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The following are abbreviations, acronyms, and definitions of certain terms used in this prospectus:

“agent loop” refers to the iterative cycle in which an AI agent receives a task, takes an action, such as planning 
a project, running code, or calling a tool, observes the result, and decides its next step, with this cycle repeating 
until the task is complete. The agent loop is the core execution pattern underlying agentic AI behavior.

“agentic AI” refers to AI systems that, with varying degrees of autonomy, can plan, determine, and sequence 
actions  and  execute  them  to  accomplish  defined  objectives,  including  through  interaction  with  external  tools, 
services, datasets, and environments on behalf of a user.

“AI” refers to artificial intelligence, a machine-based system that learns or is trained from data and, for explicit 
or  implicit  objectives  and  with  varying  levels  of  autonomy,  processes  that  data  to  infer  patterns,  generate 
outputs—such  as  predictions,  content,  recommendations,  and  decisions—and  act  upon  physical  or  virtual 
environments.

“AI  accelerators”  refer  to  processors  purpose-built  or  adapted  for  the  large-scale  computations  required  to 
pre-train,  post-train,  refine,  and  run  AI  models.  These  processors  are  significantly  more  efficient  and  faster 
than general-purpose processors for AI workloads.

“API”  refers  to  application  programming  interface,  a  standardized  interface  that  allows  external  software  to 
access  a  system’s  capabilities  programmatically.  AI  model  providers  may  offer  APIs  that  allow  software 
developers to send inputs to an AI model and receive outputs, enabling integration into the software developers’ 
products and workflows.

“Applied AI” refers to a discipline and organizational function focused on embedding AI capabilities directly 
into  production  business  environments  to  address  specific  customer  requirements.  Applied  AI  teams,  which 
include forward-deployed engineers, work with enterprises to design and deploy custom solutions, specialized 
workflows, and domain-specific implementations tailored to a customer’s own systems and processes.

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

“compute”  refers  to  the  computational  processing  resources  used  to  train  and  run  AI  models,  including, 
without  limitation,  specialized  AI  accelerators,  memory,  networking,  storage,  and  power  systems  that  are 
deployed in data centers or other physical computing environments.

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

“computer use” refers to an AI capability that allows a model to observe and interact with a graphical user 
interface, such as clicking, typing, and navigating applications, similar to how a human operator would.

“Constitutional AI” refers to an approach to training AI models using a set of written principles, which we 
refer to as Claude’s constitution, currently organized around four priorities: being broadly safe, broadly ethical, 
compliant with our guidelines, and genuinely helpful. Under Constitutional AI, our models evaluate, critique, 
and  revise  their  own  responses  against  these  principles,  enabling  us  to  define  and  control  model  behavior 
through auditable principles, rather than relying solely on fixed rule-based instructions or human reviewers. 

“context window” refers to the maximum amount of text, code, or other content, measured in tokens, that an 
AI model can process in a given request, encompassing instructions, prior conversation history, user input, and 
the model’s output.

“CSP”  refers  to  a  cloud  services  provider,  a  technology  company  that  owns  and  operates  large-scale  cloud 
computing infrastructure and provides on-demand compute, storage, and networking. CSPs may also serve as 
distribution  channels  through  which  AI  products  and  services  are  made  available  to  customers.  The  largest 

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