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may  require  us  to  modify  our  models,  platform,  and  applications,  limit  features  or  use  cases,  or  implement 
technical  safeguards  that  could  reduce  the  usability  or  attractiveness  of  our  offerings,  slow  adoption,  and 
adversely  affect  customer  demand  and  revenue.  More  broadly,  as  AI  models  are  increasingly  used  to  develop 
other  AI  models,  the  knowledge  and  techniques  required  to  build  frontier  models  may  become  more  widely 
available  over  time,  including  through  distillation,  unauthorized  access  to  or  theft  of  model  weights,  or  the 
diffusion  of  AI-assisted  research  and  development  capabilities  across  the  industry.  If  the  ability  to  develop 
highly  capable  models  becomes  more  broadly  available,  our  technological  differentiation  could  erode  and 
competition  could  increasingly  depend  on  access  to  large-scale  compute  rather  than  on  model  development 
capability.  In  such  an  environment,  access  to  sufficient  compute  on  economical  terms  could  become  the 
primary constraint on our ability to train and serve competitive models, and our business, competitive position, 
and results of operations could be adversely affected if we are unable to secure compute at the scale, cost, or on 
the timeline we require.

Additionally, open-weight or open-source AI models may capture a portion of the market for certain use cases, 
which could exert pricing pressure on our offerings or lead certain customers to adopt open-source alternatives 
for those use cases.

A significant portion of our customer base consists of startups and technology-oriented enterprises that may be 
more  sensitive  to  adverse  macroeconomic  conditions  and  may  reduce  or  delay  spending  during  a  downturn. 
Because our cost structure limits our ability to rapidly adjust spending, a sustained economic downturn could 
disproportionately disadvantage us relative to larger, better-capitalized competitors. Such competitors may be 
better  positioned  to  absorb  or  withstand  adverse  economic  conditions  or  reductions  in  customer  technology 
spending due to their greater financial resources, diversified product offerings and customer bases, and more 
stable  revenue  streams.  It  could  also  disadvantage  us  relative  to  smaller  startups  with  leaner  cost  structures, 
lower  fixed  overhead,  and  fewer  long-term  contractual  commitments  that  could  help  enable  them  to  adjust 
spending more rapidly than we can. As a result, competitive pressures on our business could intensify, which 
could adversely affect our business, financial condition, and results of operations.

If  we  are  unable  to  differentiate  our  models,  platform,  and  applications  from  competing  offerings  through 
enhanced functionality, superior performance, better safety features and reliability, lower cost, or other factors, 
demand for our models, platform, and applications may decline. The rapid pace of technological change in AI 
means that models, platforms, and applications can quickly become outdated. We must continuously invest in 
research and development to maintain our competitive position, but there is no guarantee that our investments 
will result in models, platforms, and applications that customers prefer over competing alternatives. Moreover, 
we are first and foremost a research lab, and our strategy prioritizes frontier research and model capability. We 
believe the advancing intelligence and capabilities of our models are the principal drivers of their adoption, and 
we have historically prioritized research and model development rather than new products and applications or 
sales and marketing. This approach assumes that model capability will continue to be a meaningful source of 
competitive differentiation. However, as the AI industry matures, the degree of differentiation among advanced 
AI  models  may  narrow.  This  convergence  may  result  from  factors  such  as  overlapping  or  similarly  sourced 
training  data,  the  widespread  adoption  of  comparable  model  architectures,  training  techniques,  and 
optimization  methods,  shared  access  to  computing  infrastructure,  and  the  rapid  dissemination  of  research 
advances across the industry. To the extent competitors are able to achieve comparable model capabilities with 
lower development or deployment costs, our ability to differentiate our models, platform, and applications on 
the basis of performance, functionality, or cost may be diminished. If differentiation among advanced models 
narrows, or if customers increasingly select AI providers on the basis of application features, user experience, 
distribution,  integration  into  products  they  already  use,  or  the  strength  of  a  sales  organization  rather  than 
model  capability,  competitors  that  have  invested  more  heavily  than  we  have  in  those  areas  could  be  better 
positioned  than  we  are.  Responding  to  such  a  shift  could  require  us  to  increase  our  investment  in  product 
development and sales and marketing substantially beyond our current plans, which could take longer or cost 
more than we anticipate and could reduce the compute and other resources available for frontier research, and 
we may not be able to execute such a shift quickly or effectively. See also the section titled “—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.”

If we are unable to compete successfully, our business, financial condition, and results of operations could be 
adversely affected.

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