical work is concentrated. British Columbia leads in terms of per-person use, with Ontario—which has the largest share of overall conversations—close behind. In both provinces, usage is higher than what we would expect to see based on population alone.

Looking closer, we can see that how people use Claude lines up with the local economy. Translation requests are most common in provinces where more people work in government, likely because Canada’s bilingualism regulations require federal services and communications to be in both English and French. New Brunswick, Nova Scotia, and Québec lead the country in both government employment and the share of conversations about translation.

A shared view on the future of AI

The countries that invest the most in advanced AI over the next few years will also shape the rules that govern it. We’ve long believed democracies should lead that work, and Canada has a place at the forefront. Canada published the world’s first national AI strategy in 2017, and this June published its next: AI for All, which commits to strengthening the country’s AI safety institute, expanding AI literacy, and reinforcing the three national institutes that have anchored Canadian research for nearly a decade.

Last week we published a case study on the Government of Alberta, whose Ministry of Technology and Innovation used Claude Code to review 466 million lines of code across provincial systems in roughly 20 hours, then shared their methods with other governments.

The eight partnerships we’re announcing today are just the beginning of our investment in Canadian research. We look forward to supporting this work—in research, in hospitals, and in universities—for years to come.

Anthropic s’engage à verser 10 millions de dollars à la recherche canadienne en IA

Les institutions et les chercheurs canadiens jouent un rôle essentiel dans la révolution moderne de l’IA. À une époque où la recherche sur les réseaux neuronaux suscitait un scepticisme généralisé, l’Université de Toronto et l’Université de Montréal figuraient parmi les rares institutions à encourager la recherche dans ce domaine crucial, et des chercheurs de l’Université de l’Alberta menaient déjà des travaux fondateurs sur l’apprentissage par renforcement. Au début des années 2010, plusieurs instituts de recherche canadiens ont été parmi les premiers à démontrer qu’avec l’arrivée de nouvelles ressources informatiques puissantes (notamment le calcul générique sur processeurs graphiques), les réseaux neuronaux profonds pouvaient fonctionner à grande échelle, marquant ainsi le début d’une nouvelle ère.

Aujourd’hui, les Canadiens, tant au pays qu’à l’étranger, continuent de jouer un rôle de premier plan dans la recherche, la sécurité et les politiques entourant l’IA, notamment chez Anthropic qui, en juste retour des choses, a décidé de verser 10 millions de dollars canadiens à des instituts de recherche du pays afin de financer les prochaines avancées. Nous en profitons aussi pour publier notre première fiche d’information sur le Canada basée sur l’Anthropic Economic Index afin de donner un aperçu de la manière dont les Canadiens utilisent Claude.

Notre investissement dans la recherche canadienne

Les 10 millions de dollars que nous nous engageons à verser financeront divers projets de recherche sur les applications utiles et responsables de l’IA. Nous avons conclu des partenariats avec les trois principaux instituts régionaux canadiens spécialisés dans l’IA – soit l’Alberta Machine Intelligence Institute (Amii) à Edmonton, Mila à Montréal et l’Institut Vector à Toronto –, ainsi qu’avec le CHEO, le Centre de toxicomanie et de santé mentale (CAMH), l’Université Laval, l’Université de Toronto et l’Université de la Saskatchewan. D’autres partenariats devraient suivre au cours des prochains mois.

L’Amii, basé à Edmonton, mettra des crédits Claude à la disposition de ses équipes de recherche et d’ingénierie afin de leur permettre de poursuivre leurs travaux, notamment, sur l’apprentissage par renforcement ainsi que sur la confiance et la sécurité de l’IA en vue d’accroître l’adoption de l’IA dans les principaux secteurs économiques du Canada.
L’institut québécois d’intelligence artificielle Mila, qui regroupe la plus grande concentration au monde de chercheurs universitaires spécialisés dans l’apprentissage profond, mettra Claude à la disposition de ses équipes de recherche qui travaillent notamment sur l’IA responsable, la santé, le développement durable, les systèmes multiagents et la robotique. Mila utilisera également Claude pour développer des assistants IA qui aideront les chercheurs à faire des découvertes et à valider leurs résultats.
L’Institut Vector de Toronto utilisera des crédits Claude pour faire progresser la recherche en intelligence artificielle dans les domaines de la confiance et de la sécurité de même que de la santé et des sciences, mais aussi pour relever des défis locaux plus généraux que l’intelligence artificielle est particulièrement bien placée pour aider à résoudre.
Le CHEO et l’institut de recherche qui lui est affilié utiliseront des crédits Claude pour développer et évaluer des approches recourant à l’IA afin d’améliorer les résultats cliniques et l’expérience de soin des enfants, des jeunes et des familles, ainsi que pour étudier les applications pratiques responsables de l’IA en pédiatrie.
Le CAMH utilisera des crédits Claude dans diverses activités de recherche et d’éducation ainsi que dans des projets cliniques. Par exemple, le Centre Krembil de neuroinformatique du CAMH utilisera Claude pour mener des recherches informatiques en santé mentale, notamment en vue de développer et d’évaluer des modèles prédictifs pour le traitement des personnes souffrant de troubles mentaux, ainsi que pour réaliser des évaluations à grande échelle de l’équité des systèmes d’IA en psychiatrie. Ces crédits serviront également à renforcer l’impact du Centre éducatif mondial du CAMH, qui développe des programmes éducatifs multilingues sur la santé mentale assistés par l’intelligence artificielle.
L’Institut intelligence et données de l’Université Laval utilisera Claude pour approfondir la connaissance des chercheurs quant au comportement des grands modèles de langage (GML) dans divers contextes culturels, ainsi que leur connaissance des langues et dialectes disposant de moins de ressources, tels que le français québécois et les langues autochtones.
L’Université de la Saskatchewan utilisera Claude dans divers domaines de recherche, notamment la biomédecine, la sécurité alimentaire et l’approvisionnement en eau, la santé publique, l’informatique quantique et l’administration publique.
Le Data Sciences Institute de l’Université de Toronto utilisera des crédits API de Claude pour procéder à une revue scientifique visant à soutenir divers projets de recherche.

Outre ces dons, Anthropic intégrera cet été Amii, Mila et l’Institut Vector au Programme de jeunes pousses Anthropic, afin que les fondateurs qui travaillent avec Claude puissent accéder à une communauté et à des ressources capables d’appuyer leur croissance. Des centaines de jeunes entreprises canadiennes affiliées à ces institutions recevront chacune au moins 5 000 $ US sous forme de crédits API afin de poursuivre le développement de leurs activités.

« Certains fondements de l’IA moderne ont vu le jour à Toronto, Montréal et Edmonton, et c’est de là aussi, fait remarquable, que viennent bon nombre des chercheurs les plus engagés dans la sécurité de l’IA », a déclaré Chris Olah, cofondateur d’Anthropic. « J’ai grandi dans cette culture et je suis fier qu’Anthropic puisse contribuer à écrire un nouveau chapitre. »

Utilisation de Claude au Canada

Voici quelques données sur l’utilisation de Claude au Canada. Celles-ci sont tirées de l’édition de mars 2026 du rapport Anthropic Economic Index, qui fait régulièrement le point sur la manière dont l’IA est intégrée aux tâches professionnelles partout dans notre économie. Nos recherches économiques s’appuient sur des données issues de l’utilisation réelle de Claude, produites à l’aide d’un outil d’analyse respectueux de la vie privée qui mesure les tendances d’utilisation sans révéler l’identité des utilisateurs.

Le Canada occupe la huitième place mondiale en termes d’utilisation de Claude.ai. Par habitant, les Canadiens utilisent Claude plus de quatre fois plus que ce qui serait attendu considérant la taille de sa population. Parmi les dix pays où Claude est le plus utilisé, seuls les États-Unis le devancent sur ce point.

Part d’utilisation et taux d’adoption par habitant dans les dix pays qui utilisent le plus Claude.ai. Les barres indiquent la part de chaque pays (graphique de gauche) et l’indice d’utilisation de l’IA Anthropic (graphique de droite), calculés à partir d’un échantillon d’un million de conversations sur Claude.ai en février 2026. Le Canada est mis en évidence en bleu. L’indice d’utilisation de l’IA d’Anthropic (AUI) indique si l’utilisation de Claude est élevée ou faible dans un pays par rapport à sa population en âge de travailler. Le Canada représente 2,6 % de l’utilisation mondiale de Claude.ai, ce qui le place au huitième rang mondial, mais occupe le deuxième rang pour l’AUI. Sources : Anthropic Economic Index, février 2026; Banque mondiale.

Au Canada, l’adoption de Claude est reliée au type de travail de ses utilisateurs : la Colombie-Britannique arrive en tête en termes d’utilisation par habitant, suivie de près par l’Ontario, qui enregistre la plus grande part du nombre total de conversations. Dans ces deux provinces, le taux d’utilisation est supérieur à ce qui serait normalement attendu considérant la taille de la population. À l’échelle nationale, l’utilisation par habitant est plus élevée dans les provinces où se concentrent les emplois scientifiques, techniques et de services professionnels.

En y regardant de plus près, on constate que l’utilisation de Claude s’aligne sur les caractéristiques de l’économie locale. Les demandes de traduction sont plus fréquentes dans les provinces où les fonctionnaires sont plus nombreux, probablement parce que les lois sur le bilinguisme exigent que les services et les communications du gouvernement fédéral soient disponibles en anglais et en français. En fait, le Nouveau-Brunswick, la Nouvelle-Écosse et le Québec affichent à la fois les taux les plus élevés d’emploi dans la fonction publique et les plus grandes proportions de demandes d’aide à la traduction.

Perspective commune sur l’avenir de l’IA

Les pays qui investiront le plus dans l’IA de pointe au cours des prochaines années seront également ceux qui en définiront les règles. Nous sommes depuis longtemps convaincus que les démocraties doivent mener ces efforts et que le Canada peut y jouer un rôle de premier plan. Le gouvernement du Canada a publié en 2017 la première stratégie nationale au monde en matière d’IA, puis, en juin dernier, sa nouvelle stratégie intitulée « L’IA pour tous », qui prévoit renforcer les capacités de l’Institut canadien de la sécurité de l’IA, augmenter la littératie en IA et soutenir davantage les trois instituts nationaux qui sont à l’avant-garde de la recherche canadienne dans ce domaine depuis près d’une décennie.

La semaine dernière, nous avons publié une étude de cas sur le ministère de la Technologie et de l’Innovation du gouvernement de l’Alberta, où une équipe a utilisé Claude Code pour passer en revue 466 millions de lignes de code dans l’ensemble des systèmes provinciaux en environ 20 heures, puis a communiqué ses méthodes à d’autres gouvernements.

Les huit partenariats annoncés aujourd’hui ne sont qu’un commencement pour nos investissements dans la recherche canadienne. Nous sommes ravis à l’idée d’apporter notre soutien à ces travaux dans les instituts de recherche, les hôpitaux et les universités pour de nombreuses années encore.

UST is bringing Claude to physical AI

Wed, 08 Jul 2026 16:00:00 GMT

Before a factory commits to manufacturing millions of chips, engineers stress-test the design in the fab. Before a product ships, a fault on the assembly line has to be caught before it becomes a recall. When AI does this kind of work, it’s called physical AI: intelligence built into the equipment and engineering processes that produce the things people use.

We’re partnering with UST, a technology and engineering services company that builds and runs the engineering environments its clients depend on to get chips, cars, and connected devices to market. UST is putting Claude to work inside those environments, and training 20,000 of its engineers, architects, and consultants on Claude worldwide.

How UST is putting Claude into the production processes behind physical products

UST works alongside semiconductor, automotive, manufacturing, telecom, embedded, and IoT companies. It builds the systems those companies use to verify their designs, validate their chips, run their factories, and service their products once they’re out in the world.

These are long, multi-step processes where an early mistake gets more expensive with every step that follows. A design flaw caught during verification costs an engineer an afternoon; the same flaw caught after a factory has committed to manufacturing costs a production run.

UST is bringing Claude into this work. Claude Code reads the schematics and pinouts an engineer works from, then writes and runs the tests that check the design. It carries that task across many steps, holding the context of a design through hours-long tasks. UST is aiming to catch design flaws earlier, speed up chip validation, and bring hardware and software together in a single system.

The clearest example is a UST platform called iDEC, which its engineers use to validate hardware and silicon before it goes to production. Validation is the work of proving a chip actually behaves the way its designers intended, and it’s arduous: engineers write test scripts by hand, run them, read the results, and repeat the cycle many times over. UST reports that iDEC’s closed-loop pipeline,reads hardware designs, generates and runs regression tests, and compares live equipment data against its digital twin to flag issues early, already cuts validation cycle times by 50 to 70%, condensing standard four-day turnarounds into 48 hours.

UST is now integrating Claude into that pipeline as its reasoning layer. Claude Code reads chip pinouts and hardware schematics directly, then writes and runs regression tests—the checks that confirm a change to a design didn’t cause an unintended downstream effect—which engineers used to script by hand. Claude also compares the live data from real equipment against its digital twin—the software model of how that hardware is supposed to behave—and flags firmware regressions and signal-integrity faults. UST’s goal is to make its pipeline even faster, with less hand scripting and earlier fault detection, and no new tools for engineers to learn.

“Our alliance with Anthropic reflects UST’s unwavering commitment to helping clients navigate the AI landscape with confidence and achieve meaningful business outcomes. By combining the capabilities of Claude with UST’s engineering, industry knowledge, and delivery expertise, we are bringing to market industry-specific platforms and digital and engineering solutions that improve productivity, accelerate business outcomes, and help clients operationalize AI-led decisions in a safe and secure environment,” said Krishna Sudheendra, Chief Executive Officer, UST.

“UST helps the world’s banks, telecoms, and manufacturers put new technology to work. They’re proving Claude inside their own engineering first, and training 20,000 of their own people on it, before bringing it into the systems they build and run for clients,” said Paul Smith, Chief Commercial Officer, Anthropic.

How UST is putting Claude into healthcare, telecom, and banking systems

UST is bringing Claude into three other platforms it operates for clients:

In healthcare, insurers and providers use UST CarePath to run member services, care management, and claims. Claude connects CarePath directly to its underlying claims and care systems, and turns scattered health data into clear next steps for care teams. Every recommended action routes to a person for approval before it reaches a member, and it stays inside the data controls healthcare requires.
In telecom, UST IntelliOps runs network operations. To keep networks up and running, teams work through the alerts that spot problems and outages, but this is a time-consuming process. Claude now helps operators spot service issues, predict failures in the radio access network (the towers and antennas that connect phones to the network), and shorten outages through response workflows, which a person still approves. For the teams watching the alerts, that means less time separating real problems from noise.
In banking, most mid-sized institutions still run on core systems old enough that the ledger updates once a night rather than in real time. Banks often license these systems rather than own them, so every new product or integration means months of waiting for a vendor to make the change. UST FinX helps banks modernize progressively by solving immediate business and operational challenges while reducing the dependency on disruptive, high-risk transformation programs. FinX will use Claude to embed AI agents directly into bank workflows and processes, supporting both operations teams and customers through intelligent case handling, servicing automation, knowledge retrieval, workflow assistance, and decision support.

A partnership in AI adoption and training

UST is committing to train 20,000 of its associates on Claude worldwide, including engineers, architects, consultants, industry specialists, and forward-deployed engineers, who sit alongside client teams. It’s also building specialized teams to deploy Claude. We’re supporting UST in this effort with enablement, technical guidance, and certification through the Claude Partner Network. This partnership also makes UST a Global Premier Partner in the Claude Partner Network.

Pairing reliability, safety, and governance

The industries UST serves are extremely high-stakes. Human approval steps and audit controls are important forms of governance that make it possible for the systems to run in production. The reliability and safety we’ve prioritized in building Claude, paired with UST’s experience in governance and regulated delivery, enables this work to move out of a pilot and into the systems that run a business.

Inviting hard questions

Wed, 08 Jul 2026 16:00:00 GMT

Who decides the rules for AI?

Can AI give my children a better future?

Does AI make the world a more dangerous place?

Can AI help scientists cure diseases?

People have a lot of hard questions about AI. It’s our job to address them.

Many people are positively disposed to AI. They already use it every day, and they see its potential for making our work and our lives less laborious, for changing the way we learn, for helping speed up scientific and technological progress, for creating new sources of prosperity, and for solving some of the biggest medical and social problems we face.

But many also hold serious concerns. Some are worried about AI’s potential contribution to job loss. Some fear that it could devalue creative work. Others are concerned about human agency: that AI might affect our ability to think for ourselves, to make human connections, and to have meaning in our lives. Many are concerned about what it means if AI’s capabilities fall into the wrong hands—and wonder whether the benefits outweigh the costs.

In the film below, you can hear some of these hopes and concerns from people we’ve spoken with.

Anthropic is a Public Benefit Corporation—it’s our mission to secure the benefits of advanced AI models and mitigate their risks. That public benefit mission has led us, for example, to invest in AI safeguards to reduce the risk of misuse, to research the behavior and inner workings of AI models to help us align them to beneficial goals, to give our AI models for free to scientists, and to launch a fellowship program that pairs early-career Claude users with nonprofits to help extend the benefits of AI.

To truly act in the public interest, we need to fully understand the hopes and concerns the public has about this new technology. As part of our new initiative on hard questions, we’re taking further steps to understand people’s views and questions on a variety of AI-related topics, and transparently chart our progress toward our public benefit goals.

We’ve already been laying the groundwork, asking thousands of people for their views on AI:

We’ve begun the Anthropic Public Record—a public survey that, in its first round, asked 52,000 Americans to set out their biggest hopes and concerns about this technology;
We surveyed 81,000 Claude users across 159 countries and 70 languages through our Anthropic Interviewer;
We conducted dozens of in-person focus groups and convened sessions with groups whose work and traditions bear on the questions raised by AI; and
We’ve been studying the use of Claude through anonymized, real-world data.

We created the Anthropic Institute—a research effort within the company with the goal of confronting the most significant challenges that AI will pose to society—and since early in our company’s history our Long-Term Benefit Trust has provided impartial oversight on how effectively we’re advancing our public benefit mission.

Now, we’re explicitly asking you to send us your hardest questions on AI: questions about AI’s effects on jobs, society, and families; questions about how we achieve the dramatic potential of AI for science and medicine; questions about where one of the most powerful technologies in human history can take us next.

In return, we’ll publicly track and report the specific actions we’re taking to address those questions—and we’ll be clear about the ways in which we might fall short of our stated goals.

To see some of the questions others are asking, and to share your own, visit our hard questions website.

Ben Bernanke appointed to Anthropic’s Long-Term Benefit Trust

Wed, 08 Jul 2026 16:00:00 GMT

Anthropic's Long-Term Benefit Trust (LTBT) has appointed Dr. Ben Bernanke, a Distinguished Fellow at the Brookings Institution and former Chair of the Federal Reserve, as its newest member. He joins an independent body that works to hold Anthropic to its mission: the responsible development of advanced AI for the long-term benefit of humanity.

Bernanke led the Federal Reserve from 2006 to 2014, steering the central bank through the 2008 global financial crisis and the recovery that followed. Before entering government, he spent more than two decades as an academic economist, much of that time at Princeton, where he chaired the economics department and built a body of research on the Great Depression and the role banks play in financial crises. That work earned him the Nobel Prize in Economic Sciences in 2022.

“The potential of artificial intelligence is enormous, and so is the range of outcomes. How that potential plays out will depend, in part, on the institutions we build around it,” said Dr. Bernanke. “Anthropic has created a unique governance structure to try to ensure that the long-run benefits of AI for humanity far outweigh the risks. I am honored to have this opportunity, and I will try to contribute in any way I can to this critical mission.”

“AI may have the most significant economic effects of any technology in modern history, and Anthropic has a dual responsibility to understand those effects and to act on them,” said Daniela Amodei, Co-Founder and President of Anthropic. “Ben’s career has run from studying how economies react to disruptive moments to helping steer the world’s largest economy through one such time. His judgment will make us better at anticipating and responding to how advanced AI affects workforces and economies around the world.”

Anthropic is a Public Benefit Corporation, meaning the company was created to balance commercial success with generating social and public good. The LTBT exists to help the company responsibly maintain that balance over the long term, providing a check on how Anthropic develops and deploys AI. The Trustees are chosen for their expertise across a wide range of areas, and are independent of the company’s management and investors: they hold no equity in Anthropic, don’t share in its profits, and are compensated only for their time and service. New Trustees are selected by the existing ones, in consultation with the company.

“The institutions built around this technology will matter as much as the technology itself,” added Neil Buddy Shah, Chair of the LTBT. “Ben spent eight years leading the Federal Reserve and guided the country through the worst financial crisis in nearly a century with expertise, independence, and sound judgment. That's the standard we look for in Trustees as AI's impact grows, and we're fortunate Ben has chosen to join us in this work.”

The LTBT has the authority to appoint members to Anthropic’s board. The Trustees advise the board and Anthropic’s leadership on critical decisions, particularly those involving the potential risks and societal impacts of AI. Bernanke’s appointment brings expertise to one of the questions Anthropic studies most closely: how AI is changing the economy. He will lend his insight to our economic research, in addition to other areas of Anthropic's work.

Bernanke joins Neil Buddy Shah, Richard Fontaine, and Mariano-Florentino Cuéllar on the LTBT. Their collective experience spans global health, national security, law, policy, and economics.

Introducing a way to reflect on how you use Claude

Wed, 08 Jul 2026 16:00:00 GMT

Today we're introducing, in beta, a new way to reflect on and refine how you use Claude. In our interviews with users, a common theme that’s emerged is a desire to better understand how, exactly, AI can be integrated into daily life. How often should someone use AI? How can it be used most effectively? When is AI suited to a task, and when is it better left to a human?

We built this feature to help answer these types of questions. It lets you easily track and visualize how you use Claude, and decide whether that time aligns with your goals. Your reflection dashboard can be found in Settings on Claude for web or the desktop app.

See your patterns and shape them

Your reflection starts with a summary of how you've been using Claude, covering key topics, your usage patterns, and the types of tasks you often work through.

You can look back on your Claude chat activity over the past 1, 3, 6, or 12 months.

The reflect feature provides a breakdown of when you use Claude most, and what you spent that time working on. Soon, we'll add a view of how much time you've spent using Claude.

Your reflection also invites you to step back and examine the role Claude plays in your life. It will periodically surface questions like, "What's one thing you want to keep doing yourself, even if Claude could do it faster?" and give you the chance to talk it through with Claude.

Within your dashboard, you can also set quiet hours or schedule a nudge to take a break from using Claude after a certain amount of time. Both are reminders of your own preferences and can be dismissed.

Build AI skills that support your original thinking

You can use your reflection to build new skills and sharpen how you work with Claude, using the 4D AI Fluency Framework.

Delegation: Setting goals and deciding whether and how to engage with AI
Description: Effectively describing goals to prompt useful AI behaviors and outputs
Discernment: Accurately assessing the usefulness of AI outputs and behaviors
Diligence: Taking responsibility for what we do with AI and how we do it

Your report gives you a summary of your Claude activity across each dimension, with examples of how you tend to collaborate with Claude, like noting that you often rework email drafts in your own voice, or delegate tasks only after settling the strategy yourself. Your reflection will also offer practical suggestions, like starting a Project instead of needing to re-explain the context of ongoing work.

Privacy and sensitive topics

Your reflection doesn’t draw from incognito chats, nor does it pull in underlying files from your connected tools. For example, if you asked Claude to summarize your inbox, that summary may come up in your reflection, but the source emails would not. And any conversation connected to a health integration tool is left out of your insights entirely. The information and insights in your reflection stay there; they aren’t used for any other purpose. You can read more about our privacy practices here.

We built this tool to be a reflection of how you use Claude. For some users, this may include sensitive or more personal conversations. In developing this tool and our approach to these topics, we worked with digital media and wellbeing experts from the MIT Media Lab's Advancing Humans with AI (AHA) program, the Digital Wellness Lab at Boston Children’s Hospital, and the Family Online Safety Institute. Sensitive conversations can still appear as part of your reflection, but only at a high level.

Getting started

This tool is currently available in beta for Free, Pro, and Max users who have memory turned on. Open Settings in Claude on the web or the desktop app, and select the option to reflect on your usage to generate your report. If you can't generate a report, it may be because you don’t have Memory turned on. Reflecting on your Cowork conversations will be available soon.

Anthropic News

A research agenda for the Economic Futures Research Fund

What is the fund?

The kinds of research we’re funding

Our five research priorities

1. Shaping AI’s impact on workers at the firm and workplace level

2. Equipping people to navigate AI-driven transitions

3. Modernizing income support for AI-driven displacement

4. Building worker stakes in AI-driven growth before disruption arrives

5. Generating new evidence on public investments

Ask Claude about the Anthropic Economic Index

Anthropic is donating another $20 million to Public First Action

Why now?

Policy that meets the moment

What comes next?

Apply for Anthropic’s AI for Science rare disease research grants

Track one: Scaling our basic science partnerships

Track two: Scaling our biotech partnerships

Next steps

Introducing Claude for Teachers

Why we’re building for teachers

Connected to evidence-based curricula and the K-12 ecosystem

How it works

Built for educators, safe with student data

AI fluency for teachers

Sharing what we build

Getting started

Anthropic commits $10 million to Canadian AI research

Investing in Canadian research

How Canada is using Claude

A shared view on the future of AI

Anthropic s’engage à verser 10 millions de dollars à la recherche canadienne en IA

Notre investissement dans la recherche canadienne

Utilisation de Claude au Canada

Perspective commune sur l’avenir de l’IA

UST is bringing Claude to physical AI

How UST is putting Claude into the production processes behind physical products

How UST is putting Claude into healthcare, telecom, and banking systems

A partnership in AI adoption and training

Pairing reliability, safety, and governance

Inviting hard questions

Ben Bernanke appointed to Anthropic’s Long-Term Benefit Trust

Introducing a way to reflect on how you use Claude

See your patterns and shape them

Build AI skills that support your original thinking

Privacy and sensitive topics

Getting started