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expansion  and  contraction  behavior  this  metric  is  intended  to  capture.  The  Base  Period  is  the  three  months 
ending  a  year  prior  to  the  measurement  date.  The  Current  Period  is  the  three  months  ending  as  of  the 
measurement date. 

“open-weight” refers to an AI model whose trained model weights, or the numerical parameters encoding the 
model’s learned behavior, are publicly released, allowing third parties to download, run, and modify the model, 
subject to the terms of the model’s license, without necessarily providing access to the underlying training data 
or code.

“pay-for-intelligence” or “consumption” refers to our revenue model under which customers pay for their 
usage of our intelligence service, which is metered in tokens and priced according to the model selected.

“persistent,”  in  the  context  of  AI  applications,  refers  to  denoting  data,  state,  or  context  that  is  continuously 
retained  beyond  the  boundary  of  a  single  operation,  session,  or  interaction,  remaining  accessible  for  reuse 
across subsequent steps, sessions, or workflows without requiring reinitialization.

“post-training”  refers  to  the  collection  of  techniques  applied  to  a  pre-trained  model  to  refine  its  behavior, 
including reinforcement learning, supervised refinement methods, and other alignment methods. Post-training 
is  the  stage  in  which  much  of  the  work  to  make  a  frontier  model  helpful,  accurate,  and  safe  is  performed, 
including the application of safety and alignment techniques. 

“pre-training” refers to the initial stage of training an AI model, in which the model learns general patterns 
and  representations  from  large-scale  datasets  of  text,  code,  and  other  data,  typically  by  predicting  the  next 
token in a sequence. The output of pre-training is a base model with broad capabilities that has not yet been 
adapted for any specific deployment context.

“primitives”  refer  to  the  foundational,  modular  building  blocks  of  a  software  system  from  which  more 
complex applications and workflows are constructed. 

“prompt  injection”  refers  to  an  adversarial  technique  in  which  malicious  instructions  are  embedded  in 
content  an  AI  model  is  asked  to  process,  such  as  websites,  documents,  or  tool  outputs,  with  the  aim  of 
overriding or manipulating the model’s intended behavior, typically without the user’s knowledge.

“prosumers” refer to individual users who subscribe to AI tools to perform knowledge work.

“reinforcement learning” refers to a machine learning approach in which a model learns to perform a task 
by taking actions, receiving feedback signals called rewards, and adjusting its behavior to maximize the rewards 
it accrues over time. In contemporary AI development, reinforcement learning is widely used to refine model 
behavior after pre-training, including to improve helpfulness, accuracy, and adherence to safety guidelines.

“RLHF”  refers  to  reinforcement  learning  from  human  feedback,  a  specific  form  of  reinforcement  learning  in 
which human evaluators rate the model’s responses, and those ratings are used as the reward signal to guide 
training.

“Run-Rate  Revenue”  is  an  operating  metric  that  refers  to  the  estimated  annualized  value  of  our  revenue-
generating activity as of a measurement date, calculated as the sum of (i) our consumption-based fees during 
the  trailing  28-day  period  ended  on  the  measurement  date,  multiplied  by  13,  and  (ii)  our  aggregate  monthly 
subscription fees as of the measurement date, multiplied by 12. For purposes of calculating Run-Rate Revenue, 
consumption-based fees and subscription-based fees are derived from customer usage and active subscriptions.

“SIs” refer to systems integrators, or third-party technology and professional services firms that design, build, 
and  deploy  integrated  solutions  for  customers  by  combining  products  and  services  from  multiple  technology 
vendors into unified systems tailored to customer needs. 

“tokens” refer to numeric units into which an AI model converts the data it processes, including text, images, 
audio,  and  video,  the  number  of  which  varies  by  the  model  and  tokenizer  applied.  Tokens  are  the  smallest 
individual units of a language model and can correspond to words, subwords, characters, or bytes. A tokenizer 
is the component of a model that converts customer inputs into the sequence of tokens the model processes and 
then converts the processed tokens into the outputs provided to customers. 

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