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Our failure to achieve any of the foregoing could prevent us from maintaining our competitive position, cause 
us  to  fall  behind  competitors,  reduce  customer  adoption  of,  or  demand  for,  our  models,  platform,  and 
applications, and impair our revenue growth. Limitations to our revenue growth could, in turn, constrain our 
ability  to  fund  the  substantial  and  increasing  expenditures  required  for  frontier  AI  development,  including 
investments in compute, model training, data acquisition, and safety and security systems, as well as our ability 
to attract, retain, and appropriately compensate highly skilled technical and operational personnel in a highly 
competitive market. Any such constraints could slow our rate of development and innovation, degrade model 
performance  or  reliability  relative  to  competitors,  limit  our  participation  in  industry  standards-setting  and 
regulatory processes, and reduce our attractiveness to enterprises and prosumers. These effects could create a 
self-reinforcing or compounding cycle in which diminished growth limits our capacity to invest in the resources 
necessary  to  remain  competitive,  which  in  turn  could  further  weaken  our  ability  to  sustain  or  increase  our 
revenue growth, making it increasingly difficult and costly to regain technological leadership and operate at the 
frontier  of  AI.  Any  of  the  foregoing  could  adversely  affect  our  business,  financial  condition,  and  results  of 
operations.

Our ability to maintain our position at the frontier of AI depends in part on our use of AI systems to accelerate 
our research processes, a recursive dynamic we refer to as “closing the loop,” in which successive versions of our 
models assist in building the next, with the goal of compounding capability gains over time. Our models have 
been and are increasingly becoming critical tools in designing, training, testing, and optimizing next-generation 
AI  systems.  If  we  fail  to  direct  sufficient  compute  and  research  efforts  to  AI-assisted  development,  or  if  our 
competitors more effectively leverage AI to enhance their own innovation cycles, we risk falling progressively 
further  behind  as  competitors  continuously  use  AI  to  further  enhance  their  own  development  capabilities, 
enabling the creation of increasingly advanced systems at an accelerating pace. As our reliance on our own AI 
systems  to  write  production  code,  conduct  research,  and  build  subsequent  model  versions  deepens,  any 
systemic  flaw,  bias,  or  error  in  our  current  models  could  propagate  into  the  next  versions  of  models  and 
products built using those systems, compounding across successive versions of Claude models in ways that may 
be difficult to detect or remediate. If we are unable to manage the foregoing risks, our ability to deploy the most 
advanced, capable, and useful AI models could be adversely affected.

In addition, our increasing use of AI systems to accelerate our own research and development may cause the 
capabilities  of  our  models  to  advance  more  rapidly  than  our  ability  to  develop,  validate,  and  implement 
corresponding safety, alignment, and security measures. Much of our evidence regarding the pace of such AI-
driven acceleration is derived from lagging indicators, and our ability to forecast or measure the rate at which 
our capabilities are advancing, including any acceleration attributable to our most recent or unreleased models, 
is limited. As a result, we may not identify a significant acceleration in our rate of development until after it has 
occurred. If our capabilities were to outpace our safety, alignment, and security measures, or if we were unable 
to  anticipate  the  pace  of  such  advancement,  we  may  be  unable  to  adequately  assess  or  mitigate  the  resulting 
risks, which could increase the likelihood of safety incidents, regulatory intervention, or reputational harm, and 
could adversely affect our business, financial condition, and results of operations. In light of these uncertainties, 
including limits on our ability to observe the pace at which our capabilities are advancing, we have adopted, and 
may  continue  to  adopt,  precautionary  safety,  alignment,  and  security  measures  to  allow  our  safety  efforts  to 
advance  together  with  model  capabilities.  In  the  past  these  measures  have  included  delaying  the  release  of 
models and limiting their initial availability, such as with Mythos through Project Glasswing, and in the future, 
as  we  learn  more  about  model  capabilities  and  behavior,  they  could  include  additional  measures  that affect 
whether,  how,  and  when  we  develop  or  deploy  new  models  or  capabilities.  We  cannot  predict  with  precision 
when  we  will  determine  that  such  measures  are  necessary,  what  measures  will  be  required,  or  how  long  they 
will remain in place, and as the capabilities of our models advance, such measures may become more frequent 
and  more  costly.  These  measures  could  slow  our  capability  gains  relative  to  competitors  that  do  not  adopt 
similar  constraints,  and  could  adversely  affect  our  competitive  position,  revenue  growth,  and  results  of 
operations.

The  development  of  frontier  AI  models  requires  substantial  investments  of  compute  resources,  capital,  and 
human capital to conduct large-scale training runs that may span weeks or months and cost many millions of 
dollars. There is no guarantee that any given training run will succeed—a run may fail for a number of reasons, 
such  as  failing  to  achieve  the  targeted  capability  or  safety  thresholds,  producing  unexpected  or  undesirable 
behaviors  that  cannot  be  adequately  remediated,  or  yielding  a  model  that  is  ultimately  not  competitive  with 
alternatives available in the market. We would also face the additional cost and delay of initiating a new training 

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