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CSPs, sometimes referred to as hyperscalers, operate at global scale with significant infrastructure and market 
concentration. 

“customer” refers to a prosumer or an organization that uses our models, platform, or applications. 

“customers  with  $100,000  or  more  of  revenue  on  a  TTM  basis,”  “customers  with  $1  million  or 
more of revenue on a 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.”

“frontier AI” refers to AI systems at the current leading edge of capability and safety, performing at or near 
the highest levels of any existing system across a broad range of tasks.

“hallucinations” refer to outputs in which an AI model generates factually incorrect or fabricated information 
with apparent confidence.

“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.

“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.

“interpretability” refers to research into how and why AI models produce specific outputs, with the goal of 
making model behavior more transparent and verifiable, especially in high-stakes applications.

“knowledge work” refers to complex cognitive work and related professional activities, including obtaining, 
producing,  analyzing,  and  communicating  information  through  tasks  such  as  coding,  drafting  analyses, 
synthesizing research, building financial models, and managing and automating documents and workflows. 

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

“mechanistic  interpretability”  refers  to  a  subfield  of  interpretability  research  focused  on  reverse-
engineering  the  internal  computations  of  neural  networks.  Rather  than  inferring  model  behavior  from  inputs 
and outputs alone, mechanistic interpretability seeks to identify the specific features and circuits within a model 
that  give  rise  to  its  behavior  with  the  goal  of  making  model  reasoning  auditable  at  the  mechanism  level  and 
detecting failure modes, including deceptive or unsafe behavior, that may not surface in standard testing.

“model  weights”  refer  to  the  numerical  parameters  encoding  a  trained  AI  model’s  behavior,  which  are  the 
primary intellectual asset of a frontier AI developer, representing the accumulated investment in compute, data, 
and research.

“multi-agent orchestration”  refers  to  an  architecture  in  which  multiple  AI  agents  collaborate  on  a  shared 
task,  with  one  coordinating  while  others  execute  subtasks  in  parallel,  enabling  more  complex,  long-horizon 
work than a single agent can complete alone.

“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, 
excluding  subscribers  to  our  Claude  Pro  and  Max  plans,  whose  fixed-price  subscriptions  do  not  reflect  the 

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