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Dwarkesh Podcast

Dwarkesh Patel
China
Artificial Intelligence
Artificial General Intelligence
United States
Reinforcement Learning
Large Language Models
Machine Learning
World War II
Economic Growth
Xi Jinping
Mao Zedong
Covid-19
AI Alignment
Solar Energy
AI Research
Civil War
Automation
Ethics In AI
Great Leap Forward
ARC Benchmark

Deeply researched interviews www.dwarkesh.com

PublishesWeeklyEpisodes130Founded6 years ago
Number of ListenersCategories
ScienceTechnology

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Latest Episodes

Read the transcript here.

Thanks to Mercury for sponsoring this essay!

Mercury just released a new feature called Command, which gives me AI right in my banking platform. And since I use Mercury to run basically my entire business, Command has acce... more

Had Ada Palmer back on – this time to talk about Machiavelli, perhaps the most misunderstood thinker of all time.

Machiavelli cut his teeth as a high-level diplomat for Florence, a position from which he got to closely observe the most important rul... more

YouTube

Economics of AGI episode w Alex Imas and Phil Trammell.

There’s a bunch of important questions about how we deal with AI that only economics can answer.

What is the optimal way to tax and redistribute the wealth that will be generated? How should c... more

New blackboard lecture with Reiner Pope: how do chips actually work - starting with basic logic gates, and working up to why GPUs, TPUs, FPGAs, and the human brain each look the way they do.

Reiner is CEO of MatX, a new chip startup (full disclosure... more

YouTube

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Recent Guests

Alex Imas
Director of AGI Economics at Google DeepMind; Professor of Economics at University of Chicago
Google DeepMind; University of Chicago
Episode: Alex Imas and Phil Trammell – What remains scarce after AGI?
Phil Trammell
Head of Economics at EFAC; Research Scholar at Stanford
EFAC; Stanford University
Episode: Alex Imas and Phil Trammell – What remains scarce after AGI?
Reiner Pope
CEO of MatX, AI chip designer
MatX
Episode: Reiner Pope – Chip design from the bottom up
Eric Jang
AI researcher; former Vice President of AI at One X Technologies; former Senior Research Scientist at Google DeepMind Robotics
One X Technologies; Google DeepMind Robotics
Episode: Eric Jang – Building AlphaGo from scratch
David Reich
Professor of ancient DNA at Harvard
Harvard University
Episode: David Reich – Why the Bronze Age was an inflection point in human evolution
Michael Nielsen
Research fellow at the Astera Institute; author and open science advocate
Astera Institute
Episode: Michael Nielsen – How science actually progresses
Terence Tao
Mathematician, Professor (likely UCLA)
University (inference from Tao's profile)
Episode: Terence Tao – Kepler, Newton, and the true nature of mathematical discovery
Dylan Patel
CEO of SemiAnalysis
SemiAnalysis
Episode: Dylan Patel — Deep dive on the 3 big bottlenecks to scaling AI compute
Ada Palmer
Renaissance historian, novelist, and composer
University of Chicago / Author of Inventing the Renaissance
Episode: Why Leonardo was a saboteur, Gutenberg went broke, and Florence was weird – Ada Palmer

Host

Dwarkesh Patel
Host of the Dwarkesh Podcast

Reviews

4.6 out of 5 stars from 2.7k ratings
  • Not Terence Tao please!

    I am relatively new to this show. It’s one of those that I have meant to get round too but a combination of (1) there are others that I have saved and planned to listen to first and (2) this stuff is heavy going so let me put if off till I’m ready, that stopped me from buckling down.

    And then when I’m just about ready to listen to one of his episodes it turns out to be Terence Tao, the Richard Dawkins of pure mathematics. I could have said the Stephen Hawking of mathematics and it would have c... more

    Apple Podcasts
    2
    sidneyhart
    United States2 months ago
  • Mixed bag

    Interesting guests and topics. Admire the hustle and sweat of Dwarkesh. Some good insights. But sometimes it’s tiring, pedantic and monotonous. And difficult to follow because of the minutia and cadence. But the biggest issue for me is D’s interview delivery. It’s so jumpy, fast and nervous it literally makes me jittery. It takes away from the content. Moderation is needed. I

    Apple Podcasts
    4
    LadyMacbethsHands
    United States2 months ago
  • Excellent Subject Matter five star BUT…

    Unfortunately you speak Californian, too fast, inflection at the end of the sentence. Go to broadcasting school, or hire a speech coach.

    Apple Podcasts
    1
    RaFa my dogs name
    United States2 months ago
  • Talks too fast

    Slow down. Remember that you have an audience who would like to hear what you have to say

    Apple Podcasts
    3
    a speck of stardust
    United States2 months ago
  • Skip Jensen Huang Episode

    Normally the show is intellectually interesting even with disagreements but the entire 2 hrs is Huang calling every single thing out of Dwarkesh’s mouth stupid and illogical but then giving politician answers no real justifications. He sounds like a child and really takes away from any of his so called “expertise”.

    Apple Podcasts
    3
    CuriousHuman3
    United States2 months ago

Listeners Say

Key themes from listener reviews, highlighting what works and what could be improved about the show.

Listeners appreciate the depth and range of guests, though some critique delivery pace and pacing.
Many find the episodes dense but rewarding, with strong signal for those tracking AI and science trends.
High regard for rigorous, well-researched conversations with leading figures.

Chart Rankings

How this podcast ranks in the Apple Podcasts, Spotify and YouTube charts.

Talking Points

Recent interactions between the hosts and their guests.

Reiner Pope – Chip design from the bottom up
Q: How does a systolic array reduce data movement compared to a traditional data-path?
A systolic array keeps a significant portion of the matrix data local to the processing elements, reusing weights within the array and feeding inputs in a streaming fashion, which minimizes off-chip data transfers and lowers energy per operation.
Reiner Pope – Chip design from the bottom up
Q: Why is multiply-accumulate chosen as the natural primitive for AI chips, especially in a matrix multiply context?
Because matrix multiplication is effectively a loop of multiply-accumulate operations, and doing accumulation with higher precision while multiplying low-precision numbers reduces rounding errors and aligns directly with the arithmetic pattern in neural nets.
Reiner Pope – The math behind how LLMs are trained and served
Q: What is the practical latency impact of pipelining in inference?
In inference, pipelining does not change latency itself but reduces memory per rack and enables larger scale-up domains, which can lower effective bottlenecks without altering the raw per-token latency.
Reiner Pope – The math behind how LLMs are trained and served
Q: Is there a lower bound on latency due to memory bandwidth?
Yes, for a given hardware setup there is a latency floor set by the time required to read all the model parameters and related KV caches from memory, which you can't beat just by batching more tokens.
Reiner Pope – The math behind how LLMs are trained and served
Q: What is the dominant factor when you scale batch size for frontier models?
Batch size mainly shifts the balance between compute time and memory fetches; larger batches amortize memory costs and can dramatically reduce cost per token, but they also influence latency depending on the context length and hardware.

Audience Metrics

Listeners, social reach, demographics and more for this podcast.

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Professions
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Frequently Asked Questions About Dwarkesh Podcast

What is Dwarkesh Podcast about and what kind of topics does it cover?

This show features deep-dive conversations with leading technologists, scientists, historians, and thinkers who explore AI, science, technology, and the broader implications for society. Episodes often center on how breakthroughs in AI, computing, or scientific methods unfold, with guests offering rigorous context, historical perspective, and forward-looking analyses. A recurring strength is high-density, research-backed discussion that ranges from technical breakdowns (like AlphaGo-style AI, ML training architectures, and compute bottlenecks) to broader implications for governance, ethics, and culture. Noteworthy is the willingness to host guests from diverse fields—mathematicians, historians, open-science advocates, and AI researchers—to ... more

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Which podcasts are similar to Dwarkesh Podcast?

These podcasts share a similar audience with Dwarkesh Podcast:

1. No Priors: Artificial Intelligence | Technology | Startups
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3. BG2Pod with Brad Gerstner and Bill Gurley
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5. All-In with Chamath, Jason, Sacks & Friedberg

How many episodes of Dwarkesh Podcast are there?

Dwarkesh Podcast launched 6 years ago and published 130 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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What guests have appeared on Dwarkesh Podcast?

Recent guests on Dwarkesh Podcast include:

1. Alex Imas
2. Phil Trammell
3. Reiner Pope
4. Eric Jang
5. David Reich
6. Michael Nielsen
7. Terence Tao
8. Dylan Patel

To view more recent guests and their details, simply upgrade your Rephonic account. You'll also get access to a typical guest profile to help you decide if the show is worth pitching.

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