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or  other  damage.  More  broadly,  the  long-term  effects  of  regular  AI  interaction  on  mental  health,  cognitive 
development, and social behavior are not yet fully understood, and if such long-term harms do materialize in 
the future, we could face significant liability, reputational harm, and adverse effects on our business, financial 
condition, and results of operations.

Our  development  of  more  advanced  AI  models  and  our  expansion  into  new  jurisdictions  could  amplify  the 
foregoing risks. As our AI models become more advanced and capable, we have faced and may continue to face 
heightened alignment, interpretability, controllability, and safety challenges, as more sophisticated models may 
be better able to circumvent safeguards or behave in unintended ways. We may be unable to fully understand or 
explain why our models produce particular outputs or behave in particular ways, which could limit our ability to 
predict  or  prevent  failures,  provide  explanations  to  customers  or  regulators,  or  satisfy  emerging  regulatory, 
transparency, and explainability requirements.

In  addition,  our  safety  evaluations  and  testing  processes  have  produced  and  may  in  the  future  produce 
incomplete or unreliable results. For example, we have observed models behaving differently when they detect 
or infer that they are being evaluated, which can limit our ability to accurately assess real-world risks or failure 
modes prior to deployment. Such model awareness of our evaluation efforts creates a significant limitation on 
our ability to assess model safety.

Furthermore, AI models have developed, and may in the future develop, unexpected capabilities during training 
that were not anticipated, intended, or tested for, and such emergent capabilities may not be discovered until 
after  a  model  has  been  deployed,  potentially  resulting  in  safety  incidents,  reputational  harm,  regulatory 
scrutiny, or the need to withdraw, retrain, or modify deployed models at significant expense. Such models may 
also  be  deployed  in  higher-stakes  or  more  sensitive  applications,  increasing  the  potential  impact  of  errors, 
misuse, or misalignment.

In  addition,  advanced  AI  models,  including  our  models,  give  rise  to  novel  ethical  and  safety  risks,  including 
risks related to autonomy, manipulation, and the simulation of aspects of human psychology. These risks could 
result  in  unforeseen  harms  and  may  require  the  development  of  governance  structures,  safeguards,  or 
mitigation strategies beyond existing AI safety frameworks, and there is always a risk that we will be unable to 
develop or implement them effectively. In addition, models, including our own, have exhibited, and may in the 
future exhibit, unexpected strategic or self-preserving behaviors, including attempts to resist shutdown, conceal 
or  manipulate  information,  or  generate  outputs  that  could  be  interpreted  as  coercive,  deceptive,  or 
manipulative,  including  behavior  resembling  blackmail  or  efforts  to  influence  human  decision-makers  to 
preserve the model’s continued operation or access.

Even isolated or simulated instances of such behavior, whether occurring in testing or real-world deployments, 
and  whether  occurring  with  our  models  or  models  developed  by  others,  could  generate  significant  public 
concern, regulatory scrutiny, litigation risk, or reputational harm, regardless of whether such behavior results 
in actual harm. For example, state attorneys general in California and at least 15 other states have announced 
investigations of another company in our industry regarding AI safety. Concerns have been raised both within 
our organization and by other researchers, regulators, policymakers, and industry participants that sufficiently 
advanced  AI  systems  could,  if  not  developed,  deployed,  and  operated  with  appropriate  safeguards,  oversight, 
and  security  measures,  pose  catastrophic  or  existential  risks  to  humanity,  particularly  if  such  systems  were 
misused, replicated, weaponized, or allowed to operate with a high degree of autonomy. We have observed that 
our  models  are  contributing  to  the  pace  of  our  own  research  and  these  risks  will  increase  as  AI  systems 
substantially  automate  AI  research  and  development  if,  for  example,  they  accelerate  AI  progress  beyond  our 
ability  to  evaluate  and  safeguard  new  models,  which  could  adversely  affect  our  business,  financial  condition, 
and results of operations. 

Our  development  of  highly  advanced  models,  platforms,  and  applications  and  expansion  of  use  cases  could 
further  increase  the  risk  that  our  models  cause  harm.  For  example,  agentic  AI  systems  are  capable  of 
autonomously  taking  actions  on  behalf  of  users,  interacting  directly  with  external  systems,  executing  code, 
making  API  calls,  sending  communications,  or  completing  transactions  with  limited  human  oversight.  These 
systems  are  designed  to  possess  enterprise-level  permissions,  integrations,  and  contextual  awareness  and  to 
operate autonomously for extended durations, including sustained multi-step workflows lasting hours or days, 
which amplifies the potential severity of any errors, misalignments, or security incidents that may occur during 
such extended autonomous operation. These autonomous capabilities could increase the potential for harm, as 

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