Jeff Dean & Noam Shazeer — 25 years at Google: from PageRank to AGI

ℹ️ EPISODE INFO

Released

February 12, 2025

Duration

2h 15m

Channel

Dwarkesh Podcast

Watch on YouTube

▶ Open ↗

📝 EPISODE DESCRIPTION

This week I welcome two of the most important technologists in any field. Jeff Dean is Google's Chief Scientist, and through 25 years at the company, has worked on basically the most transformative systems in modern computing: from MapReduce, BigTable, Tensorflow, AlphaChip, to Gemini. Noam Shazeer invented or co-invented all the main architectures and techniques that are used for modern LLMs: from the Transformer itself, to Mixture of Experts, to Mesh Tensorflow, to Gemini and many other things. We talk about their 25 years at Google, going from PageRank to MapReduce to the Transformer to MoEs to AlphaChip – and soon to ASI. 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊𝐒

• Transcript: https://www.dwarkesh.com/p/jeff-dean-and-noam-shazeer
• Apple Podcasts: https://podcasts.apple.com/us/podcast/jeff-dean-noam-shazeer-25-years-at-google-from-pagerank/id1516093381?i=1000691556147
• Spotify: https://open.spotify.com/episode/4atx1POpKIL8WGvdVfdnbb?si=DLn5uQYMQMWKPTTkj5pt_A

𝐒𝐏𝐎𝐍𝐒𝐎𝐑𝐒

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🎤 SPEAKERS

• Noam Shazeer

  guest

• Jeff Dean

  guest

• Dwarkesh Patel

  host

🧾 EPISODE SUMMARY

In this episode of Dwarkesh Podcast, featuring Noam Shazeer and Jeff Dean, Jeff Dean & Noam Shazeer — 25 years at Google: from PageRank to AGI explores jeff Dean and Noam Shazeer Envision AI’s Self‑Improving, Compute‑Hungry Future Jeff Dean and Noam Shazeer reflect on 25 years at Google, from early search and massive n‑gram language models to today’s Gemini and TPUs. They describe how hardware and algorithms have co‑evolved, enabling deep learning, mixture‑of‑experts architectures, massive context windows, and increasingly capable coding and reasoning systems. A major theme is the coming feedback loop where AI designs better AI and hardware, driving rapid progress in algorithms, chips, and large‑scale systems, with huge implications for productivity and global GDP. They also discuss modular, continually‑learning models, inference‑time scaling, multi‑datacenter training, and the need to shape powerful systems safely while exploiting their vast economic and social upside.

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