Background

Questions

1. Can you give us a brief history of the evolution of generative AI tools over the last few years and why they have suddenly become popular in 2022?
2. Within six months of launch, your ARR crossed $15M. Could you help us understand where you found early traction, what customer segments and use cases saw rapid adoption, and what your core growth loop looked like?
3. Did the specificity of your early positioning as a tool for writing Facebook/Google ads help in getting early people attracted to Jasper?
4. What is your core customer profile and how has it trended over the last few months?
5. How does Jasper use GPT-3 foundational model? Do you only use vanilla GPT-3 or build your own fine-tuned models on top? Are there ways in which you are generating content differently from other apps out there?
6. How do you collect training data from user engagement signals and how do you use it for fine tuning your models?
7. Does the model fine tuning happen on OpenAI or your infrastructure? Who owns the fine tuned models? Are they proprietary to you?
8. When users generate content, does some of it come from GPT-3 models and others from Jasper’s fine-tuned models? Is it even the right way to think about it?
9. AI foundational model companies like Midjourney mention that 10% of their cost goes to training and 90% to inference. As an application that sits on top of a foundational model, how is your cost structure different from them and what are the key components?
10. Some critics say that with LLMs like GPT-3, trained on terabytes of data, fine-tuned models will take a long time to add value to users, if at all. Is their understanding of model fine tuning correct?
11. The AI-assisted content generation market is rapidly getting crowded with companies like Copy.AI, Rytr, Writesonic, Writer, and Peppertype.ai. How is Jasper positioned vis-à-vis these other players, and where do you see differentiation coming from?
12. Do you see Jasper as an AI company or a sales/marketing company that happens to use AI?
13. With OpenAI and others making it cheap and friction-free to build AI applications, it’s a common refrain that all incumbents like Microsoft, Google, and Canva need to do is add AI to their existing apps to win this space. What do you think about the competitive threat posed by these kinds of incumbents?
14. Jasper recently released a Chrome extension. Do you see Jasper evolving into a standalone app like Canva/Figma or more of a utility embedded across user workflows like Grammarly?
15. There are different generative AI apps tackling different types of content across text, image, audio, and video. Do you see these boundaries blurring, or is there a constraining function that prevents apps from bundling all of them together? Do you see Jasper evolving to cover more categories beyond text and image?
16. Are you seeing any surprising changes in how folks use Jasper via the Chrome extension?
17. Some critics believe that the generative AI apps are creating generic, low-quality content which is not close to what humans can create. What do these critics get wrong?
18. Emad Mostaque, the CEO of Stability AI, mentioned it took him less than $600k to train Stable Diffusion. As the cost of compute falls, what are the implications for a company like Jasper? Will we see more companies building self-trained models and shifting away from foundation models?
19. As the cost of compute falls, do the foundational model companies like OpenAI, Stability AI and others also face the risk of commoditization? How do open source models change the dynamics in the foundational model layer?
20. What does Jasper’s margin profile look like at scale? Are there scenarios in which scale improves the margins and unit economics?
21. What does Jasper become in the next five years if everything you're doing goes right?