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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
AI Alignment
Covid-19
Solar Energy
AI Research
Civil War
Automation
Ethics In AI
Great Leap Forward
ARC Benchmark

Deeply researched interviews www.dwarkesh.com

PublishesWeeklyEpisodes128Founded6 years ago
Number of ListenersCategories
ScienceTechnology

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Artwork for Dwarkesh Podcast

Latest Episodes

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

Eric Jang walks through how to build AlphaGo from scratch, but with modern AI tools.

Sometimes you understand the future better by stepping backward. AlphaGo is still the cleanest worked example of the primitives of intelligence: search, learning fr... more

David Reich is back.

He and collaborator Ali Akbari just published a paper that overturns a long-standing consensus about human evolution — that natural selection has been dormant in our species since the agricultural revolution.

By scaling ancient... more

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Recent Guests

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
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
Sarah Paine
Expert on Russo-Chinese relations
Episode: Sarah Paine — How Russia sabotaged China's rise
Richard Sutton
One of the founding fathers of reinforcement learning and inventor of many main techniques used in the field.
Episode: Richard Sutton – Father of RL thinks LLMs are a dead end
Arthur Kroeber
Founder of Gavekal Dragonomics; author of China's Economy, What Everybody Needs to Know
Gavekal Dragonomics
Episode: Why China's manufacturing economy is dominating — Arthur Kroeber
Tyler Cowen
Economist and author, known for his insights on economics and public policy.
Episode: Tyler Cowen — The #1 bottleneck to AI progress is humans

Host

Dwarkesh Patel
Host of the Dwarkesh Podcast

Reviews

4.7 out of 5 stars from 2.6k 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 Statesa month 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 Statesa month 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 Statesa month 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 Statesa month 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 Statesa month 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 – 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.
Michael Nielsen – How science actually progresses
Q: What is the role of open science in shaping the future of scientific discovery and careers?
Open science changes incentives and collaboration by valuing openly shared work, enabling broader verification and reuse, and creating a reputation economy that can accelerate or alter how researchers are recognized and funded.
Michael Nielsen – How science actually progresses
Q: Is AlphaFold a scientific explanation, or does it represent a different kind of explanatory object?
There are multiple paths: one view treats AlphaFold as a powerful predictive tool that may not be an explanation in the classic sense, while another view sees it as containing partial explanations and as a new kind of object whose interpretability could reveal underlying principles over time.

Audience Metrics

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

Listeners per Episode
Gender Skew
Location
Interests
Professions
Age Range
Household Income
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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. The a16z Show
2. No Priors: Artificial Intelligence | Technology | Startups
3. Cheeky Pint
4. Conversations with Tyler
5. All-In with Chamath, Jason, Sacks & Friedberg

How many episodes of Dwarkesh Podcast are there?

Dwarkesh Podcast launched 6 years ago and published 128 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. Eric Jang
2. Michael Nielsen
3. Terence Tao
4. Dylan Patel
5. Ada Palmer
6. Sarah Paine
7. Richard Sutton
8. Arthur Kroeber

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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