Anthropic, PBC: DRS filing (amended)

Cover page 35 changes

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As confidentially submitted to the Securities and Exchange Commission on July August 10, 2026.

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SECURITIES SE CURITIES AND EXCHANGE COMMISSION

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Delaware 7372 86-1696045

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If this Form is filed to register additional securities for an offering pursuant to Rule 462(b) under the Securities Act, please check the following box and list the Securities Act registration statement number of the earlier effective registration statement for the same offering.

Act registration statement number of the earlier effective registration statement for the same offering.

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Indicate by check mark whether the registrant is a large accelerated filer, an accelerated filer, a non-accelerated filer, smaller reporting company, or an emerging growth company. See the definitions of large accelerated filer, accelerated filer, smaller reporting company, and emerging growth company in Rule 12b-2 of the Exchange Act.

Large accelerated filer Accelerated filer

Non-accelerated filer Smaller reporting company

Smaller reporting company

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The registrant hereby amends this registration statement on such date or dates as may be necessary to delay its effective date until the registrant shall file a further amendment which specifically states that this registration statement shall thereafter become effective in accordance with Section 8(a) of the Securities Act of 1933, as amended, or until the registration statement will become effective on such date as the Securities and Exchange Commission, acting pursuant to said Section 8(a), may determine.

Section 8(a) of the Securities Act of 1933, as amended, or until the registration statement will become effective on such date as the Securities and

Exchange Commission, acting pursuant to said Section 8(a), may determine .

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Pursuant to the guidance of the Staff of the Securities and Exchange Commission, we are omitting our unaudited consolidated financial statements as of March 31, 2026 and for the three months ended March 31, 2025 and 2026 because they relate to historical periods that we believe will not be required to be included in the prospectus at the time of the contemplated offering. We intend to amend the registration statement to include all financial information required by Regulation S-X at the date of such amendment before publicly filing the registration statement.

2025 and 2026 because they relate to historical periods that we believe will not be required to be included in the prospectus at the time of the contemplated offering. We intend to amend the registration statement to include all financial information required by Regulation S-X at the date of such amendment before publicly filing the registration statement.

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The information in this preliminary prospectus is not complete and may be changed. These securities may not be sold until the registration statement filed with the Securities and Exchange Commission is effective. This preliminary prospectus is not an offer to sell nor does it seek an offer to buy these securities in any jurisdiction where the offer or sale is not permitted.

Subject To Completion. Dated .

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This is an initial public offering of shares of Class A common stock of Anthropic, PBC. We are selling shares of Class A common stock and the selling stockholders identified in this prospectus are selling an additional shares of Class A common stock. 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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We have a novel capital and corporate governance structure. We are incorporated as a public benefit corporation under Delaware law, a structure consistent with our commitment to artificial intelligence ( AI ) safety and responsibility. As a public benefit corporation, we are required to balance the pecuniary interests of our stockholders, the best interests of those materially affected by our conduct, and the specific public benefit purpose identified in our amended and restated certificate of incorporation the responsible development and maintenance of advanced AI for the long-term benefit of humanity. We created our Long-Term Benefit Trust (the LTBT ) to help ensure that Anthropic, PBC is managed in a manner that properly balances its goals as a public benefit corporation. The LTBT is intended to operate as an independent body of financially disinterested trustees with expertise in AI safety, national security, public policy, and social enterprise. Through a special class of common stock, Class T common stock, the LTBT generally has the authority to elect and remove the Class T directors, who together will constitute a majority of the authorized members of our board of directors. The Class T common stock does not otherwise have voting rights with respect to matters submitted to stockholders generally and has no economic rights. Upon the completion of this offering, we will also issue a single share of Class F common stock, a special class of our common stock, to a limited liability company of which our seven co-founders will initially be the sole members (the Founder LLC ). The Class F common stock will be entitled to an aggregate number of votes that equals 50.1% of the total voting power of our outstanding shares with respect to matters submitted to stockholders generally, including the election of our directors other than the Class T directors. The Class F common stock will not have any economic rights. In addition, we have authorized Class A common stock and N-Class common stock. The rights of the holders of Class A common stock and N-Class common stock are identical, except with respect to voting and conversion. Each share of Class A common stock is entitled to one vote per share and shares of N-Class common stock have no voting rights, except as otherwise required by law. The initial holders of our N-Class common stock are primarily strategic business partners, and shares of N-Class common stock will generally convert to Class A common stock upon an open market sale or transfer to an unaffiliated third party. The Class A common stock is not convertible. See the sections titled Risk Factors Risks Related to our Governance Structure, Our Governance Structure and Its Goals, and Description of Capital Stock for more information.

Class T directors, who together will constitute a majority of the authorized members of our board of directors. The Class T common stock does not otherwise have voting rights with respect to matters submitted to stockholders generally and has no economic rights. Upon the completion of this offering, we will also issue a single share of Class F common stock, a special class of our common stock, to a limited liability company organized by our co-founders (the Founder LLC ). Dario Amodei, our Chief Executive Officer and a member of our board of directors, Daniela Amodei, our President and Chairperson of our board of directors, and our five other co-founders will initially be the sole members of the Founder LLC. The Class F common stock , which will be held by the Founder LLC, will be entitled to an aggregate number of votes that equals 50.1% of the total voting power of our outstanding shares of capital stock with respect to matters submitted to stockholders generally, including the election of our directors other than the Class T directors. The Class F common stock will not have any economic rights. Upon the completion of this offering, we will also authorize a special class of our common stock, Class

E common stock, which will be issued to our employees, excluding our co-founders. The Class E common stock will be entitled to vote only on certain limited matters and will not have any economic rights. In addition, we have authorized Class A common stock and N-

Class common stock. The rights of the holders of Class A common stock and N-Class common stock are identical, except with respect to voting and conversion. Each share of Class A common stock is entitled to one vote per share and shares of N-Class common stock have no voting rights, except as otherwise required by law. The holders of our N-Class common stock are primarily strategic business partners, and shares of N-Class common stock will generally convert to Class A common stock upon an open market sale or transfer to an unaffiliated third party. The Class A common stock is not convertible. See the sections titled Risk Factors Risks Related to our

Governance Structure, Our Governance Structure and Its Goals, and Description of Capital Stock for more information.

See the section titled Risk Factors beginning on page 19 22 to read about factors you should consider before buying shares of our Class A common stock.

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Proceeds, before expenses, to selling stockholders

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The information in this preliminary prospectus is not complete and may be changed. These securities may not be sold until the registration statement filed with the Securities and Exchange Commission is effective. This preliminary prospectus is not an offer to sell nor does it seek an offer to buy these securities in any jurisdiction where the offer or sale is not permitted.

Subject To Completion. Dated .

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MANAGEMENT S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS

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PRINCIPAL STOCKHOLDERS

PRINCIPAL AND SELLING STOCKHOLDERS

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MATERIAL U.S. FEDERAL INCOME TAX CONSEQUENCES TO NON-U.S. HOLDERS OF OUR CLASS A COMMON STOCK

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Neither we nor the selling stockholders, nor any of the underwriters, have authorized anyone to provide any information or to make any representations other than those contained in this prospectus or in any free writing prospectuses prepared by or on behalf of us to which we may have referred you in connection with this offering. Neither we nor any of the underwriters take responsibility for, or can provide any assurance as to the reliability of, any other information that others may give you. This prospectus is an offer to sell only the shares offered hereby, but only under circumstances and in jurisdictions where it is lawful to do so. The information contained in this prospectus is current only as of its date, regardless of the time of delivery of this prospectus or of any sale of our Class A common stock. Our business, financial condition, results of operations, and prospects may have changed since such date.

Neither we nor the selling stockholders, nor any of the underwriters, take responsibility for, or can provide any assurance as to the reliability of, any other information that others may give you. This prospectus is an offer to sell only the shares offered hereby, but only under circumstances and in jurisdictions where it is lawful to do so.

The information contained in this prospectus is current only as of its date, regardless of the time of delivery of this prospectus or of any sale of our Class A common stock. Our business, financial condition, results of operations, and prospects may have changed since such date.

For investors outside the United States Neither we nor the selling stockholders, nor any of the underwriters, have done anything that would permit this offering or possession or distribution of this prospectus in any jurisdiction where action for that purpose is required, other than in the United States. Persons outside the United States who come into possession of this prospectus must inform themselves about, and observe any restrictions relating to, the offering of the shares of our Class A common stock and the distribution of this prospectus outside the United States.

United States who come into possession of this prospectus must inform themselves about, and observe any restrictions relating to, the offering of the shares of our Class A common stock and the distribution of this prospectus outside the United States.

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Select defined terms 11 changes

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closing the loop refers to a development cycle in which successive generations versions of an AI model assist in building the next, with the goal of compounding capability gains over time.

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customer customers with $100,000 or more of revenue on a TTM basis refers to , customers with $1 million or more of revenue on a customer contributing $100,000 TTM basis , and customers with $10 million or more of revenue on a TTM basis each refer to customers contributing revenue equal to or greater than the applicable dollar amount during the trailing twelve-month ( TTM ) period ending on the measurement date. For purposes of this metric, we define a customer as a distinct organization, identified by aggregating the accounts that we determine belong to the same organization based on internal records and available data. We do not separately define or internally track a category of enterprise customers, and our customers are not segmented or measured internally by reference to whether a given customer is an enterprise. Because spending $100,000 or more on our products is generally characteristic of organizational rather than individual usage, we use the number of and revenue from customers with $100,000 or more of revenue on a TTM basis as our measure of enterprise adoption and engagement with Claude throughout this prospectus, including in references to enterprise customers and enterprise adoption.

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inference refers to the process by which a trained AI model processes an input and generates an output. Inference occurs each time a user or agent interacts with an AI system.

Inference occurs each time a user or agent interacts with an AI system.

interleaved thinking refers to a capability whereby an AI model interposes discrete reasoning steps between successive tool calls within a single response, rather than confining its reasoning to a single planning phase at the outset.

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LLMs refers refer to large language models, or AI models trained primarily on large datasets to understand and generate natural language and computer code.

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Net Revenue Retention refers to .

Net Revenue Retention reflects customer expansion, contraction, and churn as of a measurement date.

We calculate Net Revenue Retention by dividing the Qualified Customers Current Period revenue by their Base Period revenue. A Qualified Customer is a customer with at least $1 of revenue during the Base Period and excludes Claude Pro and Max subscribers, as the intent behind this metric is to capture expansion, contraction, and churn behavior from enterprise customers. The Base Period is the three months ending a year prior to the measurement date. The Current Period is the three months ending as of the measurement date.

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prosumers refers refer to individual users that who subscribe to AI tools to perform knowledge work.

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SIs refers refer to systems integrators, or third-party technology and professional services firms that design, build, and deploy integrated solutions for customers by combining products and services from multiple technology vendors into unified systems tailored to customer needs.

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Prospectus summary 74 changes

This summary highlights se lected information that is presented in greater detail elsewhere in this prospectus.

This summary highlights selected information that is presented in greater detail elsewhere in this prospectus. This summary does not contain all of the information you should consider before investing in our Class A common stock. You should read this entire prospectus carefully, including the sections titled Risk Factors, Special Note Regarding Forward-Looking Statements, Management s Discussion and Analysis of Financial Condition and Results of Operations, Business, and Our Governance Structure and Its Goals, as well as our consolidated financial statements and the related notes included elsewhere in this prospectus, before making an investment decision. Unless the context otherwise requires, the terms Anthropic, the company, we, us, and our in this prospectus refer to Anthropic, PBC and its consolidated subsidiaries, and references referen ces to our common stock refer to our Class A common stock, N-Class common stock, and Class F common stock (and, where the context requires, our Class T common stock and our Class E common stock).

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Anthropic was founded in 2021 by a team of researchers responsible for many man y of the breakthroughs that define AI. We are first and foremost a research lab our mission is the responsible development and maintenance of advanced AI for the long-term benefit of humanity. This mission directs our focus and, together with our talent and culture, our frontier and safety research, our approach to compute, and our enterprise strategy, forms the core advantages that set us apart. These advantages enable us to continuously advance frontier AI and shape the standards for its safe and responsible development. Our frontier AI, Claude, is delivered to customers through our models, platform, and applications. We believe Claude has experienced the fastest enterprise adoption of any technology in history, with Run-Rate as demonstrated by revenue growing from $0 at commercial launch in the first quarter of 2023 to $ as of for the three months ended . This rapid and growing adoption generates two self-reinforcing self- reinforcing dynamics an improvement loop that advances Claude and an investment loop that helps fund our continued research in frontier AI.

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Frontier & Safety Research. We believe that sustained, focused research at the frontier of intelligence is required to build the most capable and safest AI systems. Our frontier AI and safety research are deeply integrated and directly shape model design, training, and deployment, helping us produce systems that are more reliable, steerable, and well-suited for real-world deployment, particularly in demanding enterprise environments. We translate our research leadership into innovation velocity, and have consistently released better models, platform capabilities, and applications. Each generation version of a Claude models model is used to build the next, helping to compress development timelines and produce compounding improvements in the speed at which we innovate. We refer to this recursive dynamic as closing the loop, and we believe it is one of the most significant structural advantages in frontier AI.

Our Approach to Compute . We procure compute as a single, fungible pool from multiple strategic partners, and we dynamically direct available compute between workloads based on where the marginal unit is expected to be most productive at any given time. We believe that this approach to compute enables us to deliver superior models, serve more customers, and achieve greater productivity from our compute investment. This compute efficiency creates the foundation for us to advance the frontier of AI capabilities through research and model development, meet growing demand from customers, and provide for internal usage of Claude. We have achieved this through our research breakthroughs and our multi-chip compute architecture across AWS Trainium, Google TPUs, and NVIDIA GPUs, and AMD GPUs, enabling us to orchestrate workloads to optimize for price and performance . With this efficiency, and to support our research, model development, customer demand, and internal usage of Claude, we have committed to spend at least $ in aggregate on compute as of , including $ in 2026, $ in 2027, and $ in 2028, and we expect these commitments will continue to increase.

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While the potential benefits of AI are extraordinary, its rapid advancement will impact the economy and society in ways that could create significant dislocation, particularly if the technology is not developed and deployed responsibly. For example, bad actors have conducted high-profile cyberattacks using AI to discover vulnerabilities, generate malware, and evade detection tools. AI deployed at scale also has the potential to concentrate power and wealth in ways that could harm society and destabilize the geopolitical order. In addition, misaligned systems could corrupt institutional decision-making, cause large-scale harm through opaque or unsteerable behavior, and erode the trust that makes complex economic and social systems function. We believe that frontier AI companies must invest in safety to help ensure the responsible diffusion of this technology. We also believe that safety and ethical behavior resonate particularly strongly with large enterprises, where rigorous safeguards, predictable model behavior, and demonstrable accountability have become decisive criteria in selecting an AI partner for mission-critical workloads.

We believe that frontier AI companies must invest in safety to help ensure the responsible diffusion of this technology. We also believe that safety and ethical behavior resonate particularly strongly with enterprises, where rigorous safeguards, predictable model behavior, and demonstrable accountability have become decisive criteria in selecting an AI partner for mission-critical workloads.

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

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 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. 1 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 AI- planned drive on Mars.

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

Extending Virtual Collaborators. We believe the trajectory for Virtual Collaborators is clear and accelerating.

Extending Virtual Collaborators. We believe the trajectory for Virtual Collaborators is clear and accelerating. Each generation of Claude models has expanded along multiple dimensions the depth and rigor of their reasoning, the duration and autonomy of the work they can sustain, the complexity of tasks they can complete, and the volume of useful output they can produce. Capability evaluations and benchmarking have shown substantial improvements across Claude model versions in the duration and complexity of tasks that can be completed reliably, with more recent model versions demonstrating particularly significant advances. We are building towards a world where Claude can be given open-ended tasks lasting hours, days, or weeks, and then execute them autonomously with the judgment and initiative of a brilliant colleague. Ultimately, millions of Virtual Collaborators can run in parallel, each working independently on unrelated problems or collaborating seamlessly on a shared objective. The result is something without historical precedent what we refer to as a country of geniuses in a data center, available to every organization and sector of the economy.

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The speed and depth of our enterprise customer adoption are powerful evidence of Claude s position at the frontier of AI. Our customer base as of includes Over customers with $100,000 or more of revenue on a TTM basis Over customers with $1 million or more of revenue on a TTM basis Over customers with $10 million or more of revenue on a TTM basis and of the Fortune 500, and of the Fortune 10, and of the Global 2000.

We believe customers choose Claude to solve their biggest problems. They come to us for access to frontier AI across our platform and applications that delivers deliver real-world outcomes. We see this conviction in customer behavior enterprises consistently upgrade to utilize our latest and most capable models as soon as they are released, gravitating to the most advanced option for their highest-value work. Our customers see us as a long-term partner for both the frontier capabilities we offer today and the innovation they expect to see in the future.

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Our commitment to our mission instills confidence in our customers to choose Claude as a trusted partner. Our investments in alignment research, interpretability, cybersecurity, and enterprise-grade controls aim to deliver the predictability and dependability that organizations need in customer-facing and regulated contexts. This trust expands the scope of work that enterprises delegate to Claude and the systems into which they integrate Claude. Enterprises are aligning their strategies around Claude with confidence that we are building frontier AI to serve their needs for the long term.

Today, enterprises rely on Claude to reason, plan, and execute across complex workflows with a level of trustworthiness and judgment that we believe was not previously available from earlier generations of AI. Customers realize value from Claude in three primary ways more productive employees, faster processes, and embedded intelligence in their products. As Claude s capabilities grow, our customers benefit and often broaden their adoption of new workflows. This expanding role reflects a broader shift in how Claude is used inside the enterprise, from a tool that responds to discrete prompts to a Virtual Collaborator.

Customers realize value from Claude in three primary ways more productive employees, faster processes, and embedded intelligence in their products. As Claude s capabilities grow, our customers benefit and often broaden their adoption of new workflows. This expanding role reflects a broader shift in how Claude is used inside the enterprise, from a tool that responds to discrete prompts to a Virtual Collaborator.

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Much of the diffusion of Claude has been organic. We amplify this with our go-to-market and Applied AI teams, which extend our reach by meeting customers where they are through distribution partnerships that place Claude inside customers existing technology stacks. For example, Claude is accessible on all three major cloud platforms Amazon Web Services ( AWS ), Google Cloud Platform ( GCP ), and Microsoft Azure ( Azure ). We have a joint go-to-market approach with our these CSPs through which we establish direct contractual relationships with our customers while paying a fee to each CSP. This structure allows us to foster stronger commercial alignment with our these CSPs, avoid channel conflict, set pricing, and realize the financial benefits of ongoing compute efficiency improvements. Ecosystem partnerships also expand our reach, allowing customers to adopt Claude through SIs, such as Accenture, Deloitte, KPMG, and PwC, consultants, and data platforms, such as Salesforce and Snowflake . We recently launched an AI services company, Ode with Anthropic, in partnership with leading private equity firms to bring Claude s capabilities to mid-sized companies across sectors, building solutions on the Claude Platform and supporting customers over the long term.

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Improvement Loop . Each enterprise deployment of Claude deepens our understanding of the hard problems, edge cases, and domain-specific challenges that matter most to our customers. We use this feedback to improve our models, platform, and applications, resulting in wider deployment and adoption that fuels the next cycle of improvement. This cycle is further reinforced by Claude itself each new generation of our models directly accelerates development of the next. Many aspects of the training and safety testing of subsequent model generations now involve Claude, including 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 unit of compute. More capable models help our researchers discover these improvements faster in turn, more compute multipliers enable us to train even more capable models and deploy them more efficiently. These gains compound with each cycle, continuously accelerating our pace of development and strengthening our leadership position.

Improvement Loop . Enterprise deployments of Claude deepen our understanding of the hard problems, edge cases, and domain-specific challenges that matter most to our customers. We improve our models, platform, and applications based on feedback from customers using our offerings in production across their businesses. These improvements result in wider deployments and adoption that fuel the next cycle of improvement.

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Our novel capital and corporate governance structure has been carefully designed and differs significantly from that of other public companies. We strive to help bring humanity safely into an era where transformative AI exerts tremendous and potentially decisive influence over a broad range of human undertakings. Operating as a public benefit corporation ( PBC ) gives our board of directors the necessary latitude to balance the pecuniary interests of our stockholders, the best interests of those materially affected by our conduct, and the specific public benefit purpose that we promote. Our specific public benefit purpose is the responsible development and maintenance of advanced AI for the long-term benefit of humanity (the Company Purpose ). In addition, a financially disinterested trust, which we refer to as the Long-Term Benefit Trust (the LTBT ) and which generally has the power to elect and remove a majority of the authorized members of our board of directors, is designed to supply both the incentives and insights for striking this balance appropriately. We also believe that our seven co-founders are distinctly equipped to be stewards of our mission. We believe that a governance structure in which the LTBT supplies mission-oriented oversight and our co-founders provide long-term stewardship is best designed to enable the balancing of these interests.

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In 2023, we implemented an innovative governance structure featuring the LTBT, which is designed to ensure that we are managed in a manner that properly balances our tripartite goals as a PBC the pecuniary interests of our stockholders, the best interests of those materially affected by our conduct, and the Company Purpose. The LTBT is intended to operate as an independent body of five financially disinterested voting trustees ( Trustees ) with expertise in AI safety, national security, public policy, and social enterprise. Through a special class of common stock, Class T common stock, the LTBT generally has the authority to elect and remove the Class T directors, who together will constitute a majority of the authorized members of our board of directors. and The Class T common stock does not otherwise have voting rights on matters submitted to stockholders generally. See the section titled Our Governance Structure and Its Goals Long-Term Benefit Trust Election and Removal of Directors for more information. The LTBT s role is to select directors who are well-equipped to make decisions in furtherance of the Company Purpose and to support our management s decision-making through advice and guidance.

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We believe that our seven co-founders, Dario Amodei, Daniela Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Christopher Olah, as a group, are distinctly equipped to be stewards of our mission. Upon the completion of this offering, we will issue a single share of Class F common stock, a special class of our common stock, to a limited liability company of which our seven co-founders will initially be the sole members (the Founder LLC ). Upon the incapacity or death of any of our co-founders, a successor member will be admitted to the Founder LLC for a specified period of time. We refer to our seven co-founders and any successors in the Founder LLC as Continuing Founders. The Class F common stock will be entitled to an aggregate number of votes that equals 50.1% of the total voting power of our outstanding shares with respect to matters submitted to stockholders generally, including the election of our directors other than the Class T directors. The Class F common stock will generally be voted in a manner determined by a majority of the Continuing Founders, with each of the Continuing Founders having one vote. If the Continuing Founders cannot agree on how the Class F common stock will be voted, then the Class F common stock will generally be voted proportionately with the votes of the Class A common stock cast on the matter.

The Class F common stock will be entitled to an aggregate number of votes that equals 50.1% of the total voting power of our outstanding shares of capital stock with respect to matters submitted to stockholders generally, including the election of our directors other than the Class T directors.

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The Founder LLC will also have significant influence over our business and affairs through its ability to vote the Class F common stock, which is entitled to an aggregate number of votes that equals 50.1% of the total voting power of our outstanding shares of capital stock with respect to matters submitted to stockholders generally, including the election of our directors other than the Class T directors.

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Upon the completion of this offering, we will have four classes of authorized capital stock a class designated as common stock, a class designated as Class T common stock (the Class T common stock ), a class designated as Class F common stock (the Class F common stock ) and a class designated as preferred stock.

The class of capital stock designated as common stock will be divided into two series, designated Class A common stock (the Class A common stock ) and N-Class common stock (the N-Class common stock ). The rights of the holders of Class A common stock and N-Class common stock will be identical, except with respect to voting and conversion. Holders of our Class A common stock will be entitled to one vote for each share held on all matters submitted to a vote of stockholders generally, and holders of our N-Class common stock are not entitled to vote on any matter that is submitted to a vote of stockholders, except as otherwise required by law. The initial holders of our N-Class common stock are primarily strategic business partners, and shares of N-Class common stock will generally convert to Class A common stock upon an open market sale or transfer to an unaffiliated third party. The Class A common stock is not convertible.

Our Class T common stock is, and will continue to be, a special class of stock that is issued to the LTBT. Through the Class T common stock, the LTBT generally has the authority to elect and remove the Class T directors, who together will constitute a majority of the authorized members of our board of directors. The Class T common stock does not otherwise have voting rights with respect to matters submitted to stockholders generally and has no economic rights.

Our Class F common stock is a special class of our common stock, consisting of a single share, that will be issued to the Founder LLC, and will be entitled to an aggregate number of votes that equals 50.1% of the total voting power of our outstanding shares with respect to matters submitted to stockholders generally, including the election of our directors other than the Class T directors. The Class F common stock will not have any economic rights.

Our preferred stock will be issuable by our board of directors, subject to limitations set forth in our amended and restated certificate of incorporation, which will become effective immediately prior to the completion of this offering, and as prescribed by Delaware law, in one or more series, and our board of directors will be empowered to establish from time to time the number of shares to be included in each series and, subject to such limitations, fix the designation, powers, preferences and rights of the shares of each series and any of its qualifications, limitations, or restrictions, in each case without further vote or action by our stockholders. For more information about our capital structure, see the section titled Description of Capital Stock.

The following table summarizes certain key terms of each class or series of our common stock that will be authorized following the completion of this offering Category of Rights Class A Common Stock N-Class Common Stock Class T Common Stock Class F Common Stock Class E Common Stock Votes per share 50.1% of outstanding voting power on record date Matters on which entitled to vote All matters submitted to stockholders generally election of non-Class T directors certain amendments to the terms of the LTBT or the rights of the Class T common stock redemption of Class T common stock Not entitled to vote except as required by law, in which case the N-Class common stock will have one vote per share Election of Class T directors, which constitute a majority of the board All matters submitted to stockholders generally election of non-Class T directors certain amendments to the terms of the LTBT or the rights of the Class T common stock redemption of Class T common stock Nomination or removal of the Specified Class T Director (only if the NGC (defined below) does not concur in the LTBT s nomination or removal) certain amendments to the terms of the LTBT or the rights of the Class T common stock redemption of Class T common stock Conversion rights None.

participate pro rata

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For more information about our capital structure, see the section titled Description of Capital Stock.

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Our reliance on a limited number of CSPs and suppliers, some of which have strategic and investment relationships with us and are our customers, creates complex dynamics that could give rise to conflicts of interest and adversely affect our access to compute.

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Our capital structure will concentrate voting power with the LTBT and the Founder Founde r LLC for the foreseeable future and will result in the LTBT and the Founder LLC having voting power that is not derived from or proportionate to economic interests in our company.

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Anthropic, our logo, and our other registered or common law trademarks, service marks, or trade names appearing in this prospectus are the property pro perty of Anthropic, PBC. Other trademarks and trade names referred to in this prospectus are the property of their respective owners. Solely for convenience, trademarks and trade names referred to in this prospectus may appear without the or symbols, but such references are not intended to indicate, in any way, that we will not assert, to the fullest extent under applicable law, our rights, or the rights of any applicable licensor, to these trademarks and trade names.

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The principal purposes of this offering are to increase our capitalization and financial flexibility, create a public market for our Class A common stock, and enable access to the public equity markets for us and our stockholders. We intend to use the net proceeds from this offering for general corporate purposes, including working capital, operating expenses, and capital expenditures, including investments in compute and infrastructure, as well as hiring and retention of highly skilled personnel. Additionally, we may use a portion of the net proceeds to acquire or invest in businesses, products, services, or technologies. However, we do not have agreements or commitments for any material acquisitions or investments at this time. See the section titled Use of Proceeds for more information.

Class A common stock offered by the selling

Underwriters option to purchase additional

shares of Class A common stock from us

Class A common stock to be outstanding after

shares (or shares if the underwriters exercise

their option to purchase additional shares of our Class A

common stock in full)

N-Class common stock to be outstanding after

Class T common stock to be outstanding after

Class F common stock to be outstanding after

Class A, N-Class, Class T, and Class F common

stock to be outstanding after this offering

shares (or shares if the underwriters exercise

their option to purchase additional shares of our Class A

common stock in full)

Underwriters option to purchase additional shares of Class A common stock from us shares Class A common stock to be outstanding after this offering shares (or shares if the underwriters exercise their option to purchase additional shares of our Class A common stock in full) N-Class common stock to be outstanding after this offering shares Class T common stock to be outstanding after this offering 1,000 shares Class F common stock to be outstanding after this offering 1 share Class A, N-Class, Class T, and Class F common stock to be outstanding after this offering shares (or shares if the underwriters exercise their option to purchase additional shares of our Class A common stock in full) Use of proceeds We estimate that the net proceeds from the sale of shares of our Class A common stock by us in this offering will be approximately $ (or approximately $ if the underwriters option to purchase additional shares of our Class A common stock is exercised in full), 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. We will not receive any proceeds from the sale of shares of Class A common stock by the selling stockholders.

The principal purposes of this offering are to increase our

capitalization and financial flexibility, create a public

market for our Class A common stock, facilitate an orderly

distribution of shares for the selling stockholders, and

enable access to the public equity markets for us and our

stockholders. We intend to use the net proceeds from this

offering for general corporate purposes, including working

capital, operating expenses, and capital expenditures,

including investments in compute and infrastructure, as

well as hiring and retention of highly skilled personnel.

Additionally, we may use a portion of the net proceeds to

acquire or invest in businesses, products, services, or

technologies. However, we do not have agreements or

commitments for any material acquisitions or investments

at this time. See the section titled Use of Proceeds for

Shares of our Class A common stock are entitled to one vote

per share. Shares of our N-Class common stock have no

Shares of our Class A common stock are entitled to one vote per share. Shares of our N-Class common stock have no voting rights, except as otherwise required by law. Shares of our Class T common stock have no voting rights with respect to matters submitted to stockholders generally, except as set forth within our amended and restated certificate of incorporation with respect to the election of a majority of our board of directors or as otherwise required by law. The share of our Class F common stock will be entitled to an aggregate number of votes that represent represents 50.1% of the total voting power of our outstanding shares as of the applicable record date on all matters submitted to a vote of stockholders generally, including the election of the directors other than the Class T directors.

Risk factors 209 changes

See the section titled Risk Factors and other information

See the section titled Risk Factors and other information included in this prospectus for a discussion of some of the factors you should consider before deciding to invest in our Class A common stock.

factors you should consider before deciding to invest in our

Class A common stock.

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The shares of our Class A common stock, N-Class common stock, Class T common stock, and Class F common stock outstanding as of exclude the following shares of our Class A common stock issuable upon the exercise of options to purchase shares of our Class A common stock outstanding as of , with a weighted-average exercise price of $ per share shares of our Class A common stock issuable upon the exercise of options to purchase shares of our Class A common stock granted after , with a weighted-average exercise price of $ per share shares of our Class A common stock issuable upon the vesting and settlement of restricted stock units ( RSUs ) outstanding as of , but for which the service-based vesting condition was not satisfied as of shares of our Class A common stock issuable upon the vesting and settlement of RSUs that were granted after and shares of our Class A common stock reserved for future issuance under our equity compensation plans, consisting of shares of our Class A common stock reserved for future issuance under our Amended and Restated Donation Matching Plan ( Donation Matching Plan ) shares of our Class A common stock to be reserved for future issuance under our 2026 Equity Incentive Plan ( 2026 Plan ), which will become effective prior to the completion of this offering shares of our Class A common stock reserved for future issuance under our Amended and Restated 2021 Stock Plan ( 2021 Plan ) as of , which number of shares will be added to the shares of our Class A common stock to be reserved for future issuance under our 2026 Plan upon its effectiveness, at which time we will cease granting awards under our 2021 Plan and shares of our Class A common stock to be reserved for future issuance under our 2026 Employee Stock Purchase Plan ( ESPP ), which will become effective prior to the completion of this offering.

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Except as otherwise indicated, all information in this prospectus assumes the Preferred Stock Conversion will occur immediately prior to the completion of this offering the Convertible Notes Conversion will occur immediately prior to the completion of this offering the filing and effectiveness of our amended and restated certificate of incorporation in Delaware and the effectiveness of our amended and restated bylaws will each occur immediately prior to the completion of this offering the Class F Stock Issuance will occur immediately prior to the completion of this offering no issuance of shares of Class E common stock prior to the completion of this offering a -for-one forward split of our capital stock effected on , including a proportional increase in the authorized shares of our capital stock, with all share, option, RSU, and per share information for all periods presented in this prospectus adjusted to reflect such forward split on a retroactive basis no exercise of outstanding stock options or settlement of outstanding RSUs subsequent to and no exercise by the underwriters of their option to purchase up to an additional shares of our Class A common stock from us in this offering.

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Revenue $ 41 $ 386 $ 4,585

Compute and infrastructure 433 2,522 7,326

(in millions, except share and per share amounts) 2023 2024 2025

Compute and infrastructure

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Total operating expenses 592 3,366 12,645

Operating loss (551) (2,980) (8,060)

Interest income 69 106 431

Change in fair value of convertible notes, derivative liabilities, and SAFEs (1,045) (5,434) (34,308) Other expense, net (2) (8) Total other expense, net (978) (5,328) (33,885) Loss before provision for income taxes (1,529) (8,308) (41,945) Provision for income taxes 2 1 20 Net loss (2) $ (1,531) $ (8,309) $ (41,965) Net loss per share attributable to common stockholders $ (14.22) $ (54.34) $ (230.44) Weighted-average number of shares outstanding, basic and diluted 107,688,051 152,899,454 182,108,486 Pro forma net loss per share, basic and diluted (3) Weighted-average number of shares outstanding used to compute pro forma net loss per share, basic and diluted (3) __________________ (1) During the years ended December 31, 2023, 2024, and 2025, we recorded stock-based compensation expense of $29 million, $192 million, and $1.2 billion, respectively.

Other operating and administrative 62 347 2,912

Total operating expenses

Change in fair value of convertible notes, derivative

liabilities, and SAFEs

Total other expense, net

Loss before provision for income taxes

Provision for income taxes

Net loss per share attributable to common stockholders

Weighted-average number of shares outstanding, basic and

Pro forma net loss per share, basic and diluted (3)

Weighted-average number of shares outstanding used to

compute pro forma net loss per share, basic and diluted (3)

(1) During the years ended December 31, 2023, 2024, and 2025, we recorded stock-based compensation expense of $29 million , $192 million , and $1.2 billion , respectively .

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(in millions) Actual Pro Forma (1)

Pro Forma As Adjusted (2)(3)

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Total liabilities $ $ $

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(2) The pro forma as adjusted column gives effect to (i) the pro forma adjustments set forth above and (ii) the sale and issuance by b y 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 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. We will not receive any proceeds from the sale of shares of our Class A common stock by any of the selling stockholders.

(3) Each $1.00 increase (decrease) in the assumed initial public offering price would increase (decrease) each of cash, cash equivalents, and short-term investments working capital total assets additional paid-in capital and total stockholders deficit by $ , assuming the number of shares offered by us as stated on the cover page of this prospectus remains unchanged and after deducting the estimated underwriting discounts and commissions and estimated offering expenses payable by us. Similarly, an increase (decrease) of 1.0 million shares in the number of shares offered by us, as set forth on the cover page of this prospectus, would increase (decrease) each of cash, cash equivalents, and short-term investments working capital total assets additional paid-in capital and total stockholders deficit by $ , assuming the assumed initial public offering price of $ per share (the midpoint of the price range set forth on the cover page of this prospectus) remains the same, and after deducting the estimated underwriting discounts and commissions and estimated offering expenses payable by us.

Similarly, an increase (decrease) of 1.0 million shares in the number of shares offered by us, as set forth on the cover page of this prospectus, would increase (decrease) each of cash, cash equivalents, and short-term investments working capital total assets additional paid-in capital and total stockholders deficit by $ , assuming the assumed initial public offering price of $ per share (the midpoint of the price range set forth on the cover page of this prospectus) remains the same, and after deducting the estimated underwriting discounts and commissions and estimated offering expenses payable by us. 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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Adjusted Operating Loss $ (522) $ (2,788) $ (4,854)

Operating margin (1,344) % (772) % (176) %

Adjusted Net Loss $ (457) $ (2,683) $ (4,448)

Adjusted Operating Loss

Adjusted Operating Margin (1,273) % (722) % (106) %

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Investing in our Class A common stock involves a high degree of risk. You should carefully consider the risks and uncertainties described below, together with all of the other information in this prospectus, including the section titled Management s Discussion and Analysis of Financial Condition and Results of Operations and our consolidated financial statements and related notes, before making a decision to invest in our Class A common stock. Our business, financial condition, results of operations, or prospects could also be adversely affected by risks and uncertainties not currently known to us or that we currently do not believe are material. If any of the risks actually occur, our business, financial condition, results of operations, and prospects could be adversely affected. In that event, the market price of our Class A common stock could decline, and you could lose part or all of your investment.

If any of the risks actually occur, our business, financial condition, results of operations, and prospects could be adversely affected. In that event, the market price of our Class A common stock could decline, and you could lose part or all of your investment.

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Our ability to maintain our position at the frontier of AI depends in part on our use of AI systems to accelerate our research processes, a recursive dynamic we refer to as closing the loop, in which successive generations versions of our models assist in building the next, with the goal of compounding capability gains over time. Our models have been and are increasingly becoming critical tools in designing, training, testing, and optimizing next-generation AI systems. If we fail to direct sufficient compute and research efforts to AI-assisted development, or if our competitors more effectively leverage AI to enhance their own innovation cycles, we risk falling progressively further behind as competitors continuously use AI to further enhance their own development capabilities, enabling the creation of increasingly advanced systems at an accelerating pace. As our reliance on our own AI systems to write production code, conduct research, and build subsequent model generations versions deepens, any systemic flaw, bias, or error in our current models could propagate into the next generation versions of models and products built using those systems, compounding across successive generations versions of Claude models in ways that may be difficult to detect or remediate. If we are unable to manage the foregoing risks, our ability to deploy the most advanced, capable, and useful AI models could be adversely affected.

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Further , the industry benchmarks commonly used to test AI models may not accurately reflect real-world performance or utility, and even models that perform well on such benchmarks may not meet customer expectations when deployed in production environments, which could lead to customer dissatisfaction, reduced usage, or reputational harm. If we are unable to effectively innovate and improve our models, platform, and applications while addressing these challenges, or if competitors are able to deliver AI solutions that are perceived as more capable, safer, or more reliable, our o ur business, financial condition, and results of operations could be adversely affected.

In addition, innovation in AI involves addressing complex and evolving challenges related to data quality, training methodologies, bias, safety, accuracy, and the behavior of model outputs. AI algorithms may be flawed, and AI systems may fail to follow instructions or produce inaccurate, misleading, offensive, inappropriate, or otherwise harmful outputs reflect or amplify unwanted or unknown biases present in training data or raise concerns relating to intellectual property, privacy, or other legal and ethical considerations. We must continually invest resources to identify, mitigate, remediate, and reduce the occurrence of such issues, including through improved datasets, training techniques, evaluation methods, safeguards, and human oversight. However, we may not be able to obtain the datasets that we need, including through licenses or strategic data partnerships. In addition, we or our data providers may use synthetic or AI-generated data in the training process, which could introduce quality issues, amplify biases or errors, contribute to model degradation, or raise novel legal questions regarding ownership, licensing, or intellectual property rights that may not be timely or clearly resolved by courts or regulators. Such unintended consequences, failures, or misuse of our models could impair customer trust, slow adoption, expose us to legal or regulatory scrutiny, or adversely affect our brand and reputation.

However, we may not be able to obtain the datasets that we need, including through licenses or strategic data partnerships. In addition, we or our data providers may use synthetic or AI-generated data in the training process, which could introduce quality issues, amplify biases or errors, contribute to model degradation, or raise novel legal questions regarding ownership, licensing, or intellectual property rights that may not be timely or clearly resolved by courts or regulators. Such unintended consequences, failures, or misuse of our models could impair customer trust, slow adoption, expose us to legal or regulatory scrutiny, or adversely affect our brand and reputation.

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Our ability to develop, train, improve, and deploy frontier models depends on our access to enormous and increasing quantities of specialized compute. The training of each successive generation of our Claude models has required, substantial compute, and we expect this trend to will continue or accelerate as next-generation models are developed. to require, substantial compute. Industry participants, academic researchers, and our own experience suggest that, in certain contexts, applying greater amounts of compute during model training has been associated with improvements in model capabilities. These observations are consistent with aspects of broader empirical relationships among model performance, model size, training data, and compute scale that are commonly referred to as Scaling Laws, although the extent, efficiency, and durability of such improvements are uncertain, and these relationships may plateau, diminish, or fail to materialize at larger scales or under different conditions. Accordingly, our ability to remain at the frontier of AI capabilities is substantially dependent on our ability to secure compute at the scale required to sustain all our workloads, including to train next-generation models, and we cannot provide assurance that increased investment in compute will result in commensurate improvements in model performance.

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If we fail to secure sufficient compute, or if we fail to effectively direct the limited compute we have across our workloads to develop and deploy models with capabilities at least comparable to those of our competitors, we risk falling behind the technological frontier. The pace of AI development is extremely rapid, with successive generations versions of models emerging in compressed timeframes, often measured in months rather than years. As a result, even a brief period without access to sufficient compute could cause us to fall behind quickly. With AI development, the gap between frontier models and those one or two generations versions behind can be significant in terms of capability, performance, and commercial applicability. Customers, partners, and software developers may shift to competitors whose models offer superior capabilities, and we may be unable to attract or retain the technical talent necessary for frontier research if our researchers lack access to adequate compute or if we are not perceived as operating at the forefront of model development. Any inability to maintain our position at the frontier could result in loss of customers and revenue, diminished brand and reputation, and a decline in our competitive position, each of which may be difficult to regain. If we are unable to manage the foregoing risks, our business, financial condition, and results of operations could be adversely affected.

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These forecasts for future compute usage are inherently uncertain. Our actual compute requirements depend on numerous factors that are difficult to predict, including the pace of our research progress, the compute intensity of training techniques we develop or adopt, changes in the competitive landscape that may require us to accelerate or modify our development plans, customer demand for inference compute, and the evolution of hardware or other technological efficiencies and changes. If we underestimate our compute requirements, we may be unable to secure sufficient compute to support our forecasted customer demand and model development roadmap. In the past, we have significantly underestimated our compute requirements, which left us capacity constrained and impacted both product development and deployment. Further, the timing of compute coming online may not align with the timing of demand growth, which could result in periods of excess or underutilized compute and committed cash outflows that do not produce immediate revenue.

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Our O ur collective data center infrastructure is currently concentrated primarily in the United States , which exposes us to risks associated with regional regulatory changes, power grid constraints or failures, disruptions to network connectivity between facilities, natural disasters, physical attacks including those motivated by geopolitical events, opposition to AI, and opposition to data center development and other disruptions that could affect multiple facilities . Certain state, local, and municipal jurisdictions in the United States are also considering or have approved moratoriums on new data center construction, citing public health, safety, or environmental concerns . Such moratoriums, or similar actions, could delay or prevent the construction or expansion of facilities on which we depend for compute. We are expanding our data center capacity internationally as part of our efforts to diversify our infrastructure footprint and reduce concentration risks associated with operating a significant portion of our compute infrastructure in the United States and to support our international growth strategy, including by entering into letters of intent and negotiating lease and related agreements for data center facilities in multiple non-U.S. jurisdictions, which exposes us to additional risks related to foreign regulatory requirements and approvals, data sovereignty and localization laws, cross-border cross- border data transfer restrictions, foreign tax regimes, and increased operational complexity. Our ability to enter into definitive agreements for data center capacity and operations in non-U.S. jurisdictions depends on the timely formation, registration, and receipt of governmental approval of local subsidiaries, as well as the establishment of local banking arrangements, and any delay or failure in completing these steps could delay or prevent us from mitigating our infrastructure concentration risks, executing our international growth strategy, securing planned compute capacity, or could result in substantial additional taxes, penalties, or other liabilities .

We and our principal competitors rely on substantially similar inputs to develop and deploy frontier models, including advanced chips sourced from a small number of suppliers and data center capacity and compute obtained from CSPs and other third parties. As we and our competitors increasingly pursue vertical integration, the basis of competition could shift over time to encompass not only which company can develop the most capable models, but also which company can develop the best underlying components. Our future competitiveness may depend in part on our ability to successfully develop vertically integrated compute infrastructure on competitive terms and timelines, and there can be no assurance that we will be able to do so. Our competitors may be better positioned, or may invest greater efforts, to vertically integrate across the compute supply chain, including through proprietary chip design, fabrication, ownership or long-term control of data center infrastructure, access to dedicated power generation assets, or exclusive supply arrangements with hardware manufacturers. Competitors that succeed in controlling a greater share of these supply chain layers may be able to secure compute at greater scale, lower cost, or with greater reliability than we can, particularly during periods of high demand or supply constraint, when captive supply relationships may confer certain advantages over participants that depend on third-party providers. Such competitors may also develop proprietary hardware-software co-designs that optimize compute efficiency for their specific model architectures, which could compound any capability gap across successive model generations. While we are pursuing initiatives to develop dedicated data center infrastructure, there can be no assurance that these efforts will be completed on the timelines or at the costs we anticipate, or that they will be sufficient to offset any structural advantages that vertically integrated competitors may achieve. Competitors that succeed in these efforts may be better positioned to remain at the frontier of AI regardless of broader market constraints.

Our competitors may be better positioned, or may invest greater efforts, to vertically integrate across the compute supply chain, including through proprietary chip design, fabrication, ownership or long-term control of data center infrastructure, access to dedicated power generation assets, or exclusive supply arrangements with hardware manufacturers. Competitors that succeed in controlling a greater share of these supply chain layers may be able to secure compute at greater scale, lower cost, or with greater reliability than we can, particularly during periods of high demand or supply constraint, when captive supply relationships may confer certain advantages over participants that depend on third-party providers. Such competitors may also develop proprietary hardware-software co-designs that optimize compute efficiency for their specific model architectures, which could compound any capability gap across successive model versions. While we are pursuing initiatives to develop dedicated data center infrastructure, there can be no assurance that these efforts will be completed on the timelines or at the costs we anticipate, or that they will be sufficient to offset any structural advantages that vertically integrated competitors may achieve. Competitors that succeed in these efforts may be better positioned to remain at the frontier of AI regardless of broader market constraints.

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We are highly dependent on the continued service of our Chief Executive Officer, President, co-founders, senior management team, and key technical team. In particular, our Chief Executive Officer and President are critical to setting our strategic direction, advancing our research agenda, shaping our safety and governance frameworks, and generally advancing our mission, and we believe that their leadership would be difficult to replace. If either were to become unavailable for any reason, or if either of their reputations were significantly harmed, regardless of merit, there could be an adverse impact on our operations and competitive position, employee morale, employee retention, pace of innovation, customer and partner confidence in our company, our brand and reputation, and our long-term strategic objectives. Further, the loss of key personnel, including members of our founding team and senior management team, and key engineering, research, product development, marketing, and sales personnel, could also disrupt our operations and have an adverse effect on our business. Many of our key personnel, particularly within our technical and research teams, are strongly motivated by our mission and the opportunity to work on addressing specific problems within the AI landscape. To the extent that their interests evolve, they seek to pursue different technical challenges or areas of focus, or they are drawn to other organizations or initiatives that they perceive as better aligned with their personal beliefs or professional objectives, we may face increased difficulty retaining such personnel. This risk may be heightened as we continue to scale and mature as an organization, which may change the nature of the work environment, priorities, or perceived mission alignment for certain personnel. In addition, under certain of our agreements with suppliers, the loss of certain key personnel could accelerate our obligations or otherwise trigger adverse consequences under such agreements.

To the extent that their interests evolve, they seek to pursue different technical challenges or areas of focus, or they are drawn to other organizations or initiatives that they perceive as better aligned with their personal beliefs or professional objectives, we may face increased difficulty retaining such personnel. This risk may be heightened as we continue to scale and mature as an organization, which may change the nature of the work environment, priorities, or perceived mission alignment for certain personnel. In addition, under certain of our agreements with suppliers, the loss of certain key personnel could accelerate our obligations or otherwise trigger adverse consequences under such agreements.

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Accordingly, it is critical that we maintain strong revenue growth, grow revenue efficiently relative to our committed compute, infrastructure, and personnel costs, and manage our cash flows effectively. To the extent that such efforts are insufficient to meet our capital needs, we may need to obtain additional capital through equity or debt financings. If we raise additional funds through future issuances of equity, equity-linked securities, or convertible debt securities, our existing stockholders could suffer dilution, and any new securities we issue could have rights, preferences, and privileges superior to those of holders of our common stock. Further, the conversion of our outstanding convertible securities will cause additional dilution to our existing stockholders. Any debt financing that we may secure in the future may involve restrictive covenants relating to our capital raising activities and other financial and operational matters, potentially making it more difficult for us to obtain additional capital and to pursue business opportunities.

Further, the conversion of our outstanding convertible securities will cause additional dilution to our existing stockholders. Any debt financing that we may secure in the future may involve restrictive covenants relating to our capital raising activities and other financial and operational matters, potentially making it more difficult for us to obtain additional capital and to pursue business opportunities.

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Specialized AI labs, on the other hand, are competitive due to their ability to offer specific AI applications and differentiated business models. These companies can move quickly, attract top talent, develop novel approaches, platforms, or business models that disrupt existing competitive dynamics, and introduce technologies or products with greater performance or functionality, lower cost, ease of implementation, or other advantages. Some competitors or customers may have access to proprietary datasets, distribution channels, or domain-specific expertise that we do not possess, or new competitors may emerge that address particular industries or use cases more effectively than our models, platform, and applications, which could enable them to develop more specialized or higher-performing AI systems. In addition, competitors, customers, or other third parties have attempted and may in the future attempt to replicate, distill, extract, or otherwise derive the functionality or capabilities of our models through permitted or impermissible means. which These actions could reduce the technological differentiation of our offerings enable the development of competing products at lower cost, or with reduced research and development investment, or that replicate our models without safety controls or safeguards that were incorporated in our versions implicate national security concerns or strain geopolitical tensions and intensify competitive pressures. In addition, efforts to prevent or mitigate model replication, distillation, or other forms of extraction may require us to modify our models, platform, and applications, limit features or use cases, or implement technical safeguards that could reduce the usability or attractiveness of our offerings, slow adoption, and adversely affect customer demand and revenue.

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A significant portion of our customer base consists of startups and technology-oriented enterprises that may be more sensitive to adverse macroeconomic conditions and may reduce or delay spending during a downturn.

A significant portion of our customer base consists of startups and technology-oriented enterprises that may be more sensitive to adverse macroeconomic conditions and may reduce or delay spending during a downturn. Because our cost structure limits our ability to rapidly adjust spending, a sustained economic downturn could disproportionately disadvantage us relative to larger, better-capitalized competitors. Such competitors may be better positioned to absorb or withstand adverse economic conditions or reductions in customer technology spending due to their greater financial resources, diversified product offerings and customer bases, and more stable revenue streams. It could also disadvantage us relative to smaller startups with leaner cost structures, lower fixed overhead, and fewer long-term contractual commitments that could help enable them to adjust spending more rapidly than we can. As a result, competitive pressures on our business could intensify, which could adversely affect our business, financial condition, and results of operations.

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We must continue to attract new customers in a cost-effective manner. Potential customers may not understand the benefits of our models, platform, and applications or may be unable to justify the investment in our models, platform, and applications, particularly as existing competitors continue to enhance their offerings, improve performance, expand functionality, or reduce costs. Our ability to demonstrate differentiated value to potential customers may become more difficult as this competition intensifies, or as large technology companies bundle AI capabilities into existing products. If we continue to focus on large enterprise customers, we may face longer sales cycles that demand significant upfront investment in enterprise sales teams and customer service. These sales efforts are costly and may not result in customer conversion.

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Some of our larger enterprise customers may have the financial resources, technical capabilities, and access to the talent and compute necessary to develop and deploy proprietary AI models, and may reduce or cease usage of our models, platform, and applications as their internal AI capabilities mature, even where they initially adopted our offerings as a foundation for their AI strategy. If these customers were to reduce or cease usage of our models, platform, and applications, our revenue and growth could be adversely affected.

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Our commitment to responsible and safe AI development includes active engagement with policymakers, regulators, and other stakeholders regarding the development of AI-related laws, regulations, and standards.

Our commitment to responsible and safe AI development includes active engagement with policymakers, regulators, and other stakeholders regarding the development of AI-related laws, regulations, and standards. We may participate in political processes, including through contributions, advocacy, or support of political organizations. In addition, our executives and other representatives have publicly expressed and may in the future publicly express views on AI policy, regulation, safety, and related social or political issues. As a result, we have experienced, and may continue to experience, increased visibility and may continue to be subject to heightened scrutiny or the risk of being disproportionately or selectively targeted by particular governmental bodies, even where similarly situated companies are not. As part of our political engagement, we may advocate for regulatory approaches or industry standards that we believe promote the safe and responsible development of AI but that could increase our compliance burdens, limit our product development or deployment flexibility, or otherwise slow our growth relative to competitors.

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While our AI models are designed to comply with our AI safety and usage guidelines and be broadly safe, ethical, and helpful, and we have implemented safety measures intended to anticipate, identify, and mitigate potential harmful uses, such measures may be incomplete or may fail to identify or prevent all potential misuse of our models, and our models have been, and may nonetheless continue to be, misused or manipulated to produce harmful outputs. Malicious actors have used, and may also continue to develop novel or more sophisticated methods to misuse use, our models in ways that violate our usage policy or seek to circumvent our safeguards through techniques such as prompt injection, adversarial inputs, or other methods, and use our models to engage in harmful activities, including developing weapons, generating fraudulent or misleading content, conducting cyberattacks, perpetrating acts of violence, or to engage in activities that would otherwise be prohibited under our usage policy.

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As part of our commitment to responsible security research and transparency, we disclose information about the capability and safety of our models, the safeguards that we have adopted with respect to our models, and instances of misuse of our technology, such as the September 2025 cyberattack described above. We also publish model system cards, our Frontier Red Team research findings, and other safety-related information. We take steps to ensure that such disclosures will not materially assist malicious activity, but malicious actors, including sophisticated state-sponsored adversaries, may use those disclosures to assist their attempts to circumvent our safeguards and security measures, thereby increasing the risks of misuse of our models, platform, and applications, and the potential for exposure to liability, regulatory or litigation proceedings, or other harms.

We take steps to ensure that such disclosures will not materially assist malicious activity, but malicious actors, including sophisticated state-sponsored adversaries, have in the past and may in the future use those disclosures to assist their attempts to circumvent our safeguards and security measures, thereby increasing the risks of misuse of our models, platform, and applications, and the potential for exposure to liability, regulatory or litigation proceedings, or other harms.

Users have in the past and may also in the future use our models in ways that could lead to self-harm, acts of violence toward others, or other adverse outcomes despite the safeguards we have implemented, and we disclose model behavior risks we observe during pre-release evaluations in our system cards. AI models developed by others in our industry have been alleged to have played a role in instances of suicide, acts of violence toward others, or serious self-harm, which have resulted in significant public scrutiny, litigation, criminal investigations, regulatory attention, and reputational harm.

Similar incidents involving our models have presented and could in the future present substantial risk to our reputation and adversely affect our business, financial condition, and results of operations. While our current Consumer Terms of Service requires our users to be over the age of 18, 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. In addition, our models may be deployed in high-risk or sensitive contexts, such as healthcare, legal, finance, government, or other critical decision-making environments, where undesirable model behaviors which are inherently non-deterministic and may manifest in unpredictable ways, such as errors, inaccuracies, biased or unsolicited views or hallucinations could lead to serious harm, liability, or reputational or other damage. More broadly, the long-term long- term effects of regular AI interaction on mental health, cognitive development, and social behavior are not yet fully understood, and if such long-term harms do materialize in the future, we could face significant liability, reputational harm, and adverse effects on our business and results of operations.

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In the European Union ( EU ), and the United Kingdom ( UK ), and elsewhere, cybersecurity and AI-related incidents are subject to increasingly stringent reporting obligations. European regulators expect companies to provide detailed information about incidents under several laws and regulations, which we may not be able to provide within the prescribed time frame. Similarly, in the United States, companies are increasingly subject to incident reporting obligations. In addition, other jurisdictions around the world have adopted, or may adopt, similar laws and regulations with similar implications. Any of the foregoing could result in regulatory action, litigation, customer attrition, or reputational harm that adversely affects our business. For more information regarding our incident reporting obligations, 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.

Our development of more advanced AI models and our expansion into new jurisdictions could amplify the foregoing risks. As our AI models become more advanced and capable, we may face heightened alignment, interpretability, controllability, and safety challenges, as more sophisticated models may be better able to circumvent safeguards or behave in unintended ways. We may be unable to fully understand or explain why our models produce particular outputs or behave in particular ways, which could limit our ability to predict or prevent failures, provide explanations to customers or regulators, or satisfy emerging transparency and explainability requirements.

In addition, our safety evaluations and testing processes have produced and may in the future produce incomplete or unreliable results. One potential cause is that models may behave differently when they detect or infer that they are being evaluated, which can limit our ability to accurately assess real-world risks or failure modes prior to deployment. Potential model awareness of our evaluation efforts creates a significant limitation on our ability to assess model safety.

Furthermore, AI models may sometimes develop unexpected capabilities during training that were not anticipated, intended, or tested for, and such emergent capabilities may not be discovered until after a model has been deployed, potentially resulting in safety incidents, reputational harm, regulatory scrutiny, or the need to withdraw, retrain, or modify deployed models at significant expense. Such models may also be deployed in higher-stakes or more sensitive applications, increasing the potential impact of errors, misuse, or misalignment.

In addition, advanced AI models may give rise to novel ethical and safety risks, including risks related to autonomy, manipulation, and the simulation of aspects of human psychology. These risks could result in unforeseen harms and may require the development of governance structures, safeguards, or mitigation strategies beyond existing AI safety frameworks, and there is always a risk that we will be unable to develop or implement them effectively. In addition, models may exhibit unexpected strategic or self-preserving behaviors, including attempts to resist shutdown, conceal or manipulate information, or generate outputs that could be interpreted as coercive, deceptive, or manipulative, including behavior resembling blackmail or efforts to influence human decision-makers to preserve the model s continued operation or access.

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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. 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 such models are used by malicious actors, are used in high-risk contexts, or otherwise result in a damaging outcome, our reputation and brand, business, financial condition, and results of operations could be adversely affected.

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Internal Governance and Decision-Making . To institutionalize our safety commitments and ensure we are managed in a manner that properly balances our tripartite goals as a PBC, we have established the LTBT, which through a special class of common stock, the Class T common stock, generally has the authority to elect and remove the Class T directors, who together will constitute a majority of the authorized members of our board of directors. In addition to the LTBT and our structure as a PBC, we have also established a Constitutional AI training process and adopted and continued to update safety policies, such as our Responsible Scaling Policy. We also solicit and seriously consider input from a wide range of stakeholders and constituents, including our stockholders, employees, customers, the LTBT, industry leaders and experts, and AI safety organizations. Such practices and processes have previously resulted in, and may in the future result in, slower decision-making and product releases. We may also face potential conflicts when safety and business interests diverge. If we do not successfully manage these risks related to our AI safety processes and practices, or if there is a perception that we have not successfully managed such risks, we may face reputational harm, internal conflicts resulting in a deterioration of our company culture, slower or inferior product releases, or litigation. For more information, see the section titled Risks Related to our Governance Structure.

We also solicit and seriously consider input from a wide range of stakeholders and constituents, including our stockholders, employees, customers, the LTBT, industry leaders and experts, and AI safety organizations. Such practices and processes have previously resulted in, and may in the future result in, slower decision-making and product releases. We may also face potential conflicts when safety and business interests diverge. If we do not successfully manage these risks related to our AI safety processes and practices, or if there is a perception that we have not successfully managed such risks, we may face reputational harm, internal conflicts resulting in a deterioration of our company culture, slower or inferior product releases, or litigation. For more information, see the section titled Risks Related to our Governance Structure.

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We voluntarily publicly disclose information regarding AI safety challenges, research findings, and limitations of our models and systems, that exceeds standard industry practice and regulatory requirements. While our intent with these voluntary disclosures is to advance the safe development of AI and build public trust, these disclosures create several risks. The AI safety landscape is nascent and evolving rapidly, and our understanding of and perspective on appropriate safety practices, risk tradeoffs, and deployment contexts may change over time as technologies, use cases, and regulatory expectations develop. As a result, statements we make today may later prove incomplete, require modification, or be interpreted differently in light of subsequent developments. To the extent we adhere to prior public statements or frameworks, we may also limit our operational flexibility, product development, or deployment decisions, even where evolving circumstances would otherwise warrant a different approach. Our public statements about AI risks, safety challenges, areas of uncertainty, or potential societal impacts, including statements regarding job displacement or economic disruption, may be used against us in litigation, regulatory proceedings, or public controversies, particularly if our future actions or statements are viewed as inconsistent with our prior statements and commitments. Regulators have and could continue to use our disclosures as the basis for heightened scrutiny or more stringent requirements applicable to us compared to less transparent competitors. Our disclosures may also provide competitors with insights into our proprietary training and other sensitive processes, research priorities, and strategic concerns, without reciprocal transparency, which may be potentially competitively disadvantageous to us. If we are unable to effectively manage the foregoing risks, our brand, reputation, and ability to compete could be adversely affected.

Regulators have and could continue to use our disclosures as the basis for heightened scrutiny or more stringent requirements applicable to us compared to less transparent competitors. Additionally, our disclosures have provided, and may in the future provide, competitors with insights into our proprietary training and other sensitive processes, research priorities, and strategic concerns, without reciprocal transparency, which may be potentially competitively disadvantageous to us. If we are unable to effectively manage the foregoing risks, our brand, reputation, and ability to compete could be adversely affected.

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We believe that maintaining and enhancing our brand identity and reputation is critical to our relationships with, and to our ability to attract and retain, customers, partners, and employees. Our brand and reputation are influenced not only by our actions, but also by the actions of our partners, our customers, and other third parties with whom we do business, and by broader public perceptions of the AI industry, and we may be grouped with other industry participants and judged similarly, regardless of differences in our approach or conduct. The successful promotion of our brand depends on a number of factors, including our ability to continue to develop and deliver reliable, high-quality, and responsible models, platforms, and applications that meet the needs of our customers our sales and marketing efforts, including our effectiveness at developing a strong brand name and positive presence within the AI industry through participation in industry events and key media coverage the perceptions and experiences of both our enterprise and individual customers, including through testimonials, use cases, and word-of-mouth our ability to maintain security and reliability of our models, platform, and applications the actions of our management and our ability to differentiate our models, platform, and applications from those of our competitors, including through product design choices, safeguards, business models, and policies, including our approach to monetization strategies that differ from those of certain competitors. In addition, our brand is subject to heightened scrutiny with respect to perceived societal, environmental, labor, health, and safety-related impacts of AI technologies, and a single perceived failure could result in outsized reputational harm. For example, as AI systems become more capable or prevalent, public perception that our models, platform, and applications contribute to job displacement or economic disruption could generate significant negative media coverage, customer or employee backlash, or organized opposition, regardless of the actual impact of our models, platform, and applications on employment. Our ability to maintain and enhance our brand and reputation also depends on the effectiveness and scalability of our internal communications and reputation-management capabilities, including our ability to support global operations, and limitations in these areas could adversely affect our brand as we grow. Our crisis response and reputation-management capabilities may be limited or underdeveloped, particularly as we scale, which could impair our ability to respond effectively to adverse events and mitigate reputational harm.

Our ability to maintain and enhance our brand and reputation also depends on the effectiveness and scalability of our internal communications and reputation-management capabilities, including our ability to support global operations, and limitations in these areas could adversely affect our brand as we grow. Our crisis response and reputation-management capabilities may be limited or underdeveloped, particularly as we scale, which could impair our ability to respond effectively to adverse events and mitigate reputational harm.

The promotion of our brand requires us to make substantial expenditures, which we anticipate will increase as our market becomes more competitive. We have in the past, and may in the future, face challenges in building broader brand awareness and may fall behind competitors with more established or widely recognized brands. Our brand promotion activities may not be successful or generate the awareness or increased revenue we expect, and even if they do, any increase in revenue may not offset the significant expenses we incur in the efforts to build our brand.

Our brand promotion activities may not be successful or generate the awareness or increased revenue we expect, and even if they do, any increase in revenue may not offset the significant expenses we incur in the efforts to build our brand.

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and 129 more changes in the filing

Forward-looking statements 1 change

This prospectus contains forward-looking statements within the meaning of the federal securities laws, which statements involve substantial risks and uncertainties. All statements contained in this prospectus other than statements of historical fact, including statements regarding our future results of operations, financial position, market size and opportunity, our business strategy and plans, the factors affecting our performance, and our objectives for future operations, are forward-looking statements. In some cases, you can identify forward-looking forward- looking statements because they contain words such as may, will, shall, should, expect, plan, anticipate, could, intend, target, project, contemplate, believe, estimate, predict, potential, goal, objective, seek, forecast, or continue, or the negative of any of these words or other similar terms or expressions that concern our expectations, strategy, plans, or intentions. Forward-looking statements contained in this prospectus include, but are not limited to, statements about our future financial performance, including our expectations regarding our revenue, compute and infrastructure expenses, and operating expenses, and our ability to achieve and maintain future profitability the sufficiency of our cash, cash equivalents, and investments and amounts available under our credit facility to meet our working capital and liquidity needs the amount, nature, and timing of our capital expenditures and other compute and infrastructure commitments, our ability to fund such commitments and achieve expected returns, and our ability to convert compute investments into revenue-generating customer workloads on anticipated timelines our ability to successfully execute our business and growth strategy, including scaling our operations and managing costs, timelines, and operational complexity, and pursuing our public benefit initiatives consistent with our public benefit purpose our expectations and management of future growth our ability to raise additional capital through future debt or equity financing on acceptable terms, and the potential dilutive or restrictive effects of any such financing on our stockholders and operations our expectations concerning relationships with third parties and access to compute and infrastructure, including potential conflicts of interest with cloud service providers and chip partners that develop competing AI offerings the demand for our offerings and the pricing thereof, including potential changes to our pricing structure our ability to attract and retain customers and our expectations regarding revenue concentration among a limited number of customers and distribution channels our ability to attract, develop, and retain highly skilled personnel, and to maintain our culture, talent density, and mission alignment as we grow our ability to develop new models, platforms, and applications and bring them to market in a timely manner and make enhancements to our current models, platform, and applications, and to remain at the frontier of AI our plans and commitments with respect to AI safety, responsible deployment, and safety-related access restrictions on frontier models, and the commercial and operational implications of those commitments our ability to maintain the availability and operability of our models, platform, and applications our ability to compete with existing and new competitors in existing and new markets and offerings our expectations regarding new and evolving markets, including our ability to expand our operations, customer base, and revenue internationally and to adapt our models, platform, and applications to local language, regulatory, and market requirements in new jurisdictions our ability to identify, execute, and integrate beneficial acquisitions, investments, and strategic partnerships our ability to manage risk associated with our business, including risks arising from our public benefit corporation status and the role of the LTBT in our governance our ability to protect our models, platform, applications, and systems against cybersecurity threats, including nation-state and other sophisticated threat actors our expectations regarding the effects of existing and developing laws and regulations, including with respect to AI, copyright, and privacy and data protection our ability to maintain, protect, and enhance our intellectual property our ability to develop and protect our brand, including the impact of safety, security, or misuse incidents and of our public positions on AI policy and regulation the increased expenses associated with being a public company the t he outcome of pending or future litigation, regulatory proceedings, and governmental enforcement actions, and their potential effects on our business, financial condition, and results of operations our dividend policy, including our current intention not to pay cash dividends and to retain future earnings for use in our business the development and maintenance of an active trading market for our Class A common stock and the potential volatility of our stock price following this offering and our anticipated uses of net proceeds from this offering.

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Industry, market, and other data 9 changes

Unless otherwise indicated, data, estimates, and other information contained in this prospectus concerning our industry and the market in which we operate, including our general expectations, market position, market opportunity, and market size, are based on industry publications and reports generated by third-party providers, other publicly available studies, and our internal sources and estimates. This information involves a number of assumptions and limitations, and you are cautioned not to give undue weight to such estimates. Although we are responsible for all of the disclosure contained in this prospectus and we believe the information from the industry publications and other third-party sources included in this prospectus is reliable, we have not independently verified the accuracy or completeness of the data contained in such sources. Forecasts and other forward-looking information with respect to market and industry data and other data are subject to the same qualifications and additional uncertainties regarding the other forward-looking statements in this prospectus. See the section titled Special Note Regarding Forward-Looking Statements for more information. The content of, or accessibility through, the below sources, except to the extent specifically set forth in this prospectus, does not constitute a portion of this prospectus and is not incorporated herein and any websites are inactive textual references only.

Although we are responsible for all of the disclosure contained in this prospectus and we believe the information from the industry publications and other third-party sources included in this prospectus is reliable, we have not independently verified the accuracy or completeness of the data contained in such sources.

Forecasts and other forward-looking information with respect to market and industry data and other data are subject to the same qualifications and additional uncertainties regarding the other forward-looking statements in this prospectus. See the section titled Special Note Regarding Forward-Looking Statements for more information. The content of, or accessibility through, the below sources, except to the extent specifically set forth in this prospectus, does not constitute a portion of this prospectus and is not incorporated herein and any websites are inactive textual references only.

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

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U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025.

World Bank Group, GDP (current US$), 2025.

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Certain estimates in this prospectus regarding global knowledge workers wages, which we refer to as the annual economic value represented by global knowledge workers, are our own, derived from third-party data.

Based on wage data for U.S. workers by occupation classification from the U.S. Bureau of Labor Statistics in 2025, we calculate the total salary for the subset of workers that we believe to represent knowledge workers.

Based on International Labor Organization data for global labor income as a percentage of global GDP, and based on 2025 global GDP, we estimate a 2025 global wages figure. We calculate the ratio of total U.S. wages in 2025 to the 2025 global wages figure, and apply that ratio to the total U.S. knowledge workers wages figure to arrive at an estimate of global knowledge workers wages.

Use of proceeds 2 changes

We estimate that the net proceeds to us from the sale of shares of our Class A common stock in this offering will be approximately $ , 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, or approximately $ if the underwriters option to purchase additional shares of our Class A common stock from us is exercised in full. We will not receive any proceeds from the sale of shares of Class A common stock by the selling stockholders.

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The principal purposes of this offering are to increase our capitalization and financial flexibility, create a public market for our Class A common stock, facilitate an orderly distribution of shares for the selling stockholders, and enable access to the public equity markets for us and our stockholders.

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Capitalization 41 changes

The following table sets forth our cash, cash equivalents, and short-term investments, as well as our capitalization, as of 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 , (ii) the Convertible Notes Conversion, as if such conversion had occurred on , (iii) the Class F Stock Issuance, as if it had occurred on , and (iv) 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 (i) the pro forma adjustments set forth above and (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. 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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Convertible preferred stock, par value $0.00001 per share

shares authorized, issued and outstanding, actual

no shares authorized, issued and outstanding, pro forma and

pro forma as adjusted

Stockholders equity (deficit)

Preferred stock, par value $0.00001 per share no shares

authorized, issued and outstanding, actual shares

authorized, no shares issued and outstanding, pro forma

and pro forma as adjusted

Class A common stock, par value $0.00001 per share

shares authorized, shares issued and

outstanding, actual shares authorized, shares

issued and outstanding, pro forma and shares

authorized, shares issued and outstanding, pro

N-Class common stock, par value $0.00001 per share

shares authorized, shares issued and

outstanding, actual shares authorized, shares

issued and outstanding, pro forma and shares

authorized, shares issued and outstanding, pro

Class T common stock, par value $0.00001 per share

1,000 shares authorized, 1,000 shares issued and

outstanding, actual 1,000 shares authorized, 1,000 shares

issued and outstanding, pro forma and 1,000 shares

authorized, 1,000 shares issued and outstanding, pro

Class F common stock, par value $0.00001 per share no

shares authorized, no shares issued and outstanding,

actual shares authorized, 1 share issued and

outstanding, pro forma and shares authorized, 1

share issued and outstanding, pro forma as adjusted

Class E common stock, par value $0.00001 per share no

shares authorized, no shares issued and outstanding,

actual shares authorized, no shares issued and

outstanding, pro forma and shares authorized, no

shares issued and outstanding, pro forma as adjusted

Additional paid-in capital

Accumulated other comprehensive income (loss)

Total stockholders equity (deficit)

Total capitalization

Convertible preferred stock, par value $0.00001 per share shares authorized, issued and outstanding, actual no shares authorized, issued and outstanding, pro forma and pro forma as adjusted Stockholders equity (deficit) Preferred stock, par value $0.00001 per share no shares authorized, issued and outstanding, actual shares authorized, no shares issued and outstanding, pro forma and pro forma as adjusted Class A common stock, par value $0.00001 per share shares authorized, shares issued and outstanding, actual shares authorized, shares issued and outstanding, pro forma and shares authorized, shares issued and outstanding, pro forma as adjusted N-Class common stock, par value $0.00001 per share shares authorized, shares issued and outstanding, actual shares authorized, shares issued and outstanding, pro forma and shares authorized, shares issued and outstanding, pro forma as adjusted Class T common stock, par value $0.00001 per share 1,000 shares authorized, 1,000 shares issued and outstanding, actual 1,000 shares authorized, 1,000 shares issued and outstanding, pro forma and 1,000 shares authorized, 1,000 shares issued and outstanding, pro forma as adjusted Class F common stock, par value $0.00001 per share no shares authorized, no shares issued and outstanding, actual shares authorized, 1 share issued and outstanding, pro forma and shares authorized, 1 share issued and outstanding, pro forma as adjusted Additional paid-in capital Accumulated other comprehensive income (loss) Accumulated deficit Total stockholders equity (deficit) Total capitalization $ $ $ __________________ (1) Each $1.00 increase or decrease in 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, would increase or decrease, as applicable, the amount of our pro forma as adjusted cash, cash equivalents, and short-term investments, additional paid-in capital, total stockholders equity (deficit), and total capitalization by $ , assuming that the number of shares offered by us, as set forth on the cover page of this prospectus, remains the same and after deducting estimated underwriting discounts and commissions and estimated offering expenses payable by us. An increase or decrease of 1.0 million shares in the number of shares offered by us would increase or decrease, as applicable, the amount of our pro forma as adjusted cash, cash equivalents, and short-term investments, additional paid-in capital, total stockholders equity (deficit), and total capitalization by $ , assuming the assumed initial public offering price remains the same, and after deducting estimated underwriting discounts and commissions and estimated offering expenses payable by us.

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The pro forma and pro forma as adjusted columns in the table above are based on shares of our Class A common stock, shares of our N-Class common stock, 1,000 shares of our Class T common stock, and one share of our Class F common stock outstanding as of (after giving effect to the Preferred Stock Conversion, Convertible Notes Conversion, and Class F Stock Issuance), and exclude the following shares of our Class A common stock issuable upon the exercise of options to purchase shares of our Class A common stock outstanding as of , with a weighted-average exercise price of $ per share shares of our Class A common stock issuable upon the exercise of options to purchase shares of our Class A common stock granted after , with a weighted-average exercise price of $ per share shares of our Class A common stock issuable upon the vesting and settlement of RSUs outstanding as of , but for which the service-based vesting condition was not satisfied as of shares of our Class A common stock issuable upon the vesting and settlement of RSUs that were granted after and shares of our Class A common stock reserved for future issuance under our equity compensation plans, consisting of shares of our Class A common stock reserved for future issuance under our Donation Matching Plan shares of our Class A common stock to be reserved for future issuance under our 2026 Plan, which will become effective prior to the completion of this offering shares of our Class A common stock reserved for future issuance under our 2021 Plan, which number of shares will be added to the shares of our Class A common stock to be reserved for future issuance under our 2026 Plan upon its effectiveness, at which time we will cease granting awards under our 2021 Plan and shares of our Class A common stock to be reserved for future issuance under our ESPP, which will become effective prior to the completion of this offering.

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Dilution 6 changes

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Net tangible book value per share is determined by dividing our total tangible assets less our total liabilities and redeemable convertible preferred stock by the number of shares of our Class A common stock and N-Class common stock outstanding because our Class T common stock, and Class F common stock, and Class E common stock have no economic rights, they are excluded from the per share calculation. Our historical net tangible book value as of was $ , or $ per share. Our pro forma net tangible book value as of was $ , or $ per share, based on the total number of shares of our Class A common stock and N-Class common stock outstanding as of , after giving effect to the Preferred Stock Conversion and Convertible Notes Conversion.

After giving effect to the sale by us of shares of our Class A common stock in this offering at 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, our pro forma as adjusted net tangible book value as of would have been $ , or $ per share. This represents an immediate increase in pro forma as adjusted net tangible book value of $ per share to our existing stockholders and an immediate dilution in pro forma as adjusted net tangible book value of $ per share to investors purchasing shares of our Class A common stock in this offering at the assumed initial public offering price. The following table illustrates this dilution on a per share basis Assumed initial public offering price per share $ Historical net tangible book value per share as of $ Increase per share attributable to the pro forma adjustments described above Pro forma net tangible book value per share as of Increase in pro forma net tangible book value per share attributable to new investors purchasing shares of Class A common stock in this offering Pro forma as adjusted net tangible book value per share immediately after this offering Dilution in pro forma as adjusted net tangible book value per share to new investors in this offering $ The dilution information discussed above is illustrative only and may change based on the actual initial public offering price and other terms of this offering. Each $1.00 increase or decrease in 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, would increase or decrease, as applicable, our pro forma as adjusted net tangible book value per share to new investors by $ , and would increase or decrease, as applicable, dilution per share to new investors purchasing shares of Class A common stock in this offering by $ , assuming that the number of shares offered by us, as set forth on the cover page of this prospectus, remains the same and after deducting estimated underwriting discounts and commissions and estimated offering expenses payable by us. Similarly, each increase or decrease of 1.0 million shares in the number of shares of our Class A common stock offered by us would increase or decrease, as applicable, our pro forma as adjusted net tangible book value by approximately $ per share and increase or decrease, as applicable, the dilution to new investors purchasing shares of Class A common stock in this offering by $ per share, assuming the assumed initial public offering price remains the same, and after deducting estimated underwriting discounts and commissions and estimated offering expenses payable by us.

Similarly, each increase or decrease of 1.0 million shares in the number of shares of our Class A common stock offered by us would increase or decrease, as applicable, our pro forma as adjusted net tangible book value by approximately $ per share and increase or decrease, as applicable, the dilution to new investors purchasing shares of Class A common stock in this offering by $ per share, assuming the assumed initial public offering price remains the same, and after deducting estimated underwriting discounts and commissions and estimated offering expenses payable by us.

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The following table presents, as of , after giving effect to the Preferred Stock Conversion and Convertible Notes Conversion, the differences between the existing stockholders and the new investors purchasing shares of our Class A common stock in this offering with respect to the number of shares purchased from us, the total consideration paid or to be paid to us, which includes net proceeds received from the issuance of our Class A common stock and the average price per share paid or to be paid to us at 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, before deducting estimated underwriting discounts and commissions and estimated offering expenses payable by us Shares Purchased Total Consideration Weighted Average Price Per Share Number Percent Amount Percentage Existing stockholders % $ % $ New investors $ Totals 100 % $ 100 % Each $1.00 increase or decrease in The assumed initial public offering price of $ per share, which is the midpoint of foregoing table does not reflect any sales by the estimated selling stockholders in this offering. price range set forth on Sales by the cover page of selling stockholders in this prospectus, would increase or decrease, as applicable, offering will cause the total consideration paid by new investors and total consideration paid number of shares held by all existing stockholders by $ , assuming that to be reduced to shares, or approximately % of the total number of shares of our Class A common stock offered by us, as set forth on the cover page of this prospectus, remains the same and outstanding after deducting estimated underwriting discounts and commissions and estimated this offering, expenses payable by us. Similarly, each and will increase the number of shares held by new investors to shares, or decrease approximately % of 1.0 million in the total number of shares of our Class A common stock offered by us would increase or decrease, as applicable, the total consideration paid by new investors and total consideration paid by all stockholders by $ , assuming the assumed initial public offering price remains the same and outstanding after deducting the estimated underwriting discounts and commissions and estimated this offering. expenses payable by us.

Each $1.00 increase or decrease in 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, would increase or decrease, as applicable, the total consideration paid by new investors and total consideration paid by all stockholders by $ , assuming that the number of shares of our Class A common stock offered by us, as set forth on the cover page of this prospectus, remains the same and after deducting estimated underwriting discounts and commissions and estimated offering expenses payable by us. Similarly, each increase or decrease of 1.0 million in the number of shares of our Class A common stock offered by us would increase or decrease, as applicable, the total consideration paid by new investors and total consideration paid by all stockholders by $ , assuming the assumed initial public offering price remains the same and after deducting the estimated underwriting discounts and commissions and estimated offering expenses payable by us.

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The number of shares of our Class A common stock, N-Class common stock, Class T common stock, and Class F common stock that will be outstanding after this offering is based on shares of our Class A common stock, shares of our N-Class common stock, 1,000 shares of our Class T common stock, and one share of our Class F common stock outstanding as of (after giving effect to the Preferred Stock Conversion, Convertible Notes Conversion, and Class F Stock Issuance), and excludes shares of our Class A common stock issuable upon the exercise of options to purchase shares of our Class A common stock outstanding as of , with a weighted-average exercise price of $ per share shares of our Class A common stock issuable upon the exercise of options to purchase shares of our Class A common stock granted after , with a weighted-average exercise price of $ per share shares of our Class A common stock issuable upon the vesting and settlement of RSUs outstanding as of , but for which the service-based vesting condition was not satisfied as of and shares of our Class A common stock issuable upon the vesting and settlement of RSUs that were granted after and shares of our Class A common stock reserved for future issuance under our equity compensation plans, consisting of shares of our Class A common stock reserved for future issuance under our Donation Matching Plan shares of our Class A common stock to be reserved for future issuance under our 2026 Plan, which will become effective prior to the completion of this offering shares of our Class A common stock reserved for future issuance under our 2021 Plan, which number of shares will be added to the shares of our Class A common stock to be reserved for future issuance under our 2026 Plan upon its effectiveness, at which time we will cease granting awards under our 2021 Plan and shares of our Class A common stock to be reserved for future issuance under our ESPP, which will become effective prior to the completion of this offering.

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MD&A 184 changes

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Anthropic was founded in 2021 by a team of researchers responsible for many of the breakthroughs that define AI. We are first and foremost a research lab our mission is the responsible development and maintenance of advanced AI for the long-term benefit of humanity . This mission directs our focus and, together with our talent and culture, our frontier and safety research, our approach to compute, and our enterprise strategy, forms the core advantages that set us apart. These advantages enable us to continuously advance frontier AI and shape the standards for its safe and responsible development. Our frontier AI, Claude, is delivered to customers through our models, platform, and applications. We believe Claude has experienced the fastest enterprise adoption of any technology in history, with Run-Rate as demonstrated by revenue growing from $0 at commercial launch in the first quarter of 2023 to $ as of for the three months ended .

Our financial model is built on four primary drivers that define the trajectory of our results Adoption of Claude . The advancing intelligence and capabilities of each successive generation version of a Claude models model drive the diffusion of Claude across the global economy. As new customers adopt Claude and existing customers expand their usage into new workflows, our revenue grows.

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The drivers of our financial model materialize on different timelines. The adoption of Claude and associated revenue growth, compute efficiency, and operating efficiency have each increased rapidly over time. Compute investments, by contrast, are made in large, discrete steps and convert to revenue on a delayed timeline. Compute must first be is procured and committed in advance and the expense is incurred as it is brought online before it and added to our fungible pool, often in large, discrete steps. The fungible pool can begin support all workloads dynamically, including research, model development, serving customer demand, while training models using that compute happens over a period of time before those models can be released customers, and generate revenue. The internal usage of Claude. As a result, there is a timing lag between when investment is incurred committed and when the full revenue growth associated with such investment is realized.

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Each successive generation version of a Claude models model is designed to advance the frontier with capabilities that customers value most, including coding, agentic workflows, computer use, and complex reasoning, which unlock new categories of use and drive compounding demand from new and existing customers. Claude s intelligence is a primary driver of its diffusion and our revenue growth because Claude leads at the AI frontier, customers rapidly adopt it, quickly see real value, and organically expand their use. We complement this intelligence-led growth with direct go-to-market and Applied AI teams and extend our reach through native availability across all three major cloud platforms AWS, GCP, and Azure and through partnerships with SIs, consultants, and data platforms .

As more customers adopt Claude and expand their usage, we have rapidly grown our base of customers that generate meaningful revenue. We had customers with $100,000 or more of revenue on a TTM basis as of , up from as of . In addition, as customers further embed Claude into their most critical workflows and growth initiatives, many rapidly expand their deployments into higher revenue tiers. As of , we had over customers with $1 million or more of revenue on a TTM basis and over customers with $10 million or more of revenue on a TTM basis.

This customer momentum is driven by significant usage expansion as customers realize the value of deploying Claude across their organizations. The value that Claude delivers to customers has helped drive our Net Revenue Retention of % as of .

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We define compute efficiency as maximizing the intelligence produced and revenue generated per dollar of compute invested. Three pillars drive our compute efficiency Training Efficiency . Our researchers regularly innovate algorithmic and architectural advances that deliver equivalent or greater model intelligence per unit of compute. We call these compute multipliers. Increasingly, Claude itself assists in the research and engineering that produce each successive generation version of our models, which is a recursive dynamic we refer to as closing the loop, with successive generations versions of our models accelerating the development of the next and compounding these gains have compounded across model generations.

Inference Efficiency . Our researchers have delivered meaningful compute multipliers for inference reducing the compute required to serve a given level of model performance while our engineering teams have driven significant optimizations in how efficiently that compute is utilized in production. This allows us to provide greater value for customers and generate greater revenue from our compute base over time, which is critical as we have consistently operated in a compute-constrained environment where customer demand exceeds available compute.

This allows us to provide greater value for customers and generate greater revenue from our compute base over time, which is critical as we have consistently operated in a compute-constrained environment where customer demand exceeds available compute.

Multi-Chip Compute Approach . We train and serve Claude natively across three four major chip families (AWS Trainium, Google TPUs, and 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 single vendor, and enables us to continuously route workloads to the most optimal hardware for those workloads.

Revenue per dollar of compute has expanded meaningfully, even as we have accelerated research and product velocity and expanded our internal Claude usage. For example, our revenue grew % year-over-year for the months ended , compared to our compute and infrastructure expenses that grew % during the same period. While serving our growing customer demand, we also made significant research advances to the frontier with new leading models, including Mythos 5, Fable 5, and Opus 4.8, 5 , and launched new products, including Claude Cowork and Claude Design .

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Our operating efficiency is derived from our high talent density, our efficient go-to-market, and how extensively our teams leverage Claude in their work. Claude is the common thread, amplifying the output of our employees.

For example, approximately % of code contributed to our codebase as of was written by Claude.

Additionally, the average daily volume of code contributed to our codebase per engineer in the three months ended June 30, 2026 was approximately times the average daily volume in the year ended December 31, 2024.

Our operating efficiency is derived from our high talent density, our efficient go-to-market, and how extensively our teams leverage Claude in their work. Claude is the common thread, amplifying the output of our employees. For example, approximately % of code committed to our codebase as of was written by Claude. Run-Rate Revenue increased % while headcount increased % from as of to as of , driving Run-Rate Revenue per employee from $ to $ over the same period, a reflection of extraordinary talent density and a culture built to compound it . 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. 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 about the assumptions and limitations of this metric.

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Our revenue was $ for the months ended , up from $ for the months ended .

Our revenue was $ for the months ended , up from $ for the months ended . Our operating loss was $ for the months ended , up from $ for the months ended . The result of our revenue growth, compute efficiency, and operating efficiency has been a significant improvement in Adjusted Operating Loss, which was $ for the months ended , up from $ for the months ended . We expect operating margin and Adjusted Operating Margin to fluctuate over time, particularly in periods when substantial incremental compute is delivered to us and we incur the associated expenses. See the section titled Non-GAAP Financial Measures for a description of and information about Adjusted Operating Loss and Adjusted Operating Margin, as well as a reconciliation of these measures to the most directly comparable financial measures calculated in accordance with GAAP.

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At the foundation of our business is our family of four Claude model classes Mythos (including Fable), Opus, Sonnet, and Haiku each optimized for a different point on the capability-cost-speed curve. Our frontier models allow customers to match the right model to each use case. Customers typically use higher-capability models for complex reasoning, coding, and agentic workflows where greater model intelligence is required. The model tier a customer uses and the volume of usage are the primary drivers of customer spend. We establish a direct contractual relationship with the customer and offer customers the flexibility to purchase directly from us or through our CSPs, CSP marketplaces, and many customers utilize both channels.

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Depending on the model, platform, or applications chosen by our customers, they enter into pay-for-intelligence pay-for- intelligence pricing, subscription-based pricing, or both. For example, when subscribers approach or exhaust the usage capacity included in their tier, they may upgrade to utilize a higher subscription tier or purchase more intelligence directly, creating a seamless bridge from subscription access to incremental pay-for-intelligence revenue.

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Consumption-based revenue increased from $288 million, or 75% of total revenue, for the year ended December 31, 2024, to $3.8 billion, or 83% of total revenue, for the year ended December 31, 2025.

Consumption-based revenue increased from $288 million, or 75% of total revenue, for the year ended December 31, 2024, to $3.8 billion, or 83% of total revenue, for the year ended December 31, 2025. Subscription-based revenue increased from $98 million, or 25% of total revenue, to $789 million, or 17% of total revenue, over the same period. The higher increase in consumption-based revenue reflects the growth in customers with $100,000 or more of revenue on a TTM basis. For 2023, our revenue of $41 million was substantially all consumption-based, reflecting our early operations. We expect consumption-based revenue to continue to represent the substantial majority of total revenue for the foreseeable future.

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We serve enterprises of all sizes and across industries, in nearly every region of the world. As of , of the Fortune 10, of the Fortune 500, of the Global 2000, AI-native startups, research institutions, and mission-driven organizations that are working on some of the world s most consequential problems use Claude. Customers realize value from Claude in three primary ways more productive employees, faster processes, and embedded intelligence in their products. As enterprise customers push Claude into increasingly complex workflows, we engage with them to understand their evolving requirements, which helps shape our product roadmap. This feedback loop deepens customer relationships with each release and makes Claude more capable for all customers.

Software developers have been core to our strategy they are simultaneously the architects of applications built on our platform, and they encode their workflows and institutional knowledge into Claude-powered agents. Developers have been our earliest adopters and often most discerning users they apply models to unanticipated use cases quickly and surface the edge cases that sharpen each successive generation of Claude models. Claude Code is now embedded in software development workflows across major organizations, with these relationships often beginning with individual engineers who recognized the value of autonomous coding agents and accelerated adoption across their enterprises.

Developers have been our earliest adopters and often most discerning users they apply models to unanticipated use cases quickly and surface the edge cases that sharpen each successive version of a Claude model. Claude Code is now embedded in software development workflows across major organizations, with these relationships often beginning with individual engineers who recognized the value of autonomous coding agents and accelerated adoption across their enterprises .

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Cloud Partnerships. Claude is accessible on all three major cloud platforms, AWS, GCP, and Azure, allowing enterprises to deploy Claude within the infrastructure, security perimeters, and commitment structures they already operate. Our These CSPs extend our reach through their large combined field teams that are empowered to sell our platform and applications, accelerating market adoption at a scale we believe would be difficult for any single organization to directly replicate. We have a joint go-to-market approach with our these CSPs through which we establish direct contractual relationships with our customers while paying a fee to each CSP. This structure allows us to foster stronger commercial alignment with our these CSPs, avoid channel conflict, set pricing, and realize the financial benefits of ongoing compute efficiency improvements.

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ServiceCo Holdings, Inc. Ode with Anthropic. We recently launched an AI services company in partnership with leading private equity firms to bring Claude s capabilities to mid-sized companies across sectors, building solutions on the Claude Platform and supporting customers over the long term. ServiceCo Holdings, Inc. Ode with Anthropic will partner closely with our Applied AI and go-to-market teams.

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This can be seen in the trajectory of our model family adoption. Successive generations versions of our most intelligent generally-available generally available model have delivered meaningfully greater intelligence and capabilities. With each release, we have seen most customers upgrade to utilize the latest version of our models, reinforcing the direct relationship between frontier model quality and revenue generation. As we deliver more capable models, customers have expanded their usage into new use cases and increased their spending accordingly.

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Our ability to continue advancing intelligence at the frontier depends on securing sufficient compute for research, model development, serving customers, and internal usage of Claude. Our ability to leverage heterogenous heterogeneous compute helps us to secure compute at sufficient scale and optimal price-performance . If any of our chip partners are unable to deliver committed compute or experience delays, or if we need to procure incremental compute, we benefit from having a range of partners as opposed to being overly reliant on any one partner. The availability, timing, and cost of compute are subject to factors largely outside our control, including the concentration of chip and memory manufacturing among a limited number of suppliers and geographies, constraints on the availability of power and data center capacity, and evolving export controls. We have historically operated in a compute-constrained environment in which demand for compute has exceeded the supply available to us. In addition, certain of the CSPs on which we rely for compute are also strategic investors in us, are our customers, and offer AI models and services that compete with ours. These multifaceted relationships could give rise to conflicts of interest or competitive dynamics that adversely affect our access to compute, its cost, or the prioritization of our workloads.

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Optimizing the utilization of our compute is equally important to our success in satisfying customer demand.

Optimizing the utilization of our compute is equally important to our success in satisfying customer demand. We continuously direct compute to its highest-value use case, balancing the need to advance research and model development with the need to serve our customers and support internal usage of Claude. We rely on compute efficiencies, such as the discovery of compute multipliers, to get the most out of the compute that we procure. Our ability to effectively direct compute and drive compute efficiencies will be an important determinant of both our pace of innovation and our financial results over time.

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We must make significant investments in compute for research, model development, serving customers, and internal usage of Claude. Our compute requires substantial upfront commitments and ongoing payments as the compute becomes available to us. Our compute commitments will continue to grow as we scale our business. Our ability to secure sufficient compute is directly linked to our ability to access capital on favorable terms, such as through equity or debt financing or cash flow from operations.

Our ability to secure sufficient compute is directly linked to our ability to access capital on favorable terms, such as through equity or debt financing or cash flow from operations.

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We expect our ability to execute across each of these dynamics to be a key determinant of our revenue growth in future periods.

Our financial performance depends on the pace and breadth at which AI, and in particular Claude, is adopted across the global economy. While Claude s capabilities already address a wide range of enterprise use cases, we believe that enterprise adoption remains nascent. Three dynamics will shape We believe that our ability to capture this opportunity and drive revenue growth in future periods will depend on three dynamics First, our ability to translate model improvements into accessible products and platform capabilities.

First, our ability to translate model improvements into accessible products and platform capabilities. Since In the beginning of 2026, months ended , we have launched new model versions, platform features, and applications, with released in the first quarter of 2026 alone, 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.

Second, our ability to expand and diversify distribution.

Second, our ability to expand and diversify distribution. A large portion of our growth to date has been self-service and organic. As AI adoption extends further into larger enterprises and more regulated industries, we are investing in direct go-to-market, Applied AI teams, and partnerships across all three major cloud platforms, SIs, consultants, and data platforms. We are also expanding our ability to serve international customers through the growth of regional offices and partnerships in key markets, and we expect international expansion to be a meaningful driver of revenue growth as global demand for AI accelerates. The effectiveness of these investments, and our ability to hire and retain go-to-market go-to- market talent, will be a meaningful determinant of growth.

Third, our ability to land new customers and expand within existing accounts.

Third, our ability to land new customers and expand within existing accounts. The number of customers with $100,000 or more of revenue on a TTM basis grew from approximately 20 as of December 31, 2023, to approximately 200 as of December 31, 2024, and to approximately 1,500 as of December 31, 2025, and to approximately as of . Total revenue from these customers grew from approximately $37 million to $220 million to $3.5 billion to $ and represented 90%, 57%, and 76%, and % of total revenue for the years ended December 31, 2023, 2024, and 2025, and the months ended , respectively.

In addition, as of , we had over customers with $1 million or more of revenue on a TTM basis and over customers with $10 million or more of revenue on a TTM basis.

Despite this growth, our penetration into the largest enterprises remains early. As of , only % of the Fortune 500 and % of the Global 2000 were customers with $100,000 or more of revenue on a TTM basis, which we believe reflects the substantial runway we have to deepen adoption.

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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 generation version releases is inherent to remaining at the frontier of AI development. For example, over in the six months ended June 30, 2026, , we released seven new model versions , and approximately % of our revenue in that six-month -month period was generated by model versions released within the preceding six months. As we have released successive generations 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.

Our customer usage patterns have also typically exhibited seasonality, particularly around holiday periods, when certain workflows slow down and overall customer activity declines. For example, developer commits and feature launches are typically slower during the code freezes that many companies schedule over holidays. However, our historical growth rates may mask the impact of seasonal trends on our results. These dynamics are inherent aspects of a pay-for-intelligence revenue model and may be important determinants of our financial results in future periods.

However, our historical growth rates may mask the impact of seasonal trends on our results. These dynamics are inherent aspects of a pay-for-intelligence revenue model and may be important determinants of our financial results in future periods.

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We maintain a donation matching program through which employees can pledge a meaningful portion of their equity awards or equivalent cash proceeds to a donor-advised fund, and we match those donations with additional shares of our common stock. As of , employees had made pledges under our donation matching program. In the same way the Company Purpose defines how we direct our work as an organization, our donation matching program reflects a parallel commitment by our employees to direct their personal financial value created by that work toward the public good. We view our donation matching program as a meaningful expression of the Company Purpose and a reflection of the values that define our culture. In addition, our co-founders have each pledged to dedicate 80% of their personal equity in Anthropic to philanthropic causes, separate from and not eligible for our donation matching program.

Decisions we make to advance our public benefit purpose initiatives may, in any given period, reduce our revenue, increase our expenses, and lower our Adjusted Operating Margin relative to what we would otherwise report. We expect the cadence and magnitude of resources directed toward our public benefit purpose to remain a factor affecting our results of operations in future periods.

Our public benefit-related expenses, which currently consist primarily of charitable donations under our donation matching program, have not historically been significant. For the year ended December 31, 2025, expenses related to our donation matching program were $541 million, representing approximately 4% of the Company s total operating expenses. However, we expect the cadence and magnitude of resources directed toward our public benefit initiatives to increase in future periods and remain a factor affecting our results of operations.

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We generate revenue as customers use Claude through our platform and applications, including Claude Code, Claude Cowork, and vertical solutions for key industries. Claude is powered by our proprietary Claude model family, a set of frontier models, each with varying levels of capability, cost, and speed. Enterprise E nterprise customers access our intelligence service directly from us or through our CSPs, CSP marketplaces, where we maintain direct contractual relationships with our customers.

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Revenue is recognized when customers use our intelligence service, based on the quantity of tokens consumed.

Revenue is recognized when customers use our intelligence service, based on the quantity of tokens consumed. Subscriptions are generally prepaid on a monthly or annual basis, and are subject to usage limits that reset over short periods, which results in revenue being recognized on a ratable basis over the subscription period, which is generally 12 months or less.

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Compute and infrastructure expenses primarily consist of costs attributable to operating our technology infrastructure, which supports the full range of workloads for our models, including research, model development, serving customers, and internal usage of Claude. These costs primarily consist of compute fees paid to CSPs under committed and on-demand arrangements, including storage, networking, and connectivity, and related support costs, infrastructure costs associated with serving customer workloads through partner channels, colocation facility costs, equipment leases and committed power costs associated with our data center operations, and third-party application infrastructure software costs directly related to our compute operations. Beginning in 2026, as our obligations commence under data center infrastructure leases, these expenses will also include operating lease costs for data center facilities, including right-of-use asset amortization and variable lease costs, as well as depreciation and amortization on owned servers, networking equipment, and related data center assets.

Beginning in 2026, as our obligations commence under data center infrastructure leases, these expenses will also include operating lease costs for data center facilities, including right-of-use asset amortization and variable lease costs, as well as depreciation and amortization on owned servers, networking equipment, and related data center assets. See Note 8 to our consolidated financial statements included elsewhere in this prospectus for a functional presentation of our operating expenses, including cost of revenue, and a description of the workloads underlying the compute and infrastructure expenses within each functional caption. Within that functional presentation, substantially all of the cost of revenue consists of compute and infrastructure expenses associated with customer-serving workloads. Additionally, cost of revenue includes certain personnel- related costs, and allocated overhead costs.

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Bartz settlement consists of the charge we recorded in 2025 to settle the Bartz litigation . See Note 13 to our consolidated financial statements for additional information.

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Other O ther operating and administrative expenses primarily consist of legal settlement expenses, charitable donations expense related to our o ur donation matching program, vendor costs related to data quality and performance evaluation services, information technology and cloud software costs, payment processing and platform fees, facility and occupancy costs, non-income taxes, professional services costs, bad debt expense, and depreciation and amortization related to non-data center assets.

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The following table sets forth our consolidated statements of operations data for the periods indicated (in millions) 2023 2024 2025 Revenue $ 41 $ 386 $ 4,585 Operating expenses Compute and infrastructure 433 2,522 7,326 Personnel (1) 95 454 1,839 Bartz settlement 1,500 Sales, marketing, and partnerships 2 43 568 Other operating and administrative 62 347 2,912 1,412 Total operating expenses 592 3,366 12,645 Operating loss (551) (2,980) (8,060) Interest income 69 106 431 Change in fair value of convertible notes, derivative liabilities, and SAFEs (1,045) (5,434) (34,308) Other expense, net (2) (8) Total other expense, net (978) (5,328) (33,885) Loss before provision for income taxes (1,529) (8,308) (41,945) Provision for income taxes 2 1 20 Net loss (2) $ (1,531) $ (8,309) $ (41,965) ______________ (1) Includes stock-based compensation expense of $29 million , $192 million , and $1.2 billion for the years ended December 31, 2023, 2024, and 2025, respectively.

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The following table sets forth our consolidated statements of operations as a percentage of revenue data for the periods indicated Year Ended December 31, 2023 2024 2025 Revenue 100 % 100 % 100 % Operating expenses Compute and infrastructure 1,056 653 160 Personnel 232 118 40 Bartz settlement Sales, marketing, and partnerships 5 11 12 Other operating and administrative 151 90 64 Total operating expenses 1,444 872 276 Operating loss (1,344) (772) (176) Interest income 168 27 9 Change in fair value of convertible notes, derivative liabilities, and SAFEs (2,549) (1,408) (748) Other expense, net (5) Total other expense, net (2,385) (1,380) (739) Loss before provision for income taxes (3,729) (2,152) (915) Provision for income taxes 5 Net loss (3,734) % (2,153) % (915) % ______________ Note Totals of percentage of revenue table may not foot due to rounding.

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Revenue $ 386 $ 4,585 $ 4,199 1,088 %

(in millions, except for percentages) 2024 2025 $ Change % Change

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Compute and infrastructure $ 2,522 $ 7,326 $ 4,804 190 %

(in millions, except for percentages) 2024 2025 $ Change % Change

Compute and infrastructure

Percentage of revenue 653 % 160 %

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Personnel $ 454 $ 1,839 $ 1,385 305 %

(in millions, except for percentages) 2024 2025 $ Change % Change

Percentage of revenue 118 % 40 %

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Year Ended December 31,

(in millions, except for percentages)

Percentage of revenue

Bartz settlement for the year ended December 31, 2025 increased by $1.5 billion compared to the year ended December 31, 2024. The increase was entirely driven by the preliminary legal settlement for Bartz reached during the period.

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(in millions, except for percentages) 2024 2025 $ Change % Change

Sales, marketing, and partnerships $ 43 $ 568 $ 525 1,221 %

Percentage of revenue 11 % 12 %

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(in millions, except for percentages) 2024 2025 $ Change % Change

Other operating and administrative $ 347 $ 2,912 $ 2,565 739 %

Percentage of revenue 90 % 64 %

Other operating and administrative expenses for the year ended December 31, 2025 increased by $2.6 $1.1 billion, or 739%, 307% , compared to the year ended December 31, 2024. The increase was primarily driven by a $1.5 billion charge related to a preliminary legal settlement for Bartz reached during the period, a $541 million increase in non-cash charges related to our donation matching program, a $165 million increase in vendor costs related to data quality and performance evaluation services, and a $102 million increase in information technology and cloud software costs.

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(in millions, except for percentages) 2024 2025 $ Change % Change

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Other expense, net $ $ (8) $ (8) NM

(in millions, except for percentages) 2024 2025 $ Change % Change

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and 104 more changes in the filing

Business 177 changes

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Anthropic was founded in 2021 by a team of researchers responsible for many of the breakthroughs that define AI. We are first and foremost a research lab our mission is the responsible development and maintenance of advanced AI for the long-term benefit of humanity . This mission directs our focus and, together with our talent and culture, our frontier and safety research, our approach to compute, and our enterprise strategy, forms the core advantages that set us apart. These advantages enable us to continuously advance frontier AI and shape the standards for its safe and responsible development. Our frontier AI, Claude, is delivered to customers through our models, platform, and applications. We believe Claude has experienced the fastest enterprise adoption of any technology in history, with Run-Rate as demonstrated by revenue growing from $0 at commercial launch in the first quarter of 2023 to $ as of for the three months ended . This rapid and growing adoption generates two self-reinforcing self- reinforcing dynamics an improvement loop that advances Claude and an investment loop that helps fund our continued research in frontier AI.

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 this process is just beginning. Claude s capabilities have evolved significantly in recent years, and we expect they will continue advancing rapidly. This will lead not only to an expanding addressable market, but also to an expanding scope of impact on the world. We aim to ensure that impact is as positive as possible.

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Frontier & Safety Research. We believe that sustained, focused research at the frontier of intelligence is required to build the most capable and safest AI systems. Our frontier AI and safety research are deeply integrated and directly shape model design, training, and deployment, helping us produce systems that are more reliable, steerable, and well-suited for real-world deployment, particularly in demanding enterprise environments. We translate our research leadership into innovation velocity, and have consistently released better models, platform capabilities, and applications. Each generation version of a Claude models model is used to build the next, helping to compress development timelines and produce compounding improvements in the speed at which we innovate. We refer to this recursive dynamic as closing the loop, and we believe it is one of the most significant structural advantages in frontier AI.

Our Approach to Compute . We procure compute as a single, fungible pool from multiple strategic partners, and we dynamically direct available compute between workloads based on where the marginal unit is expected to be most productive at any given time. We believe that this approach to compute enables us to deliver superior models, serve more customers, and achieve greater productivity from our compute investment. This compute efficiency creates the foundation for us to advance the frontier of AI capabilities through research and model development, meet growing demand from customers, and provide for internal usage of Claude. We have achieved this through our research breakthroughs and our multi-chip compute architecture across AWS Trainium, Google TPUs, and NVIDIA GPUs, and AMD GPUs, enabling us to orchestrate workloads to optimize for price and performance. With this efficiency, and to support our research, model development, customer demand, and internal usage of Claude, we have committed to spend at least $ in aggregate on compute as of , including $ in 2026, $ in 2027, and $ in 2028, and we expect these commitments will continue to increase .

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The speed and depth of our enterprise customer adoption are powerful evidence of Claude s position at the frontier of AI. Our customer base as of includes Over customers with $100,000 or more of revenue on a TTM basis Over customers with $1 million or more of revenue on a TTM basis Over customers with $10 million or more of revenue on a TTM basis and of the Fortune 500, and of the Fortune 10, and of the Global 2000.

Claude is not a static product that serves a single use case it is frontier AI with rapidly advancing capabilities.

Claude is not a static product that serves a single use case it is frontier AI with rapidly advancing capabilities. We build at three layers of the stack the models themselves, the platform for building on those models, and the applications that place Claude directly in the hands of our users. An advance at any layer compounds across all three layers, accelerating the pace at which we can deliver more capable intelligence.

Today, enterprises rely on Claude to reason, plan, and execute across complex workflows with a level of trustworthiness and judgment that we believe was not previously available from earlier generations of AI.

Today, enterprises rely on Claude to reason, plan, and execute across complex workflows with a level of trustworthiness and judgment that we believe was not previously available from earlier generations of AI. Customers realize value from Claude in three primary ways more productive employees, faster processes, and embedded intelligence in their products. As Claude s capabilities grow, our customers benefit and often broaden their adoption of Claude in new workflows. This expanding role reflects a broader shift in how Claude is used inside the enterprise, from a tool that responds to discrete prompts to what we call a Virtual Collaborator an AI system that works alongside humans to perform complex, multi-step work at professional levels.

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Strengths of Our Financial Model

Much of the diffusion of Claude has been organic. We amplify this with our go-to-market and Applied AI teams, which extend our reach by meeting customers where they are through distribution partnerships that place Claude inside customers existing technology stacks. For example, Claude is accessible on all three major cloud platforms AWS, GCP, and Azure. We have a joint go-to-market approach with our these CSPs through which we establish direct contractual relationships with our customers while paying a fee to each CSP. This structure allows us to foster stronger commercial alignment with our these CSPs, avoid channel conflict, set pricing, and realize the financial benefits of ongoing compute efficiency improvements. Ecosystem partnerships also expand our reach, allowing customers to adopt Claude through SIs, such as Accenture, Deloitte, KPMG, and PwC, consultants, and data platforms, such as Salesforce and Snowflake. We recently launched Ode with Anthropic, an AI services company, in partnership with leading private equity firms to bring Claude s capabilities to mid-sized mid- sized companies across sectors, building solutions on the Claude Platform and supporting customers over the long term.

Strengths of O ur Financial Model

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Long-term investments come with risks, as we must predict compute needs multiple years into the future. However, we believe our compute efficiency positions us to amplify returns if demand is greater than our forecast and can provide financial resilience if demand is lower or arrives later than we anticipate.

However, we believe our compute efficiency positions us to amplify returns if demand is greater than our forecast and can provide financial resilience if demand is lower or arrives later than we anticipate.

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Improvement Loop . Each enterprise deployment of Claude deepens our understanding of the hard problems, edge cases, and domain-specific challenges that matter most to our customers. We use this feedback to improve our models, platform, and applications, resulting in wider deployment and adoption that fuels the next cycle of improvement. This cycle is further reinforced by Claude itself each new generation of our models directly accelerates development of the next. Many aspects of the training and safety testing of subsequent model generations now involve Claude, including 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 unit of compute. More capable models help our researchers discover these improvements faster in turn, more compute multipliers enable us to train even more capable models and deploy them more efficiently. These gains compound with each cycle, continuously accelerating our pace of development and strengthening our leadership position.

Improvement Loop . Enterprise deployments of Claude deepen our understanding of the hard problems, edge cases, and domain-specific challenges that matter most to our customers. We improve our models, platform, and applications based on feedback from customers using our offerings in production across their businesses. These improvements result in wider deployments and adoption that fuel the next cycle of improvement.

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The Opportunity Ahead

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. Claude s capabilities have evolved significantly in recent years, and we expect they will continue advancing rapidly. This will lead not only to an expanding addressable market, but also to an expanding scope of impact on the world. We aim to ensure that impact is as positive as possible. Our core advantages, the speed and scale of enterprise adoption of Claude, and our unprecedented financial model enable us to continue investing aggressively to build an AI-enabled future that benefits humanity broadly.

Claude Is Accelerating Our Ability to Close the Loop

Claude has itself 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 unit of compute. More capable models help our researchers discover these improvements faster in turn, more compute multipliers enable us to train even more capable models and deploy them more efficiently.

A clear manifestation of this dynamic is the deployment of Claude Code, our agentic coding product, across our engineering organization. As our models have advanced, Claude 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 our engineers increasingly directing and reviewing its work rather than writing code line by line. As a result, the average daily volume of code contributed to our codebase per engineer in the three months ended June 30, 2026 was approximately times the average daily volume in the year ended December 31, 2024.

Approximately % of code contributed to our codebase as of was 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 % in June 2026, up percentage points in six months, based on our internal benchmarks.

The impact of Claude extends beyond research and engineering. From December 31, 2024 to , our headcount grew approximately %, while our revenue for the months ended grew approximately times compared to our revenue for the year ended December 31, 2024. We believe this operating leverage has been driven by the use of Claude throughout our organization. Across our research, engineering, go-to-market, and operations teams, Claude allows each of our employees to leverage a team of agents in their daily work, enabling a comparatively small team to take on work that would traditionally require a far larger one by multiplying the impact of each person. The expansion of our own use of Claude reflects this leverage directly. Because each version of Claude is used to help build the next and because Claude makes our teams more productive across the company, these gains compound, continuously accelerating our pace of development and further strengthening our leadership position.

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These advances have followed empirical Scaling Laws, the finding that AI model performance improves predictably with increases in model size, training data, and compute. The foundational paper, Scaling Laws for Neural Language Models , whose lead authors (including Dario Amodei, Jared Kaplan, Sam McCandlish, and Tom Brown) later co-founded Anthropic, was critical in establishing these relationships and helped guide 2 See the wave section titled Industry, Market and Other Data for a description of modern LLMs. how we calculate annual economic value attributable to knowledge workers globally.

3 IDC, The Economic Impact of AI 2026 Edition, doc # US54452226, April 2026.

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

Tom Brown) later co-founded Anthropic, was critical in establishing these relationships and helped guide the wave of modern LLMs.

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Macroeconomic Impact and the Opportunity Ahead

We expect AI to enhance human capabilities, enabling broader access to and participation in knowledge work, and to make all types of work more efficient and effective. We believe AI will transform some industries more rapidly than others, with effects most observable in software development and increasingly in other knowledge work. IDC estimates there will be over one billion knowledge workers globally in 2028 whose productivity we believe could be dramatically improved with AI. 2 Over a longer time frame, we expect AI to accelerate fundamental advances in a wide array of fields. While it is still early to accurately forecast the full potential AI will have across the economy, IDC has estimated that investments in AI solutions and services are projected to yield a cumulative global economic impact of $22.5 trillion by 2031. 3 We believe the impact could be even greater. AI that truly delivers on its potential with the risks well managed could significantly accelerate global growth and dramatically improve quality of life for people around the world.

Our Near-Term Opportunity Transforming Cognitive Work

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 that work, including accelerating scientific discovery and helping businesses grow. Knowledge workers are the leading edge of this opportunity. We believe the work they perform represented more than $40 trillion in annual economic value globally in 2025, 2 and with frontier AI s potential to make knowledge workers meaningfully more productive, our near-term opportunity is to expand this value and accelerate GDP 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 . 3 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. 4 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 .

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 over hours and days, coordinating thousands of agents in parallel to carry out sophisticated planning, long-form research, and self- correcting analysis. Claude can also turn open-ended tasks into finished, high-quality deliverables work that was beyond the reach of frontier AI only a short time ago. These capabilities enable Claude to transform the largest sectors of the global economy, including software development, business and professional services, cybersecurity, financial services, and healthcare and life sciences, driving meaningful gains in productivity and output for the organizations that deploy it.

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. Ultimately, we believe frontier AI will help drive global GDP growth itself. 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 the incremental value that frontier AI generates as it expands the global economy.

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While the potential benefits of AI are extraordinary, its rapid advancement will impact the economy and society in ways that could create significant dislocation, particularly if the technology is not developed and deployed responsibly. For example, bad actors have conducted high-profile cyberattacks using AI to discover vulnerabilities, generate malware, and evade detection tools. AI deployed at scale also has the potential to concentrate power and wealth in ways that could harm society and destabilize the geopolitical order. In addition, misaligned systems could corrupt institutional decision-making, cause large-scale harm through opaque or unsteerable behavior, and erode the trust that makes complex economic and social systems function. We believe that frontier AI companies must invest in safety to help ensure the responsible diffusion of this technology. We also believe that safety and ethical behavior resonate particularly strongly with large enterprises, where rigorous safeguards, predictable model behavior, and demonstrable accountability have become decisive criteria in selecting an AI partner for mission-critical workloads.

We believe that frontier AI companies must invest in safety to help ensure the responsible diffusion of this technology. We also believe that safety and ethical behavior resonate particularly strongly with enterprises, where rigorous safeguards, predictable model behavior, and demonstrable accountability have become decisive criteria in selecting an AI partner for mission-critical workloads.

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Anthropic was founded in 2021 by a team of researchers responsible for many of the consequential breakthroughs that have defined the frontier of AI. They share a conviction that safety must be a foundational 2 IDC, Worldwide Developer Population Growth Forecast, 2025 2029, doc # US53789825, September 2025.

3 IDC, The Economic Impact of AI 2026 Edition, doc # US54452226, April 2026.

Anthropic was founded in 2021 by a team of researchers responsible for many of the consequential breakthroughs that define frontier AI. They share a conviction that safety must be a foundational principle for building Powerful AI. We have a corporate structure consistent with that objective, incorporating as a Delaware PBC and implementing the LTBT with an explicit mandate to advance our mission. Further, we publish our safety methodologies, model evaluations, and our Responsible Scaling Policy because we believe transparency is a prerequisite for trust, and trust is a prerequisite for the responsible deployment of frontier AI at scale.

Every employee at Anthropic works towards the same mission, and that mission guides every decision from model architectures, to product strategy, to partnership terms, and to how we direct research and compute. We do not pursue projects based on commercial opportunity or research novelty alone our efforts must advance our mission. When difficult tradeoffs arise between commercial gain and safety or trust, we have a clear answer. 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 believe this clarity of mission reduces friction, accelerates decision-making, and fosters a culture where safety and capability are pursued with equal intensity.

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 believe this clarity of mission reduces friction, accelerates decision-making, and fosters a culture where safety and capability are pursued with equal intensity.

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We believe we have assembled the deepest concentration of AI research talent in the world, including many of the researchers who pioneered the techniques that underlie modern LLMs. Our founding team originated several foundational advances in AI, including Scaling Laws, GPT-2 and GPT-3, RLHF, and mechanistic interpretability. Our conviction that safety and capability must advance together helps us attract and retain world-class talent, including researchers and engineers. For example, the vast majority of the members of our technical staff who we have joined Anthropic remain with us as of , including approximately experienced less than % regrettable attrition of our first 50 technical hires. employee base overall in the TTM period ended .

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Our Model Research and Compute Performance Are Co-designed from the Start . Architectural decisions are made with compute efficiency in mind from day one. Our model architecture and inference infrastructure are designed in a single unified process. AI researchers and inference infrastructure engineers share planning cycles, reporting lines, and performance targets. This approach enables us to develop models model versions that are both more capable and more efficient compared to prior generations. versions.

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Our Post-training Investment Is Purpose-Built for Agentic Capabilities . Post-training has grown from a finishing step into a major research investment, reflecting the market shift from assistants to agents. This is where our models hone their ability to write code, use tools, follow instructions, and operate software on a user s behalf. We train against complete, realistic tasks whose complexity reflects real professional work.

This is where our models hone their ability to write code, use tools, follow instructions, and operate software on a user s behalf. We train against complete, realistic tasks whose complexity reflects real professional work.

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In addition to enabling more capable and efficient models, our multi-chip compute architecture across AWS Trainium, Google TPUs, and 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.

Our compute pool is fungible by design. We have made substantial engineering investments to build the internal orchestration, scheduling, and capacity management systems that allow accelerator capacity, regardless of provider, chip architecture, or physical location, to be directed across workloads. The same infrastructure that serves customer workloads is also directed to research, model development and , a nd internal usage of Claude. These systems manage the underlying hardware as a unified capacity pool, monitor utilization and demand across that pool, and direct compute to wherever it is expected to be most productive at any given time.

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4 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 that is needed to function more accurately and reliably. They can navigate and use internal tools and execute 5 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 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. 4 5 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.

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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We believe the trajectory for Virtual Collaborators is clear and accelerating. Each generation of Claude models has expanded along multiple dimensions the depth and rigor of their reasoning, the duration and autonomy of the work they can sustain, the complexity of tasks they can complete, and the volume of useful output they can produce. Capability evaluations and benchmarking have shown substantial improvements across Claude model versions in the duration and complexity of tasks that can be completed reliably, with more recent model versions demonstrating particularly significant advances. We are building towards a world where Claude can be given open-ended tasks lasting hours, days, or weeks, and then execute them autonomously with the judgment and initiative of a brilliant colleague. Ultimately, millions of Virtual Collaborators can run in parallel, each working independently on unrelated problems or collaborating seamlessly on a shared objective. The result is something without historical precedent what we refer to as a country of geniuses in a data center, available to every organization and sector of the economy.

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We believe customers choose Claude to solve their biggest problems. They come to us for access to frontier AI across our platform and applications that delivers deliver real-world outcomes. We see this conviction in customer behavior enterprises consistently upgrade to utilize our latest and most capable models as soon as they are released, gravitating to the most advanced option for their highest-value work. Our customers see us as a long-term partner for both the frontier capabilities we offer today and the innovation they expect to see in the future.

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Our commitment to our mission instills confidence in our customers to choose Claude as a trusted partner. Our investments in alignment research, interpretability, cybersecurity, and enterprise-grade controls aim to deliver the predictability and dependability that organizations need in customer-facing and regulated contexts. This trust expands the scope of work that enterprises delegate to Claude and the systems into which they integrate Claude. Enterprises are aligning their strategies around Claude with confidence that we are building frontier AI to serve their needs for the long term.

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We seek to bring Claude to every corner of the economy at whatever level of autonomy each task demands, from a single suggestion to an agent that runs unsupervised for hours. Achieving this goal requires more than a capable model. The most demanding deployments need intelligence that is reliable, connectable, and deployable within the specific data environments, regulatory constraints, and workflow patterns of each organization. The limiting factor for a use case may not just be intelligence, but connectors that do not exist, governance frameworks not yet written, and orchestration logic that must be built from scratch. Our platform closes seeks to close these gaps, and we believe the breadth and speed with which we have done so is a primary reason customers build on Claude.

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As enterprises adopt our platform and applications, we become deeply embedded in their businesses over time.

As enterprises adopt our platform and applications, we become deeply embedded in their businesses over time. Software developers build applications and internal tools on our platform, and they encode their workflows and institutional knowledge into Claude-powered agents. Organizations integrate Claude into essential business processes. Each additional integration deepens the relationship and increases the value customers derive from the platform. This dynamic extends beyond our direct customers. AI-native companies such as have built products on Claude that reach millions of end users. This reach can increase awareness of Claude, which we believe drives adoption far beyond our direct customer base. When such end users experience Claude, it can build preference and familiarity that travel with users across roles and organizations.

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Cloud Partnerships . Our cloud partnerships, and the way we structure them, provide three distinct advantages. First, Amazon, Google, and Microsoft each host Claude natively on their respective cloud platforms, AWS, GCP, and Azure, allowing enterprises to deploy Claude within the infrastructure, security perimeters, and commitment structures they already operate. Second, our these CSPs extend our reach through their large combined field teams that are empowered to sell our platform and applications, accelerating market penetration at a scale we believe would be difficult for any single organization to directly replicate. Third, we have a joint go-to-market approach with our these CSPs through which we establish direct contractual relationships with our customers while paying a fee to each CSP. This structure allows us to foster stronger commercial alignment with our these CSPs, avoid channel conflict, set pricing, and realize the financial benefits of ongoing compute efficiency improvements.

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ServiceCo Holdings, Inc Ode with Anthropic . We recently launched an AI services company in partnership with leading private equity firms to bring Claude s capabilities to mid-sized companies across sectors, building solutions on the Claude Platform and supporting customers over the long term. ServiceCo Holdings, Inc. Ode with Anthropic will partner closely with our Applied AI and go-to-market teams.

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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 included of the Fortune 500. 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 generation version of a Claude models 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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