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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.” As a result, this measure may not precisely correspond to the population of organizations 
that would otherwise be characterized as enterprises, and may over- or understate that population.

Certain success rates and similar AI performance metrics with respect to Claude referenced in this prospectus 
are  measured  using  internal  benchmarks  developed  and  maintained  by  us.  These  benchmarks  reflect  our 
proprietary  testing  methodology  and  are  based  on  tasks,  data  sets,  and  evaluation  criteria  selected  by  us. 
Performance results derived from these internal benchmarks may not be replicable by customers, researchers, 
or other third parties who evaluate our models using different tasks, data sets, evaluation criteria, or computing 
environments. Accordingly, such metrics may differ materially from performance outcomes experienced by any 
particular customer or user in their specific deployment environment or use case, and should not be relied upon 
as  indicative  of  the  results  any  customer  or  user  will  achieve.  Because  AI  performance  benchmarking 
methodologies vary significantly across developers, such performance metrics as we measure them may also not 
be comparable to similarly titled or analogous metrics reported by other AI developers, and direct comparison 
of such metrics across companies may be misleading.

Investors  should  consider  these  metrics  in  light  of  the  assumptions  and  processes  used  in  calculating  such 
metrics  and  the  limitations  as  a  result  thereof.  In  addition,  our  internal  systems  and  tools  have  a  number  of 
limitations, which could result in unexpected changes to our metrics, including the metrics we publicly disclose. 
Further, certain of our operating metrics are based in part on data reported to us by our third-party partners. If 
the  internal  systems  and  tools  we  use  to  track  these  metrics  or  if  the  data  reported  to  us  by  our  third-party 
partners  undercount  or  overcount  performance,  or  contain  algorithmic  or  other  technical  errors,  the  data  we 
report  may  not  be  accurate.  While  our  operating  metrics  are  based  on  what  we  believe  to  be  reasonable 
estimates for the applicable period of measurement, there are inherent challenges in these measurements. We 
may  also  refine  or  change  our  methodology  for  tracking  certain  operating  metrics  from  time  to  time,  to  the 
extent  practicable,  including  to  improve  overall  accuracy  and  alignment  with  management’s  view  of  business 
and operating performance and as a result of acquisitions. Any of these updates may result in changes in certain 
business and operating trends and may impact comparability of these metrics across periods. Investors should 
not  place  undue  reliance  on  these  metrics  as  an  indicator  of  our  future  or  expected  results.  Moreover,  these 
metrics may differ from similarly titled metrics presented by other companies and may not be comparable to 
such other metrics. See the sections titled “Management’s Discussion and Analysis of Financial Condition and 
Results of Operations” and “Business” for more information regarding our operating metrics. 

Our  business  is  subject  to  an  evolving  and  uncertain  regulatory  and  political  environment, 
which  could  increase  our  costs,  restrict  our  operations,  or  adversely  affect  demand  for  our 
models, platform, and applications.

The  development,  deployment,  and  use  of  AI  technologies  are  subject  to  rapidly  evolving  and  increasingly 
complex  laws,  regulations,  and  governmental  scrutiny  across  multiple  jurisdictions.  Governments  and 
regulatory authorities in the United States and in other countries in which we operate or distribute our models, 
platform,  and  applications  have  proposed,  enacted,  or  are  considering  legislation,  regulations,  and  guidance 
relating  to  AI  model  development  and  deployment,  data  usage  and  privacy,  transparency,  safety,  consumer 
protection,  content  generation,  intellectual  property,  cybersecurity,  export  controls,  and  competition.  For 
example,  the  EU  AI  Regulation  2024/1689  (“EU  AI  Act”),  which  came  into  force  in  August  2024  and  will 
become  fully  applicable  following  a  transitional  period,  imposes  a  range  of  obligations  on  AI  systems  and 
general-purpose  AI  models  placed  on  the  market  or  put  into  service  in  the  EU,  including  transparency,  risk 
management, and governance requirements, and provides for significant penalties for non-compliance. In the 
United States, the regulatory framework for AI remains fragmented and uncertain, with evolving federal policy 
initiatives, continued debate regarding the scope and durability of executive actions, and a growing number of 
state-level  laws  and  proposals  addressing  the  development  and  use  of  AI.  For  example,  several  U.S.  states, 
including  New  York  and  California,  have  passed  AI  safety  laws.  More  generally,  proposed  legislation  in  the 
United States contemplates more expansive governmental oversight of AI, including potential nationalization, 
mandatory  shutdown  or  discontinuation  requirements  for  frontier  AI  systems  or  other  strategic  measures  to 
prevent  or  manage  the  development  of  artificial  superintelligence. Further,  in  June  2026,  the  U.S.  federal 

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