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next-generation models, and we cannot provide assurance that increased investment in compute will result in 
commensurate improvements in model performance.

The market for compute is intensely competitive. We compete for access to compute with some of the world’s 
largest  and  most  well-capitalized  technology  companies,  many  of  which  develop  their  own  competitive  AI 
models and have significantly greater financial resources than us. Many own and operate cloud computing data 
center infrastructure, maintain captive chip manufacturing or design capabilities, have long-standing strategic 
relationships  with  key  hardware  suppliers,  or  have  significant  equity  investments  from,  or  other  financial 
arrangements  with,  such  suppliers.  These  competitors  can  outbid  us  for  scarce  compute,  secure  priority 
allocations  from  chip  manufacturers  and  CSPs,  and  make  long-term  capital  commitments  that  we  cannot 
match.  We  expect  demand  for  advanced  compute  to  continue  to  grow,  and  the  cost  of  compute  could  rise 
significantly,  particularly  if  AI-driven  demand  accelerates  or  if  the  ability  to  build  highly  capable  models 
becomes  more  broadly  available  and  shifts  competition  toward  access  to  advanced  compute.  Additionally, 
numerous  well-funded  AI  companies  are  pursuing  large-scale  AI  model  development,  further  increasing 
demand for limited compute. Certain CSPs from which we obtain substantial compute also develop and offer 
their own AI models and related services. As a result, our commercial relationships with these providers may 
not always be fully aligned with our long-term interests, and if the compute we have access to from third parties 
is  curtailed,  repriced,  or  terminated,  whether  for  competitive  reasons  or  otherwise,  our  business,  financial 
condition, and results of operations could be adversely affected.

In addition to competition from third parties, our ability to secure compute is subject to allocation decisions by 
our CSPs. Although our CSPs are generally subject to contractual commitments with us regarding the provision 
of  compute,  our  ability  to  obtain  such  capacity  remains  dependent  on  those  providers’  operational  execution, 
adherence  to  the  terms  of  such  contractual  provisions,  and  broader  business  considerations,  such  as  capacity 
constraints, internal demand for compute to support their own AI and computing operations, relationships with 
or  contractual  obligations  to  third  parties  (which  may  include  other  AI  companies),  and  risk  management 
considerations. As a result, we may not receive the full amount of compute we seek, or such compute may not 
be  delivered  on  the  timelines  we  require,  even  where  contractual  arrangements  are  in  place.  Additionally,  we 
have  in  the  past  sought  quantities  of  compute  from  CSPs  in  excess  of  what  those  partners  were  willing  to 
provide. If our CSPs reduce, limit, or fail to increase our allocations to keep pace with our growing requirements 
for any reason, we may need to seek alternative compute sources on less favorable terms or timelines, and we 
may  be  unable  to  secure  adequate  alternative  compute,  or  any  at  all.  Any  such  shortfall  could  constrain  our 
ability to train models, serve customers, or maintain our competitive position.

Even if we are able to secure compute, we must still direct it efficiently across the full range of workloads that 
are  a  part  of  our  business,  including  research,  model  development,  serving  customers,  and  internal  usage  of 
Claude.  While  we  manage  our  compute  as  a  single,  fungible  pool  and  direct  available  compute  between 
workloads based on where the marginal unit is expected to be most productive at any given time, there can be 
no assurance that this approach to compute will be the most efficient or that it will support the needs of each of 
our workloads. Because demand for compute has historically exceeded available capacity, any constraint on our 
ability  to  procure,  maintain,  or  expand  that  capacity  could  adversely  affect  multiple  aspects  of  our  business 
simultaneously,  including  our  ability  to  serve  customers  at  the  quality  and  scale  they  expect,  to  train  and 
improve our models, and to develop new products and capabilities. While we actively manage and balance such 
compute capacity across these workloads via our capacity management system, if this system fails to perform as 
intended,  or  if  our  ability  to  dynamically  rebalance  capacity  across  workloads  is  disrupted,  customer-facing 
service  quality  or  model  and  product  development  timelines  could  be  adversely  affected.  There  can  be  no 
assurance  that  we  will  make  optimal  resourcing  decisions,  and  any  significant  inefficiencies,  including  those 
related to deployment and rebalancing, could adversely affect both our near-term results of operations and our 
long-term strategic position. These compute resourcing challenges are heightened by the growth of our agentic 
offerings, which involve multi-step reasoning chains, iterative tool invocations, extended context windows, and 
sustained inference sessions that consume significantly more compute per user interaction than single-turn API 
calls or chat-based interactions. As agentic workloads grow as a proportion of our total compute demand, the 
aggregate compute required to serve our customers could increase disproportionately relative to the associated 
revenue, placing additional strain on our compute workload management decisions.

The majority of our compute is procured under multi-year commitments that are typically entered into well in 
advance  of  the  periods  in  which  that  compute  is  deployed.  Because  different  AI  accelerators  have  varying 

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