Noam Shazeer: How We Spent $2M to Train a Single AI Model and Grew Character.ai to 20M Users | E1055

ℹ️ EPISODE INFO

Released

August 31, 2023

Duration

36m

Channel

The Twenty Minute VC

Watch on YouTube

▶ Open ↗

📝 EPISODE DESCRIPTION

Noam Shazeer is the co-founder and CEO of Character.AI, a full-stack AI computing platform that gives people access to their own flexible superintelligence. A renowned computer scientist and researcher, Shazeer is one of the foremost experts in artificial intelligence (AI) and natural language processing (NLP). He is a key author for the Transformer, a revolutionary deep learning model enabling language understanding, machine translation, and text generation that has become the foundation of many NLP models. A former member of the Google Brain team, Shazeer led the development of spelling corrector capabilities within Gmail, the algorithm at the heart of AdSense. --------------------------------------------------------- Timestamps: (0:00) Intro (00:43) Noam's Google Experience and Introduction to Character (06:18) Character. AI's Vision, Growth, and Ethical Considerations (14:13) Technical and Business Aspects of AI and Machine Learning (23:19) Business Strategies and AI Philosophy (30:05) Quick-Fire Round --------------------------------------------------------- In Today’s Episode with Noam Shazeer We Discuss:

1. Entry into the World of AI and NLP: How did Noam first make his way into the world of AI and come to work on spell corrector with Google? What are 1-2 of his biggest takeaways from spending 20 years at Google? What does Noam know now that he wishes he had known when he started Character?

1. Model Size or Data Size: What is more important, the size of the data or the size of the model? Does Noam agree that “we will not use models in a year that we have today?” What is the lifespan of a model? Does Noam agree that the companies that win are those that are able to switch between models with the most ease? With the majority of data being able to be downloaded from the internet, is there real value in data anymore?

1. The Biggest Barriers: What is the single biggest barrier to Character today? What are the most challenging elements of model training? Why did they need to spend $2M to train an early model? What are the most difficult elements of releasing a horizontal product with so many different use cases? Where does the value accrue in the race for AI dominance; startups or incumbents?

1. AI’s Role on Society: Why does Noam believe that AI can create greater not worse human connections? Why is Noam not concerned by the speed of adoption of AI tools? What does Noam know about AI’s impact on society that the world does not see? --------------------------------------------------------- Subscribe on Spotify: https://open.spotify.com/show/3j2KMcZTtgTNBKwtZBMHvl?si=85bc9196860e4466 Subscribe on Apple Podcasts: https://podcasts.apple.com/us/podcast/the-twenty-minute-vc-20vc-venture-capital-startup/id958230465 Follow Harry Stebbings on Twitter: https://twitter.com/HarryStebbings Follow Noam Shazeer on Twitter: https://twitter.com/NoamShazeer Follow 20VC on Instagram: https://www.instagram.com/20vc_reels Follow 20VC on TikTok: https://www.tiktok.com/@20vc_tok Visit our Website: https://www.20vc.com Subscribe to our Newsletter: https://www.thetwentyminutevc.com/contact --------------------------------------------------------- #NoamShazeer #CharacterAI #HarryStebbings

🎤 SPEAKERS

• Noam Shazeer

  guest

• Harry Stebbings

  host

🧾 EPISODE SUMMARY

In this episode of The Twenty Minute VC, featuring Noam Shazeer and Harry Stebbings, Noam Shazeer: How We Spent $2M to Train a Single AI Model and Grew Character.ai to 20M Users | E1055 explores noam Shazeer on Scaling Character.ai, Cheap Training, and Billion Use-Cases Noam Shazeer, co‑founder and CEO of Character.ai, discusses how his 20 years at Google shaped his philosophy of building full‑stack, consumer-first AI products that can serve billions of people. He explains why Character.ai is pursuing a broad, horizontal use case—“a billion users inventing a billion use cases”—rather than narrow verticals, and how emotional support, entertainment, and companionship emerged organically as core user behaviors. Technically, he outlines how modern neural language models work, why compute budget (not just data or model size) is the primary constraint, and how Character trained a flagship model for roughly $2M in compute. He also shares views on AI’s future, hallucinations as a feature for some applications, privacy, open vs. closed ecosystems, and his personal philosophy on responsibility, religion, and choosing usefulness over short-term fun.

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