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Cloud  Partnerships.  Claude  is  accessible  on  all  three  major  cloud  platforms,  AWS,  GCP,  and  Azure,  allowing 
enterprises  to  deploy  Claude  within  the  infrastructure,  security  perimeters,  and  commitment  structures  they 
already operate. These CSPs extend our reach through their large combined field teams that are empowered to 
sell our platform and applications, accelerating market adoption at a scale we believe would be difficult for any 
single organization to directly replicate. We have a joint go-to-market approach with these CSPs through which 
we establish direct contractual relationships with our customers while paying a fee to each CSP. This structure 
allows  us  to  foster  stronger  commercial  alignment  with  these  CSPs,  avoid  channel  conflict,  set  pricing,  and 
realize the financial benefits of ongoing compute efficiency improvements.

Ecosystem Partnerships. Major global SIs have built dedicated Claude practices with teams trained to deploy 
our  platform  and  applications.  Go-to-market  alliances  with  leading  data  platforms  embed  Claude  into  data 
infrastructure  that  enterprises  already  operate,  facilitating  adoption  of  Claude  within  existing  workflows  and 
making it easier for enterprises to realize value from Claude.

Ode with Anthropic. We recently launched an AI services company in partnership with leading private equity 
firms  to  bring  Claude’s  capabilities  to  mid-sized  companies  across  sectors,  building  solutions  on  the  Claude 
Platform  and  supporting  customers  over  the  long  term.  Ode  with  Anthropic  will  partner  closely  with  our 
Applied AI and go-to-market teams.

Together, these approaches have led to a structurally efficient approach to facilitating the diffusion of Claude in 
the  economy,  with  our  sales,  marketing,  and  partnerships  expenses  representing 19%  of  revenue  for  the  six 
months ended June 30, 2026. 

Key Factors Affecting Our Performance

Remaining at the Frontier of AI

We believe enterprises consistently choose the most capable and trustworthy AI systems, making our position 
at the frontier an important driver of our success. Our ability to rapidly grow our revenue is partially dependent 
on our continued delivery of more intelligent and capable models. Our experience is that as we deliver models 
with  meaningfully  improved  capabilities  that  deliver  higher  intelligence  per  dollar  spent,  customers  find  new 
applications for AI across their businesses, accelerating adoption and revenue growth. 

This  can  be  seen  in  the  trajectory  of  our  model  family  adoption.  Successive  versions  of  our  most  intelligent 
generally available model have delivered meaningfully greater intelligence and capabilities. With each release, 
we  have  seen  most  customers utilize  the  latest  version  of  our  models,  reinforcing  the  direct  relationship 
between  frontier  model  quality  and  revenue  generation.  As  we  deliver  more  capable  models,  customers  have 
expanded their usage into new use cases and increased their spending accordingly.

We expect our ability to deliver innovation in intelligence and capability at the frontier to remain a key driver of 
revenue growth in future periods, and maintaining a leading position requires continued investment in research 
and compute, top technical talent, and access to capital sufficient to support our growth trajectory. 

Securing Sufficient Compute and Optimizing its Utilization

Our  ability  to  continue  advancing  intelligence  at  the  frontier  depends  on  securing  sufficient  compute  for 
research,  model  development,  serving  customers,  and  internal  usage  of  Claude.  Our  ability  to  leverage 
heterogeneous compute helps us to secure compute at sufficient scale and optimal price-performance. If any of 
our  chip  partners  are  unable  to  deliver  committed  compute  or  experience  delays,  or  if  we  need  to  procure 
incremental compute, we benefit from having a range of partners as opposed to being overly reliant on any one 
partner.  The  availability,  timing,  and  cost  of  compute  are  subject  to  factors  largely  outside  our  control, 
including  the  concentration  of  chip  and  memory  manufacturing  among  a  limited  number  of  suppliers  and 
geographies, constraints on the availability of power and data center capacity, and evolving export controls. We 
have historically operated in a compute-constrained environment in which demand for compute has exceeded 
the  supply  available  to  us.  In  addition,  certain  of  the  CSPs  on  which  we  rely  for  compute  are  also  strategic 
investors in us, are our customers, and offer AI models and services that compete with ours. These multifaceted 
relationships could give rise to conflicts of interest or competitive dynamics that adversely affect our access to 
compute, its cost, or the prioritization of our workloads.

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