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effective, or if we are unable to effectively build, expand, and manage our sales organization and operations, our 
sales  and  revenue  may  grow  more  slowly  than  expected  or  decline,  and  our  prospects,  business,  financial 
condition, and results of operations could be adversely affected.

We  have  a  limited  history  under  our  current  go-to-market  strategy,  and  our  efforts  to  scale 
and evolve our customer acquisition, engagement, and retention model may not be successful.

We  have  a  limited  history  selling  consumption-based  access  and  subscriptions  to  our  models,  platform,  and 
applications under our current business model, and we are continuing to scale our operations and go-to-market 
strategy. Our future success depends in part on our ability to continue attracting, onboarding, expanding, and 
retaining customers under this model, including determining whether, when, and how to target, develop, and 
deepen  relationships  with  individual  customers  over  time.  Scaling  our  operations,  including  hiring,  training, 
and  retaining  additional  sales,  sales  engineering,  and  customer-facing  personnel,  and  evolving  our  go-to-
market strategy may take more time and require more effort than anticipated, and the results of these efforts 
are difficult to predict. Our business and operating models have not been fully proven, and our limited history 
with our current approach makes it difficult to evaluate our prospects, plan for future growth, and forecast our 
results of operations.

In  addition,  our  strategy  and  customer  mix  expose  us  to  competitive  and  execution  risks  as  the  AI  market 
continues to evolve. We have organized our research priorities, our product roadmap, and our go-to-market and 
Applied AI teams around enterprise use cases, and much of the diffusion of Claude to date has been organic—
driven by individual users, including software developers and prosumers, who adopt Claude and often serve as 
internal  champions  for  its  broader  adoption  within  their  organizations—rather  than  by  a  large  direct  sales 
organization.  This  approach  has  allowed  us  to  scale  with  a  smaller  team  relative  to  other  large  enterprise 
technology  companies,  but  it  assumes  that  our  applications  and  distribution  will  be  sufficient  to  deliver  the 
capabilities  of  our  models  to  customers  and  that  individual  adoption  will  continue  to  translate  into 
organizational adoption and expansion. Companies with stronger consumer-facing businesses, larger consumer 
user  bases,  more  established  brands,  broader  distribution,  or  deeper  customer  relationships  may  choose  to 
increase their focus on enterprise customers or enterprise use cases and could leverage their scale, resources, 
and  market  presence  to  compete  more  effectively  with  us.  In  particular,  if  such  competitors  capture  the 
individual  users  from  whom  our  enterprise  adoption  often  originates,  whether  through  their  consumer 
applications, by integrating AI functionality into products those users already use, or otherwise, our pipeline of 
enterprise opportunities could be reduced in ways that a direct sales organization of our current scale may not 
be able to offset. Building a substantially larger sales organization, or shifting investment toward consumer and 
application-layer products to compete for those users, would require significant time and resources beyond our 
current plans and would involve the trade-offs described in the section titled “—We face intense competition, 
and if we are unable to compete effectively, our business, financial condition, and results of operations could be 
adversely  affected.”  Because  we  have  a  limited  operating  history  with  our  current  business  model  and  go-to-
market strategy, we have less experience predicting customer adoption, expansion, and retention patterns, and 
customers may be slower than expected to adopt our models, platform, and applications or expand usage. Any 
failure to successfully scale our operations or effectively execute on and refine our go-to-market strategy could 
adversely affect our business, financial condition, and results of operations.

Sales  to  enterprise  customers  involve  complexities  and  risks  that  could  adversely  affect  our 
business, financial condition, and results of operations.

We derive a large portion of our revenue from sales to enterprise customers. Our ability to attract, retain, and 
expand  relationships  with  enterprise  customers  depends  in  significant  part  on  our  ability  to  meet  their 
generally  heightened  operational,  technical,  security,  reliability,  and  compliance  requirements.  Enterprises 
typically  expect  high  levels  of  system  uptime,  performance  consistency,  data  security,  auditability,  internal 
controls, and scalability, as well as robust support and service levels. These requirements may be heightened for 
our  agentic  offerings,  where  enterprise  customers  may  require  detailed  logs  of  each  action  taken  by  an 
autonomous agent within their environment, including the agent’s reasoning, tool invocations, and interactions 
with  external  systems.  Any  inability  on  our  part  to  provide  such  logs  at  the  level  of  granularity  customers 
require  could  limit  adoption  of  our  agentic  offerings  by  enterprises  or  result  in  customer  attrition.  If  we  are 
unable to deliver models, platforms, and applications that satisfy these enterprise-grade requirements, or if we 
experience  outages,  performance  degradation,  security  incidents,  or  other  operational  failures,  our  ability  to 
retain and expand with enterprise customers could be adversely affected. In particular, as we deploy our Virtual 

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