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their  products  or  standards  in  a  manner  that  degrades  the  functionality  of  our  models,  platform,  and 
applications  or  gives  preferential  treatment  to  competitive  models,  platforms,  or  applications,  whether  to 
enhance  their  competitive  position  or  for  any  other  reason,  the  interoperability  of  our  models,  platform,  and 
applications with these products could decrease and our business, financial condition, and results of operations 
could  be  adversely  affected.  Further,  if  we  are  not  permitted  or  able  to  integrate  with  these  and  other  third-
party applications in the future, our business, financial condition, and results of operations could be adversely 
affected.

Many of the risks we face are interconnected and may compound or trigger cascading adverse 
effects across our business, which could amplify their overall impact.

Our  business  and  operations  are  subject  to  numerous  risks  and  uncertainties,  as  described  in  this  “Risk 
Factors” section, many of which are interrelated, such that the occurrence of one risk event may exacerbate or 
trigger  additional  risk  events,  and  the  combined  effect  may  be  materially  greater  than  the  impact  of  any 
individual  risk  event  in  isolation.  For  example,  unfavorable  developments  in  intellectual  property  laws  or 
adverse outcomes in copyright or other litigation could require us to modify, retrain, or limit the capabilities of 
our  models,  increasing  costs  and  reducing  the  performance  or  competitiveness  of  our  models,  platform,  and 
applications, which could in turn harm customer demand, revenue, and our reputation and brand. In addition, 
constraints  on  the  availability  or  cost  of  compute  could  similarly  limit  our  ability  to  develop  and  deploy 
advanced models, delay product launches, reduce service reliability, and adversely affect our revenue growth, 
while also affecting customer trust and perception of our offerings. If our revenue growth is limited and we do 
not have sufficient additional sources of capital, we may be unable to acquire necessary compute or invest in 
our  talent  to  remain  at  the  technological  frontier  or  to  support  our  customers,  or  we  may  face  breaches  of 
covenants or defaults under the A&R Credit Agreement and certain of our commercial agreements. If we were 
to breach covenants or default under the A&R Credit Agreement or certain of our commercial agreements, such 
an  event  could  trigger  cross-defaults,  accelerate  repayment  obligations,  restrict  our  access  to  capital,  and 
further  constrain  our  operations  and  growth,  potentially  magnifying  other  financial,  operational,  and 
reputational risks. Because these and other risks may interact in complex and unpredictable ways, particularly 
in  a  fast-moving  industry  characterized  by  technological  change,  evolving  regulation,  and  emerging  business 
models, we may not be able to foresee or effectively manage all potential consequences. If one risk triggers or 
intensifies  others,  or  if  multiple  adverse  events  occur  concurrently  or  in  close  succession,  our  prospects, 
reputation, business, financial condition, and results of operations could be adversely affected.

Risks Related to Privacy, Cybersecurity, and Intellectual Property 

The  application  of  certain  copyright  law  doctrines  and  contractual  interpretations  of  open-
source licenses to modern AI model training are uncertain.

The  application  of  certain  copyright  law  doctrines  and  contractual  interpretations  of  open-source  licenses  to 
modern AI model training has not been authoritatively resolved by U.S. and non-U.S. courts as of this date. We 
have a good faith belief that we own or possess all sufficient legal rights and/or defenses to the training data 
used in or with our AI models, platform, and applications, which is based in part on our belief that our use or 
acquisition of the training data used in or with our AI models, platform, and applications constitutes “fair use” 
of such training data under copyright law doctrine. In addition, copyright laws in some jurisdictions outside the 
United  States  generally  do  not  recognize  fair  use,  and  our  reliance  on  analogous  exceptions  is  subject  to 
evolving  conditions  and  to  adverse  or  unsettled  judicial  interpretation  in  those  jurisdictions.  If  a  court  of  law 
disagrees  with  our  belief  that  our  use  or  acquisition  of  such  training  data  constitutes  fair  use  or  otherwise 
concludes  that  it  violates  copyright  law,  for  example,  by  adopting  the  reasoning  in  Bartz  (as  defined  below), 
including  without  limitation  in  currently  active  litigation  against  us  or  another  company  in  our  industry,  our 
business  may  be  adversely  affected.  In  particular,  adverse  rulings  in  litigation  involving  other  AI  developers, 
regardless  of  the  specific  facts,  data  practices,  or  legal  theories  at  issue  in  those  cases,  could  establish  legal 
precedents  or  interpretations  of  copyright  law  that  apply  broadly  across  the  industry  and  limit  our  ability  to 
train,  develop,  or  deploy  AI  models,  even  if  our  own  data  procurement  and  usage  practices  differ.  Such 
outcomes  could  restrict  access  to  certain  categories  of  training  data,  narrow  the  scope  of  available  legal 
defenses,  or  impose  new  licensing,  compliance,  or  operational  requirements  across  the  AI  industry.  For 
example, if we cannot rely on the fair use doctrine to access copyrighted work for training our models, we may 
be required to pay significant fees to obtain a license to such training data or our ability to further develop our 

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