In this podcast, Nathan and Nick dive deep into the data requirements for achieving Artificial General Intelligence. They explore the current paradigms, the role of data in approximating intelligence, and the scaling trends for GPT models. The discussion covers various datasets, from email and Twitter to YouTube and genomic data, as they analyze the feasibility of reaching the target of 100 trillion high-quality tokens. While the bull case suggests an abundance of data, the bear case highlights the limits on high-quality data, prompting a fascinating exploration of what makes data good for AI and the potential for AI to generate its own data.

Sponsors
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The Brave search API can be used to assemble a data set to train your AI models and help with retrieval augmentation at the time of inference. All while remaining affordable with developer first pricing, integrating the Brave search API into your workflow translates to more ethical data sourcing and more human representative data sets. Try the Brave search API for free for up to 2000 queries per month at https://bit.ly/BraveTCR

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Plumb is a no-code AI app builder designed for product teams who care about quality and speed. What is taking you weeks to hand-code today can be done confidently in hours. Check out https://bit.ly/PlumbTCR for early access.

Chapters 
(00:00) Introduction
(05:04) Scaling Hypothesis of Intelligence
(07:32) Is There Enough High Quality Data?
(10:19) Algorithms Impacting Data Requirements
(17:42) Sponsor : Omneky
(18:04) Estimating High Quality Token Requirements
(24:07) Astronomy and YouTube Data Scale
(29:42) Genomics Data
(37:58) Sponsors : Brave / Plumb / Squad
(41:16) Code Datasets and Synthetic Data
(45:48) The Bear Case: Quality and Usability of Data
(50:54) Investment Trends and Compute Efficiency
(54:19) Training Run
(57:21) Synthetic Data Generation and Self-Play