Jasper AI's Dave Rogenmoser & Saad Ansari on Growing & Maintaining an LLM-Based Company
02/17/23 • 69 min
About this episode
In this episode of Gradient Dissent, Lukas interviews Dave Rogenmoser (CEO & Co-Founder) and Saad Ansari (Director of AI) of Jasper AI, a generative AI company with a focus on text generation for content like blog posts, articles, and more. The company has seen impressive growth since it's launch at the start of 2021.
Lukas talks with Dave and Saad about how Jasper AI was able to sell the capabilities of large language models as a product so successfully, and how they are able to continually improve their product and take advantage of steps forward in the AI industry at large.
They also speak on how they keep their business ahead of the competition, where they put their focus on in terms of R&D, and how they are able to keep the insights they've learned over the years relevant at all times as their company grows in employee count and company value.
Other topics include the potential use of generative AI in domains it hasn't necessarily seen yet, as well as the impact that community and user feedback plays on the constant tweaking and tuning processes that machine learning models go through.
Connect with Dave & Saad:
Find Dave on Twitter and LinkedIn.
Find Saad on LinkedIn.
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💬 Host: Lukas Biewald
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Subscribe and listen to Gradient Dissent today!
👉 Apple Podcasts: http://wandb.me/apple-podcasts
👉 Google Podcasts: http://wandb.me/google-podcasts
👉 Spotify: http://wandb.me/spotify
About this episode
In this episode of Gradient Dissent, Lukas interviews Dave Rogenmoser (CEO & Co-Founder) and Saad Ansari (Director of AI) of Jasper AI, a generative AI company with a focus on text generation for content like blog posts, articles, and more. The company has seen impressive growth since it's launch at the start of 2021.
Lukas talks with Dave and Saad about how Jasper AI was able to sell the capabilities of large language models as a product so successfully, and how they are able to continually improve their product and take advantage of steps forward in the AI industry at large.
They also speak on how they keep their business ahead of the competition, where they put their focus on in terms of R&D, and how they are able to keep the insights they've learned over the years relevant at all times as their company grows in employee count and company value.
Other topics include the potential use of generative AI in domains it hasn't necessarily seen yet, as well as the impact that community and user feedback plays on the constant tweaking and tuning processes that machine learning models go through.
Connect with Dave & Saad:
Find Dave on Twitter and LinkedIn.
Find Saad on LinkedIn.
---
💬 Host: Lukas Biewald
---
Subscribe and listen to Gradient Dissent today!
👉 Apple Podcasts: http://wandb.me/apple-podcasts
👉 Google Podcasts: http://wandb.me/google-podcasts
👉 Spotify: http://wandb.me/spotify
Previous Episode
Sarah Catanzaro — Remembering the Lessons of the Last AI Renaissance
Sarah Catanzaro is a General Partner at Amplify Partners, and one of the leading investors in AI and ML. Her investments include RunwayML, OctoML, and Gantry.
Sarah and Lukas discuss lessons learned from the "AI renaissance" of the mid 2010s and compare the general perception of ML back then to now. Sarah also provides insights from her perspective as an investor, from selling into tech-forward companies vs. traditional enterprises, to the current state of MLOps/developer tools, to large language models and hype bubbles.
Show notes (transcript and links): http://wandb.me/gd-sarah-catanzaro
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⏳ Timestamps:
0:00 Intro
1:10 Lessons learned from previous AI hype cycles
11:46 Maintaining technical knowledge as an investor
19:05 Selling into tech-forward companies vs. traditional enterprises
25:09 Building point solutions vs. end-to-end platforms
36:27 LLMS, new tooling, and commoditization
44:39 Failing fast and how startups can compete with large cloud vendors
52:31 The gap between research and industry, and vice versa
1:00:01 Advice for ML practitioners during hype bubbles
1:03:17 Sarah's thoughts on Rust and bottlenecks in deployment
1:11:23 The importance of aligning technology with people
1:15:58 Outro
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📝 Links
📍 "Operationalizing Machine Learning: An Interview Study" (Shankar et al., 2022), an interview study on deploying and maintaining ML production pipelines: https://arxiv.org/abs/2209.09125
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Connect with Sarah:
📍 Sarah on Twitter: https://twitter.com/sarahcat21
📍 Sarah's Amplify Partners profile: https://www.amplifypartners.com/investment-team/sarah-catanzaro
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💬 Host: Lukas Biewald
📹 Producers: Riley Fields, Angelica Pan
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Subscribe and listen to Gradient Dissent today!
👉 Apple Podcasts: http://wandb.me/apple-podcasts
👉 Google Podcasts: http://wandb.me/google-podcasts
👉 Spotify: http://wandb.me/spotify
Next Episode
Shreya Shankar — Operationalizing Machine Learning
About This Episode
Shreya Shankar is a computer scientist, PhD student in databases at UC Berkeley, and co-author of "Operationalizing Machine Learning: An Interview Study", an ethnographic interview study with 18 machine learning engineers across a variety of industries on their experience deploying and maintaining ML pipelines in production.
Shreya explains the high-level findings of "Operationalizing Machine Learning"; variables that indicate a successful deployment (velocity, validation, and versioning), common pain points, and a grouping of the MLOps tool stack into four layers. Shreya and Lukas also discuss examples of data challenges in production, Jupyter Notebooks, and reproducibility.
Show notes (transcript and links): http://wandb.me/gd-shreya
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💬 *Host:* Lukas Biewald
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*Subscribe and listen to Gradient Dissent today!*
👉 Apple Podcasts: http://wandb.me/apple-podcasts
👉 Google Podcasts: http://wandb.me/google-podcasts
👉 Spotify: http://wandb.me/spotify
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