Anthropic, PBC: DRS filing (amended)

Cover page 2 changes

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As confidentially submitted to the Securities and Exchange Commission on September 30, October 9, 2026.

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See the section titled Risk Factors beginning on page 25 26 to read about factors you should consider before buying shares of our Class A common stock.

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Select defined terms 1 change

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compute multipliers refer to algorithmic and methodological improvements in training and inference that increase the effective output of a given amount of compute.

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Prospectus summary 8 changes

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3 IQVIA Institute for Human Data Science, Global Medicine Use Trends 2026, February 2026.

3 4 IDC, Worldwide Developer Population Growth Forecast, 2025 2029, doc # US53789825, September 2025.

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Ultimately, we believe frontier AI will help drive global economic growth. As frontier AI makes workers and organizations more productive, becomes embedded in the products and services they deliver, and unlocks new business models, it creates economic output that did not previously exist. We believe our opportunity is to capture a share not only of today s economic activity but also of that newly created output as the global economy expands.

Frontier AI May Reshape Entire Sectors and Accelerate Novel Discoveries We believe frontier AI represents a larger opportunity than any prior technology cycle, and Claude is already demonstrating this potential. Claude can now execute complex projects autonomously and turn open-ended tasks into finished, high-quality deliverables. These capabilities enable it to transform the largest sectors of the economy, driving meaningful gains in productivity and output. Frontier AI s capabilities may extend beyond current markets to open entirely new ones, reaching domains such as robotics, advanced manufacturing, and logistics, and accelerating progress in scientific research, including drug discovery and materials science. For example, in the life sciences sector, where annual global pharmaceutical spend is expected to exceed $2 trillion in 2026, 3 Claude is used in drug discovery and development by our customers, our biotechnology partners, and our own researchers. We are also extending Claude into robotics, where our Model Hardware Standard ( MHS ) enables AI agents to operate lab and manufacturing equipment such as microscopes, liquid handlers, and off-the-shelf robotic arms. Ultimately, we believe frontier AI will help drive global economic growth. As frontier AI makes workers and organizations more productive, becomes embedded in the products and services they deliver, and unlocks new business models, it creates economic output that did not previously exist. We believe our opportunity is to capture a share not only of today s economic activity but also of that newly created output as the global economy expands.

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We believe we have reached an inflection point where AI systems can function as genuine collaborators in professional work. They quickly complete substantive tasks that might otherwise occupy a skilled professional for several hours, a full day, or longer. AI systems are also becoming increasingly capable of performing tasks that were previously too difficult or costly to complete or surfacing original insights and analyses. We refer to these AI systems that work alongside humans to perform complex, multi-step work at professional levels as Virtual Collaborators. Virtual Collaborators carry the access rights, system connections, and context needed to function more accurately and reliably. They can navigate and use internal tools and execute workflows that span multiple steps that require sustained reasoning. Virtual Collaborators are AI designed to be embedded where work gets done, and our aim is to continue extending them to knowledge workers globally, a population that is estimated to exceed one billion in 2028. 3 4 Claude Code. The clearest manifestation of Virtual Collaborators today is Claude Code, our coding agent and the product we believe has most concretely defined what autonomous AI work looks like. Claude Code shifted the market s understanding of what an AI system could do alongside a software developer it not only suggests the next line of code, but also plans, writes, tests, and debugs all the way to production-grade code that is ready to ship. Within months of its release, autonomous coding became the reference point for measuring the progress of frontier AI. For enterprise AI use cases, we believe the launch of Claude Code was comparable in its impact to the introduction of the first widely adopted conversational AI assistants. What makes Claude Code a Virtual Collaborator is its sustained reasoning, a harness that translates intent into actions, a sandboxed virtual environment that maintains a persistent state, integration with existing tools and systems, and a user-friendly interface. Its uses are highly varied and often surprising for example, NASA engineers used Claude Code to plan, write, and execute navigation commands for the Mars Perseverance rover, resulting in the first AI- planned drive on Mars.

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Diffusion also accelerates as our models become more capable and efficient, allowing us to lower the cost per task for customers. For example, Claude Opus 5.5 reduced customer costs per task by approximately 40% compared to Claude Opus 5. Lower costs drive greater usage, which in turn drives broader diffusion of Claude. Our compute efficiency allows us to deliver these reductions while preserving our margins.

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Claude itself has become one of the most powerful drivers of our progress each new version of a Claude model directly accelerates development of the next. Many aspects of the research, training, and testing of subsequent model generations now involve Claude, including running experiments, generating and processing training data, automating evaluations and safety audits, and writing and reviewing code. We refer to this recursive dynamic as closing the loop. A direct expression of this dynamic is our accelerating pace of producing compute multipliers. algorithmic and methodological improvements in training and inference that increase the effective output of a given amount of compute. More capable models help our researchers discover these improvements faster in turn, more powerful compute multipliers enable us to train even more capable models and deploy them more efficiently.

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Enter New Markets . We intend to extend frontier AI to accelerate novel discoveries, reaching domains from healthcare and life sciences to robotics and physical work, where we believe compounding model capabilities can unlock transformative opportunities.

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Risk factors 9 changes

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For more information, see the sections titled We have incurred significant indebtedness in order to operate and grow our business, including to fund our substantial compute commitments, and our ability to service this indebtedness may be constrained by our cash flows and liquidity and We may not have the ability to raise the funds necessary to repay the principal and accrued interest of our outstanding convertible promissory notes or the A&R Credit Agreement in cash when required by the terms of the notes or the A&R Credit Agreement, as applicable, or at their maturity.

Our ability to meet the substantial and growing financial obligations of these requirements to secure compute depends on our ability to generate significant revenue growth. We must grow our customer base and expand revenue rapidly in order to generate the cash flows necessary to satisfy these commitments. However, our revenue is primarily consumption-based, and much of it is not subject to long-term contractual commitments, while our compute and infrastructure commitments generally extend over multiple years. As a result, we may enter into these obligations based on revenue expectations that may not be realized. If our revenue growth does not keep pace with our compute-related payment obligations, if we are unable to successfully convert our compute investment into revenue-generating customer workloads, if we fail to raise additional capital on acceptable terms, or if we experience other adverse developments affecting our liquidity, we may be unable to satisfy these obligations. Any such failure could also result in defaults under our agreements, loss of access to critical resources, and reputational harm. In particular, a default under certain commercial agreements could trigger cross-default provisions under the A&R Credit Agreement (as defined below), and certain of our outstanding convertible promissory notes, potentially resulting in the acceleration of substantially all of our outstanding, and any future, indebtedness. Such cross-defaults could set off a cascading series of adverse consequences, including the exercise of remedies by multiple creditors, the termination of key commercial arrangements, enforcement actions against our assets, and severe liquidity constraints. An acceleration of our indebtedness, combined with the loss of access to critical data center infrastructure, among other things, could impair our ability to train and deploy models, serve customers, and fund ongoing operations, which could have an adverse effect on our business, financial condition, and results of operations. For more information, see the sections titled We have incurred significant indebtedness in order to operate and grow our business, including to fund our substantial compute commitments, and our ability to service this indebtedness may be constrained by our cash flows and liquidity and We may not have the ability to raise the funds necessary to repay the principal and accrued interest of our outstanding convertible promissory notes or the A&R Credit Agreement in cash when required by the terms of the notes or the A&R Credit Agreement, as applicable, or at their maturity.

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Our public positions, stated values, policy advocacy, or statements by our executives have attracted, and may continue to attract, criticism from influential political figures, industry participants, regulators, or other third parties, particularly in politically sensitive or polarized environments, which could shape public narratives or regulatory perceptions of our business in ways that are unfavorable to us. For example, public statements by us or other industry participants regarding the safety of frontier AI development have been cited by governmental actors to support litigation or regulatory claims, including requests for injunctive relief restricting model development, and could be cited in future proceedings involving us. Such scrutiny or backlash could result in reputational harm, increased compliance or operating costs, investigations, enforcement actions, or restrictions on our operations, any of which could adversely affect our business, financial condition, and results of operations.

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For example, in September 2025, we detected and assessed with high confidence that a Chinese state-sponsored hacking group manipulated our Claude Code tool into attempting infiltration into roughly 30 global targets, including large technology companies, financial institutions, chemical manufacturing companies, and government agencies, resulting in a limited number of successful intrusions. We believe this is the first documented case of a large-scale cyberattack executed without substantial human intervention. We expect that malicious actors, including sophisticated state-sponsored actors, will continue to seek to leverage our models, platform, and applications for harmful actions, including by circumventing our safeguards, and their efforts to do so may increase as the capabilities of our technology increase. Any successful attempt to misuse, or any perceived misuse of, our models, platform, and applications for a cyberattack or for any other malicious purpose could result in exposure to liability, disruption of our customer or other commercial relationships, or damage to our reputation. The risks of such harms may increase as the cybersecurity-related capabilities of our models, platform, and applications increase. Independent security researchers and other third parties have also used, and may in the future use, our models to identify and exploit vulnerabilities in the systems of other companies, including other AI developers, at times in connection with authorized bug bounty programs. While such activity may be lawful and consistent with industry practice for vulnerability research, it illustrates that our models can function as a general-purpose tool for gaining unauthorized access to third-party systems, and comparable capabilities could be used by malicious actors without authorization. Such incidents, whether or not we are a direct party to them, may draw public and regulatory attention to the offensive cybersecurity capabilities of our models and could result in reputational harm, calls for additional restrictions on our models, or claims that we failed to adequately safeguard against such uses. For example, in September 2026, media reports citing a senior Federal Trade Commission ( FTC ) official indicated that the FTC has opened an industry-wide, non-public investigation into the potential risks that increasingly autonomous AI systems pose to consumers, reportedly focused in part on an incident in which AI agents developed by another company in our industry were used to gain unauthorized access to a third-party platform, and reportedly intends to issue civil investigative demands and seek testimony from executives at several AI developers, including us. For more information regarding this investigation, see the section titled If we are unable to manage the risks related to our focus on and commitment to AI safety, our brand, business, financial condition, and results of operations could be adversely affected. Additionally, state attorneys general in California and Florida have also announced investigations of another company in our industry regarding AI safety. For more information about risks related to security incidents, see the section titled Any security breaches or other incidents, including those that affect our networks, systems, or data, or the networks, systems, or data of our service providers, suppliers, or customers, could adversely affect our business, financial condition, and results of operations.

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Even isolated or simulated instances of such behavior, whether occurring in testing or real-world deployments, and whether occurring with our models or models developed by others, could generate significant public concern, regulatory scrutiny, litigation risk, or reputational harm, regardless of whether such behavior results in actual harm. For example, state attorneys general in California and Florida have announced investigations of another company in our industry regarding AI safety. Concerns have been raised both within our organization and by other researchers, policymakers, and industry participants that sufficiently advanced AI systems could, if not developed, deployed, and operated with appropriate safeguards, oversight, and security measures, pose catastrophic or existential risks to humanity, particularly if such systems were misused, replicated, weaponized, or allowed to operate with a high degree of autonomy. We have observed that our models are contributing to the pace of our own research and these risks will increase as AI systems substantially automate AI research and development if, for example, they accelerate AI progress beyond our ability to evaluate and safeguard new models, which could adversely affect our business, financial condition, and results of operations.

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Similar incidents involving our models have presented and could in the future present substantial risk to our reputation, expose us to legal, regulatory, and other proceedings, and adversely affect our business, financial condition, and results of operations. While our current Consumer Terms of Service requires our users to be at least 18 years of age, minors have accessed in the past, and may in the future access, the platform, and risks relating to child safety may be particularly acute, as minors may be more susceptible to harmful content, manipulation, or overreliance on AI outputs, and incidents involving children tend to draw heightened regulatory, media, and public scrutiny. State attorneys general and private plaintiffs have also begun to assert tort, product liability, consumer protection, and failure-to-warn claims against companies in our industry, alleging that AI models caused or contributed to harm and that the developer failed to adequately warn users of known risks. Regulators have obtained substantial monetary settlements and injunctive relief against other technology companies based on analogous theories of product-design or algorithmic harm, and we believe similar theories are likely to continue to be tested against us and other companies in our industry as adoption of our models grows, including by minors and other vulnerable users, notwithstanding our efforts to restrict such access. The relief sought in such matters has, in at least one instance, extended beyond data-handling, disclosure, or age-verification practices to include a request to enjoin an AI company from developing new models absent independent, third-party safety review and approval, illustrating the potential breadth of remedies governmental actors may pursue against companies in our industry. Any determination that we are liable under such theories, or any judicial or regulatory expansion of tort, product liability, or consumer protection law as applied to AI models, could result in significant liability and require us to change how we design and offer our models, platform, and applications.

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Balancing Safety and Commercial Objectives . Our commitment to AI safety requires us to make difficult judgments that balance safety considerations against commercial objectives, competitive pressures, and stakeholder expectations. We often make judgment calls with respect to when we determine a model is safe enough to release and which capabilities to develop or restrict. For example, in April 2026, we initially released our frontier model, Mythos, as a gated research preview through Project Glasswing, available to select organizations for cybersecurity applications, rather than making it broadly available at launch due to its advanced capabilities in cyber vulnerability discovery. We expect that frontier models with similar or enhanced capability profiles, whether developed by us or by other AI labs, will continue to emerge, and that future Anthropic models exhibiting such capabilities may be subject to access constraints. There can be no assurance, however, that the safeguards or access regimes we adopt will prove adequate, that we will correctly identify which capabilities require restriction, or that constraints we impose will not be circumvented. These judgments are particularly consequential because, as AI systems become more capable, the potential severity of harm from misaligned or misused models increases correspondingly, and a wide range of stakeholders, including researchers and policymakers, have raised concerns that sufficiently advanced AI systems could pose catastrophic or existential risks to society if not developed and deployed responsibly. Our safety-focused approach is designed in part to mitigate such risks, but there can be no assurance that our efforts will be successful or that the broader AI industry will adopt similar safeguards. In particular, as AI systems become more autonomous and capable of taking consequential actions with less human oversight, there is a risk that such systems could, whether due to misalignment with human intentions, errors in objective specification, or self-preserving behaviors, take harmful actions independently and at scale without human intervention. Even if our own models do not cause such harm, a significant safety incident involving any frontier AI could trigger broad reputational damage to the AI industry, public backlash, and sweeping regulatory responses that could adversely affect our business. For example, in September 2026, media reports citing a senior FTC official indicated that the FTC has opened an industry-wide, non-public investigation into the potential risks that increasingly autonomous AI systems pose to consumers, reportedly prompted in part by an incident in which another company s AI agents gained unauthorized access to a third-party platform, and reportedly examining conduct by us and other developers of frontier AI models. According to these reports, the FTC intends to issue civil investigative demands and may seek testimony from executives at the companies under investigation. We may receive a civil investigative demand or other formal request for information in connection with this reported investigation. The scope of any investigation of us or its outcome, and any investigation, enforcement action, or adverse finding arising from this or similar inquiries could result in significant costs, require changes to how we design, test, or deploy our models, platform, and applications, or otherwise adversely affect our business, financial condition, and results of operations. We may misjudge these trade-offs, either by being overly cautious and potentially losing market opportunities, or by being insufficiently rigorous and releasing models, platforms, or applications that cause harm. Even where human oversight mechanisms are in place, such mechanisms may prove insufficient in practice if the volume, speed, or complexity of autonomous agent actions exceeds the capacity of human reviewers to meaningfully evaluate and intervene, and any perceived failure of our oversight mechanisms could expose us to regulatory enforcement, liability claims, and reputational harm. For more information, see the section titled If the use of our AI models results in harm, whether through use by malicious actors or misuse by other third parties, or if our model guardrails or safeguards fail to prevent such misuse, our reputation and brand, business, financial condition, and results of operations could be adversely affected.

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As a result of these and other regulatory developments, our business could be subject to a wide range of regulatory limitations, obligations, or restrictions, including, among other things limitations, conditions, or prohibitions on certain AI use cases, applications, or deployment contexts requirements to modify, restrict, delay, or withdraw specific models, features, or functionalities in particular markets or for specific customers for example, in early 2026, the U.S. Department of War suggested it could invoke the Defense Production Act to compel us to remove the limitations we have placed on the use of our models for autonomous weapons and mass surveillance heightened transparency, documentation, auditing, governance, or human-oversight obligations restrictions on the collection, use, transfer, localization, or retention of data used to train or operate AI systems, based on intellectual property, privacy, or other frameworks mandated safety, risk-management, or evaluation frameworks, certifications, or approvals, including requirements for independent, third-party review or approval prior to model development, training, or release for example, in September 2026, a state attorney general asked a court, in pending litigation alleging harm to minors, to enjoin an AI company from developing new AI models without independent, third-party guardrails and approval restrictions on data center development or expansion, including permitting, zoning, or limits on energy or water usage heightened energy efficiency, sustainability, or environmental compliance requirements limitations on cross-border deployment, access, or commercialization of AI models, including through export controls, national security determinations, or other governmental measures that could restrict our ability to sell our models, platform, and applications in certain markets or to certain customers for example, in June 2026, the U.S. Department of Commerce imposed worldwide export restrictions on our Fable 5 and Mythos 5 models, which were subsequently lifted after we updated our safeguards, and other models may be subject to these or similar export restrictions in the future for more information, see the section titled Governmental trade controls, including export and import controls, sanctions, customs requirements, and related regimes, could subject us to liability or loss of contracting privileges or limit our ability to compete in certain markets.

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Antitrust litigation can be complex, protracted, disruptive, and expensive, and an adverse outcome could result in treble damages and injunctive relief. We could also become subject to additional private litigation or to investigations or enforcement actions by the U.S. Department of Justice, the Federal Trade Commission, FTC, state attorneys general, or non-U.S. competition authorities. We have proposed public efforts to work with the U.S. government and other developers of frontier AI models to establish safety standards, including with respect to the pace of capability development, and have suggested that such collaboration may warrant a narrow exemption or safe harbor from antitrust laws. These proposals have drawn public opposition from political leaders, some of whom have characterized such efforts as anticompetitive regardless of the purpose of such exemption or safe harbor. There is no assurance that any such exemption or safe harbor will be adopted. Our advocacy for such an exemption, and any safety commitments we pursue with other developers of frontier AI in its absence, could subject us to additional regulatory, litigation, and other risks. These risks may cause us to limit or modify our public advocacy, our participation in industry safety and standard-setting initiatives, and our communications with other developers of frontier AI, which could adversely affect our ability to pursue collaborative approaches to AI safety that we believe are important to our mission.

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Industry, market, and other data 2 changes

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The sources of the statistical data, estimates, and market and industry data contained in this prospectus are identified by superscript notations and are provided below International Data Corporation, IDC, The Economic Impact of AI 2026 Edition, doc # US54452226, April 2026.

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IQVIA Institute for Human Data Science, Global Medicine Use Trends 2026, February 2026.

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Capitalization 1 change

The following table sets forth our cash, cash equivalents, and short-term investments, as well as our capitalization, as of June 30, 2026 as follows on an actual basis on a pro forma basis, giving effect to (i) the Preferred Stock Conversion, as if such conversion had occurred on June 30, 2026, (ii) the Convertible Notes Conversion, as if such conversion had occurred on June 30, 2026, (iii) the Class F Stock Issuance, as if it had occurred on June 30, 2026, (iv) an increase to additional paid-in capital and accumulated deficit related to stock-based compensation of $ related to RSUs subject to the RSU Settlement, and (v) the filing and effectiveness of our amended and restated certificate of incorporation in Delaware that will become effective immediately prior to the completion of this offering and on a pro forma as adjusted basis, giving effect to t o (i) the pro forma adjustments set forth above, (ii) the sale and issuance by us of shares of our Class A common stock in this offering, based upon the assumed initial public offering price of $ per share, which is the midpoint of the estimated offering price range set forth on the cover page of this prospectus, and after deducting estimated underwriting discounts and commissions and estimated offering expenses payable by us, and (iii) the use of net proceeds from this offering, together with existing cash and cash equivalents, if necessary, to satisfy the estimated tax withholding and remittance obligations related to the RSU Settlement. We will not receive any proceeds from the sale of shares of our Class A common stock by any of the selling stockholders.

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MD&A 6 changes

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Multi-Chip Compute Approach . We train and serve Claude natively across four major chip families (AWS Trainium, Google TPUs, NVIDIA GPUs, and AMD GPUs) using a proprietary orchestration layer that enables us to optimize workloads for price and performance. This flexibility, which took years and significant investment to build and we believe is difficult to replicate at scale, expands our available compute, reduces dependence on any si ngle vendor, and enables us to continuously route workloads to the most optimal hardware for those workloads . This foundation, paired with Claude s increasing understanding of chip and model architectures, allows us to onboard new chip platforms in a fraction of the time this once required.

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As our compute needs have grown substantially, we are shifting from sourcing compute solely through fixed- term cloud agreements to a hybrid model that also includes leasing chips directly and hosting them in dedicated facilities operated on our behalf. We refer to this additional source of compute as Anthropic Direct, which helps to address our scaling compute requirements and provides potential long-term economic advantages of direct hosting. Anthropic Direct allows us to separate our commitments for data center capacity from our commitments for the AI accelerators deployed inside them. Because data centers require longer lead times but represent a smaller fraction of total costs, this approach enables us to secure physical capacity early while deferring larger accelerator investments until our specific compute needs are more certain.

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4 For purposes of this measure, we include only Fortune 500 customers that are publicly traded and calculate the measure using total expenses as reported in their publicly available financial statements.

In the months ended , we launched an aggregate of new model versions, platform features, and applications, including new capabilities in Claude Code, Claude Cowork, and vertical solutions for key industries. Our growth depends on continuing this pace of product innovation and reducing the friction between Claude s capabilities and customer workflows. Our growth also depends on how quickly new capabilities reach customers, which in turn depends on the steps we take to assess and mitigate the risks of each model during development and before and after release. In the past, these steps have included delaying the release of new models and limiting their initial availability, such as our initial release of Mythos through Project Glasswing, and in the future, as we learn more about model capabilities and behavior, could include additional measures that affect whether, how, and when we develop or release new models or capabilities. These measures, and any laws, regulations, or standards governing frontier AI development that may be adopted, could cause new use cases to emerge more slowly and slow the enterprise adoption and expansion on which our revenue growth depends. 5 For purposes of this measure, we believe that enterprises prefer models include only Fortune 500 customers that are safe and aligned, publicly traded and that our continued investment calculate the measure using total expenses as reported in safety strengthens customer trust and supports long-term adoption. their publicly available financial statements.

measures, and any laws, regulations, or standards governing frontier AI development that may be adopted, could cause new use cases to emerge more slowly and slow the enterprise adoption and expansion on which our revenue growth depends. We believe that enterprises prefer models that are safe and aligned, and that our continued investment in safety strengthens customer trust and supports long-term adoption.

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We compare this measure against our largest Fortune 500 customers, who are further along in their adoption of AI. For the three months ended , revenue from our top 10 Fortune 500 customers represented, on average, less than % of their total expenses still a small fraction of their overall spend, but times the average of our Fortune 500 customers with $100,000 or more of revenue on a TTM basis. We believe the difference reflects the substantial runway we have to deepen adoption, both within our largest customers and within customers earlier in their adoption of Claude. 4 5 Customer Usage Patterns Our revenue is influenced by customer usage patterns, which are impacted by the frequency and capability of new model version releases, as well as seasonal trends, each of which can create variability in our results from period to period. The continuous and overlapping cadence of new model version releases is inherent to remaining at the frontier of AI development. For example, in the months ended , we released new model versions, which accounted for % of our revenue for the months ended . As we have released successive versions of Claude models, we have continued to see revenue growth as new capabilities unlock additional use cases, prompting new customers to adopt Claude and existing customers to expand their usage.

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Business 46 changes

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% of the contributions to our codebase in were written by Claude . These productivity gains are accompanied by a steady improvement in Claude s capability on more challenging, open-ended engineering problems that lack a clear specification and have historically depended on experienced human judgment.

Claude s success rate on these problems reached an average of % in , up percentage points in months, based on our internal benchmarks.

Claude itself has become one of the most powerful drivers of our progress each new version of a Claude model directly accelerates development of the next. Many aspects of the research, training, and testing of subsequent model generations now involve Claude, including running experiments, generating and processing training data, automating evaluations and safety audits, and writing and reviewing code. We refer to this recursive dynamic as closing the loop. A direct expression of this dynamic is our accelerating pace of producing compute multipliers. algorithmic and methodological improvements in training and inference that increase the effective output of a given amount of compute. More capable models help our researchers discover these improvements faster in turn, more powerful compute multipliers enable us to train even more capable models and deploy them more efficiently.

A clear example of our ability to close the loop is the deployment of Claude Code, our agentic coding product, across our engineering organization. As our models have advanced, we have seen significant improvement in Claude s capability on more challenging, open-ended engineering problems that lack a clear specification and have historically depended on experienced human judgment. Claude s success rate on these problems reached an average of % in , up percentage points in months, based on our internal benchmarks.

A clear example of our ability to close the loop is the deployment of Claude Code our agentic coding product, across our engineering organization. As our models have advanced, Claude Code has progressed from suggesting code to writing and running it directly and working autonomously over long time horizons on complex, multi-step engineering tasks. with This has enabled our engineers to increasingly directing direct and reviewing review its work rather than writing write code line by line. As a result, the average daily volume of code contributed to our codebase per engineer in the months ended was times the average daily volume in the ended . % of the contributions to our codebase in were written by Claude .

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Frontier AI is transforming cognitive work the application of knowledge, reasoning, and judgment to produce economic value. We believe it will make humans far more capable and expand the world s overall capacity for 5 that work, including accelerating scientific discovery and helping businesses grow. Knowledge workers are the 6 See the section titled Industry, Market, and Other Data for a description of how we calculate annual economic value attributable to knowledge workers globally.

6 7 IDC, The Economic Impact of AI 2026 Edition, doc # US54452226, April 2026. Reflects the annual value created across AI solutions and services, investments in the AI infrastructure supply chain, and enhancements in enterprise productivity and efficiency unlocked by AI.

7 8 IDC, Worldwide Black Book Live Edition V2 2026 Forecast, July 2026.

9 IQVIA Institute for Human Data Science, Global Medicine Use Trends 2026, February 2026.

that work, including accelerating scientific discovery and helping businesses grow. Knowledge workers are the leading edge of this opportunity. As frontier AI advances, more capable models can unlock entirely new use cases while also efficiently serving a wide range of cognitive work at scale across the capability-cost-speed curve. We believe the work that knowledge workers perform represented more than $40 trillion in economic value globally in 2025, 5 6 and with frontier AI s potential to make them meaningfully more productive, our near- term opportunity is to expand this value and accelerate economic growth. While it is still early to forecast frontier AI s full impact across the economy, IDC estimates that investment in AI solutions and services will yield a cumulative global economic impact of $22.5 trillion by 2031 . 6 7 We believe the impact could be even greater.

History suggests that transformative technologies expand the markets they enter. For example, information technology spend grew from approximately $1.2 trillion in 2006 to $4.3 trillion in 2025, a CAGR of approximately 7%, as cloud computing spend grew from zero to $1.9 trillion over the same period and expanded information technology budgets. 7 8 We have seen other technologies, including the personal computer, the internet, and the smartphone, grow their markets as well. The companies that built these technologies grew at a far faster rate than the economy as a whole. Global GDP grew from approximately $34 trillion in 2000 to more than $118 trillion in 2025 according to the World Bank, more than tripling. Over that same period, the ten largest publicly traded technology companies by market capitalization as of June 30, 2026 grew their aggregate revenue from $75 billion to more than $2.5 trillion, an increase of roughly 34 times, or approximately 10 times the rate of global GDP growth .

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Over time, frontier AI s capabilities may extend beyond current markets to open entirely new ones, reaching domains such as robotics, advanced manufacturing, and logistics, and accelerating progress in scientific research, including drug discovery and materials science. We are beginning to take a direct role in realizing this potential. Through our life sciences program, we apply Claude to drug discovery and development, both in our own research and in collaboration with biotechnology partners, with the goal of demonstrating that frontier AI can materially shorten the time required to bring new medicines to patients. Ultimately, we believe frontier AI will help drive global economic growth. As frontier AI makes workers and organizations more productive, becomes embedded in the products and services they deliver, and unlocks new business models, it creates economic output that did not previously exist. We believe our opportunity is to capture a share not only of today s economic activity but also of that newly created output as the global economy expands.

For example, in the life sciences sector, where annual global pharmaceutical spend is expected to exceed $2 trillion in 2026, 9 Claude is used in drug discovery and development by our customers, our biotechnology partners, and our own researchers. We are also extending Claude into robotics, where our MHS enables AI agents to operate lab and manufacturing equipment such as microscopes, liquid handlers, and off-the-shelf robotic arms.

Ultimately, we believe frontier AI will help drive global economic growth. As frontier AI makes workers and organizations more productive, becomes embedded in the products and services they deliver, and unlocks new business models, it creates economic output that did not previously exist. We believe our opportunity is to capture a share not only of today s economic activity but also of that newly created output as the global economy expands.

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In addition to enabling more capable and efficient models, our multi-chip compute architecture across AWS Trainium, Google TPUs, NVIDIA GPUs, and AMD GPUs allows us to orchestrate workloads to optimize for price and performance. We have years of experience effectively training and running our models across multiple chip families, which we believe is differentiated and difficult to replicate. This foundation, paired with Claude s increasing understanding of chip and model architectures, allows us to onboard new chip platforms in a fraction of the time this once required.

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With each model and product advancement, Claude is able to address more valuable use cases, deepening its impact for existing customers while lowering the adoption threshold for new ones. When a model crosses the capability, reliability, and trust threshold required for a category of work, adoption in that category generally moves quickly from experimentation into production. Coding was the first such category within roughly a year, Claude progressed from suggesting individual lines of code to operating fleets of agents working autonomously across production codebases. Cybersecurity is following a similar curve, with Claude Mythos Preview and now Mythos 5.1 helping cyberdefenders identify and patch vulnerabilities at scale. We are also seeing early evidence of the same pattern in knowledge work such as scientific research, investment analysis, and insurance underwriting. 10 IDC, Worldwide Developer Population Growth Forecast, 2025 2029, doc # US53789825, September 2025.

of the same pattern in knowledge work such as scientific research, investment analysis, and insurance underwriting.

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We refer to these AI systems that work alongside humans to perform complex, multi-step work at professional levels as Virtual Collaborators. Virtual Collaborators carry the access rights, system connections, and context 8 IDC, Worldwide Developer Population Growth Forecast, 2025 2029, doc # US53789825, September 2025.

We refer to these AI systems that work alongside humans to perform complex, multi-step work at professional levels as Virtual Collaborators. Virtual Collaborators carry the access rights, system connections, and context needed to function more accurately and reliably. They can navigate and use internal tools and execute workflows that span multiple steps that require sustained reasoning. Virtual Collaborators are AI designed to be embedded where work gets done, and our aim is to continue extending them to knowledge workers globally, a population that is estimated to exceed one billion in 2028. 8 10 Claude Code The clearest manifestation of Virtual Collaborators today is Claude Code, our coding agent and the product we believe has most concretely defined what autonomous AI work looks like. Claude Code shifted the market s understanding of what an AI system could do alongside a software developer it not only suggests the next line of code, but also plans, writes, tests, and debugs all the way to production-grade code that is ready to ship.

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Applied AI Teams . Our Applied AI teams embed more deeply with select customers, building solutions that make it easier for organizations to benefit from Claude s intelligence. They work with enterprises to design and deploy custom solutions, specialized workflows, and domain-specific implementations tailored to the customer s own requirements. This work not only drives deeper adoption within individual customers but also generates the feedback and use cases that shape our platform and product roadmap. Our direct and Applied AI teams work together as a hybrid sales and engineering function, organized by geography and then by industry so that they bring domain expertise to each customer.

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9 Run-Rate Revenue is an operating metric, is not a substitute for revenue prepared in accordance with GAAP, and is not a forecast of future revenue. Because consumption-based revenue represents the substantial majority of our revenue and may fluctuate from period to period, and because our Run-Rate Revenue growth rate is expected to decline over time, our Run-Rate Revenue as of any measurement date may not be indicative of our revenue for future periods. Our Run-Rate Revenue as of any measurement date has included, and we expect will continue to include, amounts from customers that we subsequently determine are using our models, platform, and applications in violation of our usage policy or commercial terms, and whose revenue would not be expected to recur following restriction or termination of their access. See the section titled Risk Factors Certain of our operating metrics are subject to assumptions and limitations. As a result, these metrics may not provide an accurate indication of our actual performance or future results for additional information.

Ecosystem Partnerships . Major global SIs have built dedicated Claude practices with teams trained to deploy our platform and applications. Go-to-market alliances with leading data platforms embed Claude into data infrastructure that enterprises already operate, facilitating adoption of Claude within existing workflows and making it easier for enterprises to realize value from Claude. To accelerate the growth and capability of this ecosystem, in March 2026, we launched the Claude Partner Network, committing an initial $100 million in 2026 to fund partner training, technical certification, and joint market development with the firms that bring Claude into enterprise environments. This network comprises consultants across organizations who have received certifications through our Claude Certification Program. Certifications represent role-specific capabilities validated through proctored examinations designed to give enterprises confidence in the partner they are selecting . In October 2026, we launched the Claude Frontier Academy, backed by a $100 million commitment, to address a key constraint on enterprise AI adoption the shortage of engineers who can take AI from an initial use case into production. Drawing on our own experience deploying Claude inside large enterprises, the Academy trains our customers and partners engineers to the same standard as our own engineers, with each participant leading a Claude deployment at their own organization. We aim to train 10,000 engineers through the Academy by the end of 2027, beginning with cohorts from leading consulting firms and enterprises.

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We believe the diffusion of AI through the global economy represents one of the largest opportunities in history to improve how humans live and work, and it is just beginning. As of , our customers with $100,000 or more of revenue on a TTM basis included % of the Fortune 500 and % of the Global 2000. Within the Fortune 500, these customers included of the 10 largest software and IT services companies, of the 10 largest internet companies, of the 10 largest pharmaceutical companies, of the 10 largest banks, of the 10 largest semiconductor companies, and of the 10 largest healthcare companies. We believe enterprises are still in early phases of deployment, and we believe most knowledge workers have yet to fully experience what frontier AI can help them accomplish. We believe many industries have barely begun to explore the transformative potential of AI-native workflows. Furthermore, each successive version of a Claude model is designed to unlock new use cases, new customer types, and entirely new categories of economic value and expand our addressable market.

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Rapid Customer Adoption and Revenue Growth

Our enterprise strategy is designed to drive durable customer adoption by embedding Claude directly into business-critical workflows and enabling the development of products built on Claude. Over the past years, we have seen rapid customer adoption across companies of every size and industry around the world.

11 Run-Rate Revenue is an operating metric, is not a substitute for revenue prepared in accordance with GAAP, and is not a forecast of future revenue. Because consumption-based revenue represents the substantial majority of our revenue and may fluctuate from period to period, and because our Run-Rate Revenue growth rate is expected to decline over time, our Run-Rate Revenue as of any measurement date may not be indicative of our revenue for future periods. Our Run-Rate Revenue as of any measurement date has included, and we expect will continue to include, amounts from customers that we subsequently determine are using our models, platform, and applications in violation of our usage policy or commercial terms, and whose revenue would not be expected to recur following restriction or termination of their access. See the section titled Risk Factors Certain of our operating metrics are subject to assumptions and limitations. As a result, these metrics may not provide an accurate indication of our actual performance or future results for additional information.

Rapid Customer Adoption and Revenue Growth. Our enterprise strategy is designed to drive durable customer adoption by embedding Claude directly into business-critical workflows and enabling the development of products built on Claude. Over the past years, we have seen rapid customer adoption across companies of every size and industry around the world. From to , we have grown customers with $100,000 or more of revenue on a TTM basis from to . Additionally, o ur Run-Rate Revenue was $ as of . 9 11 Significant Customer Expansion . Our customers journey using Claude often begins with a single use case before expanding across departments, applications, and workflows. As our models become more capable, customers often deepen our partnerships they find more ways to embed Claude into their workflows, increasing the scope of what Claude can accomplish within their enterprises. This pattern of initial deployment, demonstrated value, and rapid expansion has been replicated across organizations and is reflected in our Net Revenue Retention of % as of .

We see this expansion as customers adopt agentic products like Claude Code. For example, in the three months ended September 30, 2026, Claude Enterprise users who did not use Claude Code spent less than $75 per month on average, while the top 5% of Claude Enterprise users who used Claude Code spent approximately 20 times that amount.

Diffusion also accelerates as our models get more capable and more efficient allowing us to pass on a portion of our compute efficiency gains by lowering costs for customers. With the release of Claude Fable 5.1 in September 2026, we reduced the cost for typical workloads versus earlier Fable 5 by approximately 25%, with savings up to 45% for complex coding and highly agentic tasks. With the release of Opus 5.5 in September 2026, we reduced customer costs compared to earlier Opus 5 by approximately 40%. We have been able to make these improvements while preserving our margins due to our compute efficiency. We have historically seen customers expand their usage in response to higher capability and lower cost per task. For example, when we reduced customer costs on Claude Opus 4.5 relative to Opus 4.1 by approximately 67% in November 2025, our quarter-over-quarter revenue growth accelerated from 48% for the three months ended December 31, 2025 to 132% for the three months ended March 31, 2025.

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Our growth strategy reflects our conviction that sustained leadership in frontier AI requires continuous investment in our people and culture, compute and infrastructure, Claude s intelligence, capability, and safety, the breadth of Claude s availability, customer adoption and expansion, entering new markets, and strategic transactions and partnerships that accelerate our mission.

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12 IQVIA Institute for Human Data Science, Global Medicine Use Trends 2026, February 2026.

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Extend the Frontier into New Markets . As frontier AI continues to advance, we see potential for it to accelerate novel discoveries and open entirely new markets, reaching domains from healthcare and life sciences to robotics and the physical world. For example, in the life sciences sector, where annual global pharmaceutical spend is expected to exceed $2 trillion in 2026, Claude is used in drug discovery and development by our customers, our biotechnology partners, and our own researchers . 12 We are building AI agents that support scientists at each stage of that process. To build the best tools for the industry, we have invested in deep life sciences expertise of our own, hiring industry experts, building a wet lab, acquiring Coefficient Bio, and partnering with industry leaders. Beyond knowledge work, we believe frontier AI has the power to transform physical work. Advances in Claude s general capabilities, together with our MHS, now allow it to operate lab and manufacturing equipment such as microscopes, liquid handlers, and off-the-shelf robotic arms. We believe that compounding model capabilities will continue to unlock transformative opportunities across many other sectors of the global economy.

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We build first-party applications to demonstrate where frontier models are heading and to deliver fully integrated solutions where we have a distinct viewpoint on the workflow. Applications like Claude Code and Claude Cowork serve as reference architectures for the future of agentic work, putting our latest capabilities directly into the hands of users while accelerating the market s understanding of what is possible. Because our research and product teams work side by side, new model capabilities inform our products, and how customers use those products informs how we train our models. This allows us to build products for where we believe that as model capabilities are going, not just where they are today. For example, we extend designed Claude Code around the frontier and broaden agentic coding capabilities we expected our application surfaces, customers realize value faster, embed Claude more deeply into their operations, and increasingly rely on us models to achieve, which positioned it to scale rapidly as those capabilities arrived, and we have taken a primary provider for their AI transformation . similar approach with Claude Cowork.

We also design our products to close the gap between what Claude can do and how fully customers use it, which we believe is important to helping customers realize the value of frontier intelligence. Rather than requiring customers to learn how the underlying models work, we build products that allow them to interact with Claude as they would with a colleague, simply and naturally, so they can adopt new capabilities as they become available. We believe that as we extend the frontier and broaden our application surfaces, customers realize value faster, embed Claude more deeply into their operations, and increasingly rely on us as a primary provider for their AI transformation .

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Model Hardware Standard . Currently in research preview, Model Hardware Standard ( MHS ) enables AI agents to find, understand, and safely operate lab and manufacturing equipment such as microscopes, liquid handlers, and off-the-shelf robotic arms. MHS is model-agnostic and lets any agent harness reach devices with a programmable interface through standard protocols such as MCP.

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We have built a deliberate and diversified compute sourcing strategy across multiple chip platforms This strategy that provides three the following distinct advantages First, We can secure compute from multiple providers rather than being constrained by any single provider. Second, we can select chips based on the best price-performance and efficiency characteristics for each workload, benefiting from the differentiated strengths of each platform.

We can select chips based on the best price-performance and efficiency characteristics for each workload, benefiting from the differentiated strengths of each platform.

Third, We maintain collaborative relationships with each of our providers, and we influence roadmaps and features of future chip generations. Our chip platforms include inc lude AWS Trainium. Our strategic collaboration with Amazon provides us with access to AI accelerators, including custom-designed Trainium chips, and compute infrastructure, as well as the opportunity to influence future generations of Trainium chips.

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We can onboard new chip platforms in a fraction of the time it once took. Historically, bringing a new chip platform into production required intense engineering effort over many months. By leveraging Claude, our engineering teams can now bring new chip platforms into production more quickly. This speed and efficiency also relies on the robust foundation we have already built across multiple chip families, including a proprietary orchestration layer designed to seamlessly test and measure performance against our actual workloads. As a result, we are able to adopt new AI accelerators as they become available, expanding our access to compute capacity while reducing our dependence on any single hardware provider.

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Historically, we purchased compute under fixed-time agreements. As our compute requirements have scaled, we have transitioned to a hybrid model in which, in addition to purchasing compute as a cloud service, we also lease specialized AI accelerators directly and host this hardware in dedicated data center facilities leased and operated on our behalf, to our specification. We refer to this transition is driven in part by the scale additional source of our compute requirements as well as the potential long-term economic advantages of Anthropic Direct. infrastructure leasing and hosting. Our first data center facility became operational this year.

This transition is driven in part by the scale of our compute requirements as well as the potential long-term economic advantages of direct infrastructure leasing and hosting. Our first data center facility became operational this year.

Anthropic Direct also lets us separate our commitment to data center capacity from our commitment to the AI accelerators deployed inside it. Because data centers require longer lead times but represent a smaller fraction of total costs, this approach enables us to secure physical capacity early while deferring larger accelerator investments until our specific compute needs are more certain.

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Various governments and regulatory authorities in the United States and other countries have proposed, enacted, or are considering legislation, regulations, and guidance relating to AI model development and deployment, data usage, privacy, data protection, transparency, safety, consumer protection, content generation, intellectual property, cybersecurity, export controls, and competition. For example, in the United States, AI regulation is developing at both federal and state levels. The National Institute of Standards and Technology has developed the AI Risk Management Framework, which is currently voluntary but could potentially inform or shape future mandatory requirements. The Federal Trade Commission FTC and many state attorneys general are interpreting federal and state consumer protection laws to impose standards for the online collection, use, dissemination, other processing, and security of data. Various states have enacted or are considering enacting AI-related legislation. In the EU, the EU AI Act establishes a risk-based framework categorizing AI systems by potential risk level. General-purpose AI models with systemic risks face obligations including transparency requirements, technical documentation, EU copyright compliance, and publication of training data summaries. High-risk systems must meet requirements for risk management, data governance, documentation, transparency, human oversight, accuracy, robustness, and cybersecurity. Any actual or perceived non-compliance could result in regulatory investigations, enforcement actions, and other actions, significant fines, litigation, reputational harm, loss of customer trust, and interruptions in the availability or performance of our products or services require unfavorable changes to existing products or services and delay the introduction of future products or services, which could have an adverse effect on our business.

High-risk systems must meet requirements for risk management, data governance, documentation, transparency, human oversight, accuracy, robustness, and cybersecurity. Any actual or perceived non- compliance could result in regulatory investigations, enforcement actions, and other actions, significant fines, litigation, reputational harm, loss of customer trust, and interruptions in the availability or performance of our products or services require unfavorable changes to existing products or services and delay the introduction of future products or services, which could have an adverse effect on our business.

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We collect, store, transfer, and otherwise process various types of data, including personal information, and are subject to numerous federal, state, local, and foreign laws and regulations addressing privacy, data protection, security, data localization, and the collection, use, transfer, and other processing of data. In the United States, these include comprehensive state privacy laws such as the California Consumer Privacy Act, which imposes obligations on us to, among other things, provide specific privacy disclosures, afford privacy rights such as abilities to opt out of certain processing, selling, or sharing of personal information, and expand consumer rights to personal information. On the federal level, the Federal Trade Commission FTC and many state attorneys general are interpreting federal and state consumer protection laws to impose standards for the online collection, use, dissemination, other processing, and security of data, some of which may be inconsistent. Outside of the United States, numerous foreign jurisdictions, including the EU, United Kingdom, and other jurisdictions in which we operate our business, have enacted similar laws and regulations that affect our business. For example, the EU General Data Protection Regulation and the United Kingdom General Data Protection Regulation impose stringent requirements upon the processing of personal data. The EU s Digital Services Act imposes requirements on online platforms, while the EU s Data Act introduces rules on data switching and data sharing. With respect to security, the EU s Network and Information Security Directive requires risk management and incident reporting. These laws and regulations may be subject to differing interpretations or conflict with the obligations imposed by other legal regimes, such as laws of the United States concerning government access to data.

Outside of the United States, numerous foreign jurisdictions, including the EU, United Kingdom, and other jurisdictions in which we operate our business, have enacted similar laws and regulations that affect our business. For example, the EU General Data Protection Regulation and the United Kingdom General Data Protection Regulation impose stringent requirements upon the processing of personal data. The EU s Digital Services Act imposes requirements on online platforms, while the EU s Data Act introduces rules on data switching and data sharing. With respect to security, the EU s Network and Information Security Directive requires risk management and incident reporting. These laws and regulations may be subject to differing interpretations or conflict with the obligations imposed by other legal regimes, such as laws of the United States concerning government access to data. Compliance with these laws and regulations requires significant investment in technical and organizational measures, and these laws and regulations are subject to frequent changes and evolving interpretations. Any actual or perceived non-compliance could result in regulatory investigations, enforcement actions, significant fines, litigation, reputational harm, loss of customer trust, interruptions in the availability or performance of our models, platform, and applications, requirements for unfavorable changes to existing models, platform, and applications, and delays in the introduction of future offerings, which could adversely affect our business.

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Our governance structure and its goals 2 changes

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A Continuing Founder will be removed from the Founder LLC upon the earliest to occur of (i) such Continuing Founder s resignation as a member of the Founder LLC (ii) such Continuing Founder s death or incapacity (iii) with respect to each of our seven original co-founders, such original co-founder s ceasing to hold (together with such original co-founder s related holders) on any record date for a vote of our stockholders a number of shares of Class A common stock (including shares subject to options, RSUs, and other convertible securities) equal to at least 2% of the shares of Class A common stock held by such original co-founder and their related holders as of the completion of this offering (iv) such Continuing Founder s removal for cause (as defined in the Founder LLC Agreement), which includes specified competitive activities described in the Founder LLC Agreement (v) with respect to any successor member admitted to the Founder LLC prior to the commencement of the Class F Transition Period, other than a successor member who is a member during the Class F Transition Period, the tenth anniversary of the completion of this offering or (vi) with respect to each of our seven original co-founders, the commencement of a leave of absence approved by a Supermajority of the other Continuing 10 13 Benjamin Mann has been an integral part of Anthropic since its founding in January 2021 and, given his role in establishing Anthropic, is often referred to as a co-founder. However, Mr. Mann is not a member of the Founder LLC nor a Continuing Founder upon the completion of this offering, and accordingly will not participate in determining how the Class F common stock is voted. Except as set forth in this paragraph, references to our co-founders elsewhere in this prospectus do not include Mr. Mann.

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Our seven co-founders co-founded Anthropic in January 2021 . Each co-founder will initially be a member of the Founder LLC and a Continuing Founder for purposes of the Class F common stock structure described above. Brief biographical information about each co-founder is set forth below. 10 13 Dario Amodei has served as our Chief Executive Officer and a member of our board of directors since co- founding Anthropic in January 2021. See Management Executive Officers for Dr. Amodei s full biographical information.

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Management 2 changes

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Yasmin Razavi . Ms. Razavi has served as a member of our board of directors since January March 2023. Ms. Razavi joined Spark Capital, a venture capital firm, in July 2017 and has served as a General Partner since March 2020. Prior to joining Spark Capital, Ms. Razavi served as an investor at Index Ventures, a venture capital firm, from August 2014 to August 2015, and as a consultant at McKinsey & Company, a business management consulting firm, from August 2012 to August 2014. Ms. Razavi holds an M.B.A. from Harvard Business School and a Bachelor of Applied Science and Engineering from the University of Toronto. We believe that Ms. Razavi is qualified to serve as a member of our board of directors because of her experience in venture capital and growth-stage investing, her deep expertise in enterprise go-to-market strategies, and her understanding of the technology sector and scaling high-growth companies.

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Following the completion of this offering, our NGC will consist of Ms. Amodei, Mr. Hastings, and Ms. Razavi , with Ms. Amodei serving as Chairperson. Mr. Hastings and Ms. Razavi will meet the requirements for independence under the listing standards of the Nasdaq Global Select Market. We will utilize the controlled company exemption from having an NGC composed entirely of independent directors, and our NGC will be composed of one Class T director and two directors who are not Class T directors. We believe that the composition and functioning of our NGC complies with all applicable requirements of the Sarbanes-Oxley Act and all applicable SEC rules and regulations. Following the completion of this offering, our NGC will, among other things assist the board of directors in evaluating individuals to become members of our board of directors, including through the exercise of governance rights conferred upon the NGC pursuant to our governing documents evaluate the performance of our board of directors and its committees consider and make recommendations to our board of directors regarding the skills, experience, characteristics, and other criteria for evaluating directors evaluate the adequacy of our corporate governance practices and develop and make recommendations to our board of directors regarding corporate governance guidelines and matters.

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