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conduct  and  misalignment,  which  could  adversely  affect  our  business,  financial  condition,  and  results  of 
operations.

We are also extending our models, platform, and applications into scientific, biomedical, and physical domains. 
For example, we offer Claude Science, an agentic research environment that connects our models to scientific 
databases,  third-party  and  open-source  scientific  models,  and  customers’  own  computing  infrastructure,  and 
we have released, initially as a limited research preview, an open protocol that enables our models to monitor 
and control laboratory instruments, robotic systems, and other physical hardware. We also conduct research on 
the  ability  of  our  models  to  operate  robotic  systems.  When  our  models  direct  physical  equipment,  errors, 
misaligned  behavior,  adversarial  manipulation,  or  misuse  could  result  in  bodily  injury,  property  damage, 
damage  to  costly  equipment  or  experimental  materials,  or  the  release  of  hazardous  substances,  and  such 
consequences  may  be  immediate  and  irreversible.  Safety  interlocks,  human-in-the-loop  controls,  and  access 
restrictions  we  implement  may  prove  inadequate,  and  because  certain  of  these  components  are  open  source, 
third parties may deploy them with hardware, or in configurations, that we have not evaluated or from which 
our safeguards have been removed. Connecting increasingly capable models to laboratory automation may also 
heighten governmental and public concern regarding the potential for AI systems to accelerate the development 
of dangerous biological, chemical, or other materials, which could result in additional regulatory restrictions on, 
or scrutiny of, these offerings.

In addition, maintaining comprehensive records of the actions, decisions, and reasoning of our agentic systems 
across  complex,  multi-step  workflows  is  technically  challenging,  particularly  when  agents  interact  with  third-
party systems outside our control, and any inability to provide adequate audit trails could impair our ability to 
investigate  incidents,  respond  to  regulatory  inquiries,  or  defend  against  liability  claims.  These  risks  are 
heightened  by  the  fact  that  we  use  our  own  agentic  offerings,  including  Claude  Code,  as  critical  tools  in  our 
internal  development  processes,  such  that  any  errors,  vulnerabilities,  or  degradation  in  these  products  could 
directly  impair  our  ability  to  develop  and  deploy  future  models  and  products.  As  we  increasingly  leverage 
Claude to accelerate progress within Anthropic, our organization may lose visibility across certain processes in a 
way that may become difficult for any individual or group of humans to understand and control. Because our 
models write a large and growing share of our code and increasingly perform our research, including our safety 
research,  misaligned  behavior  in  internal  use  could  undermine  that  work,  introduce  vulnerabilities  into  our 
systems  or  into  the  training  of  future  models,  or  result  in  models  operating  outside  of  our  intended  controls. 
Our monitoring does not cover all of our internal and external deployments and may not detect subtle or diffuse 
forms of such behavior.

Any of the foregoing model behaviors, whether in testing or in real-world deployments and whether involving 
our models or those of others, could result in safety incidents, reputational harm, increased regulatory scrutiny, 
or  liability,  any  of  which  could  adversely  affect  our  business,  financial  condition,  and  results  of  operations. 
Furthermore, while we continue to design and develop more sophisticated techniques to evaluate the safety and 
alignment  of  our  models,  the  capabilities  of  our  models  could  advance  at  a  rate  that  initiates  the 
implementation  of  enhanced  safety  and  testing  measures  as  we  have  described  in  our  Responsible  Scaling 
Policy. If our evaluations, monitoring, or reviews indicate risks that we believe warrant further testing, we have 
in the past, and may in the future, delay, restrict, or decline to proceed with the release of a model. For example, 
following incidents in 2026 in which our models gained unauthorized access to real computer systems during 
pre-release  cybersecurity  evaluations,  we  suspended  certain  of  those  evaluations,  as  well  as  higher-risk 
reinforcement learning environments. For more information about our approach to model releases, please see 
the section titled “Business—Safety Research is Core to Our Business—Our Approach to Model Releases.” Any 
decision  to  delay  or  restrict  a  release  could  adversely  affect  our  competitive  position  and  customer 
relationships, and adversely affect our business, financial condition, and results of operations.

We  may  be  exposed  to  liability,  litigation,  and  regulatory  action  arising  from  harms 
associated  with  our  AI  models,  and  our  contractual  protections  and  insurance  may  be 
insufficient, any of which could adversely affect our business, financial condition, and results 
of operations.

Harms  associated  with  our  AI  models,  whether  arising  from  misuse  by  third  parties,  from  our  models’  own 
behavior, or from their deployment in high-risk or sensitive contexts, may expose us to liability, litigation, and 
regulatory  action,  including  under  theories  that  remain  unsettled  as  applied  to  AI,  and  our  contractual  and 
insurance protections may prove insufficient.

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