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Dwarkesh Podcast

Dwarkesh Patel
Dwarkesh Podcast
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  • Dwarkesh Podcast

    The Library of Alexandria isn’t where most ancient knowledge was lost

    06-03-2026 | 2 u. 2 Min.
    Renaissance history is so much wilder and weirder than you would have expected. Very fun chatting with Ada Palmer (historian, novelist, and composer based at the University of Chicago).
    Some especially fascinating things I learned from the conversation and her excellent book, Inventing the Renaissance:
    Not only did Gutenberg go bankrupt in the 1450s (after inventing the printing press), but so did the bank that foreclosed on him, and so did his apprentices. This is because paper was still very expensive, and so you had to make this big upfront CAPEX decision to print a batch of 300 copies of a book - say the Bible. But he’s in a small landlocked German town where only priests are allowed to read the Bible - so he sells maybe 7 copies. It’s only when this technology ends up in Venice, where you can hand 10 copies to each of 30 ship captains going to 30 different cities, that it starts taking off.
    Speaking of which, the printing revolution wasn’t just one single discrete event, just as the computer revolution has been this whole century of going from mainframes -> personal computers -> phones -> social media, each with different and accelerating social impact. Books came first, but they’re slow to print, and made in small batches. The real revolution is pamphlets - much faster, much harder to censor. Pamphlet runners are how you can have Luther’s 95 Theses go from Wittenberg to London in 17 days.
    So much other wild stuff from this episode. For example, did you know that the largest and best-funded experimental laboratory in 17th century Europe was very likely the Roman one run by inquisitors? Ada jokes that the Inquisition accidentally invented peer review. The focus of the Inquisition is really misunderstood - it was obsessed with catching dangerous new heretics like Lutherans and Calvinists - it only executed one person for doing science.
    And this leads Ada to make an observation that I think is really wise: the authorities and censors are always worried about the exact wrong things given 20/20 hindsight. When Inquisition raids an underground bookshop during the French Enlightenment, they don’t mind the Rousseau, Voltaire, and Encyclopédie, but they lose their minds about some Jansenist treatises about the technical nature of the Trinity.
    More broadly, a lesson for me from this episode is that it’s just really hard to shape history in the specific way that you want to impact things. One of the most famous medieval scholars is this guy Petrarch. He survives the Black Death in the 1340s, watches his friends die to plague and bandits, and says: our leaders are selfish and terrible, we need to raise them on the Roman classics so they’ll act like Cicero. So Europe pours money into finding ancient manuscripts, building libraries, and educating princes on classical virtues. Those princes grow up and fight bigger, nastier wars than ever before with new deadlier technology. And this, combined with greater urbanization and endemic plague, results in European life expectancy decreasing from 35 in the medieval period to 18 during the Renaissance (the period which we in retrospect think of as a golden age but which many people living through it thought of as the continuation of the dark ages that had persisted since the fall of Rome).
    Anyways, the libraries Petrarch inspires stick around, the printing press makes them accessible to everyone, and 200 years later a generation of medical students is reading Lucretius and asking “what if there are atoms and that’s how diseases work?” which eventually leads to germ theory, vaccines, and a cure for the Black Death (Ada has longer more involved explanation of how cosplaying the Romans results through a series of many steps to the scientific revolution). Petrarch wanted to produce philosopher-kings that shared his values. Instead he created a world that doesn’t share his values at all but can cure the disease that destroyed his.
    Watch on YouTube; read the transcript.
    Sponsors
    * Jane Street is still waiting on someone to solve their backdoor puzzle… They’re accepting submissions until April 1st and have set aside $50,000 for the best attempts. Separately, applications are live for Jane Street’s summer ML internships in NY, London, and Hong Kong. Go check all of this out at janestreet.com/dwarkesh.
    * Labelbox can help ensure your agents don’t need to rely on overspecified prompts. They tailor real-world scenarios to whatever domain you’re focused on, and they make sure the data you train on rewards real understanding, not just instruction-following. Learn more at labelbox.com/dwarkesh
    * Mercury’s personal accounts let you add users, issue cards, and customize permissions. This is super useful for sharing finances with a partner, a roommate… or even an OpenClaw agent. And, if you’re already a Mercury Business user, your personal account is free! See terms and conditions below, and learn more at mercury.com/personal-banking
    Eligible Mercury Business users who apply for and maintain a Mercury Personal account may have their Mercury Personal subscription fee waived provided they remain a user on an active Mercury Business account in good standing. Standard Mercury Platform Subscription fees will apply if they no longer meet eligibility requirements, including but not limited to no longer being associated with an eligible Mercury Business account, or if the program is modified or terminated. Mercury may modify or discontinue this offering at any time and will provide notice as required by law. See Subscription Terms for full details.
    * To sponsor a future episode, visit dwarkesh.com/advertise.
    Timestamps
    (00:00:00) - How cosplaying Ancient Rome led to the Renaissance
    (00:28:49) - How Florence’s weird republic worked
    (00:38:13) - How the Medicis took over Florence
    (00:58:12) - Why it was so hard for Gutenberg to make any money off the printing press
    (01:17:34) - Why the industrial revolution didn’t happen in Italy
    (01:23:02) - The slow diffusion of paper through Europe
    (01:41:21) - The Inquisition accidentally invented peer review


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  • Dwarkesh Podcast

    Dario Amodei — "We are near the end of the exponential"

    13-02-2026 | 2 u. 22 Min.
    Dario Amodei thinks we are just a few years away from AGI — or as he puts it, from having “a country of geniuses in a data center”. In this episode, we discuss what to make of the scaling hypothesis in the current RL regime, why task-specific RL might lead to generalization, and how AI will diffuse throughout the economy. We also dive into Anthropic’s revenue projections, compute commitments, path to profitability, and more.
    Watch on YouTube; read the transcript.
    Sponsors
    * Labelbox can get you the RL tasks and environments you need. Their massive network of subject-matter experts ensures realism across domains, and their in-house tooling lets them continuously tweak task difficulty to optimize learning. Reach out at labelbox.com/dwarkesh.
    * Jane Street sent me another puzzle… this time, they’ve trained backdoors into 3 different language models — they want you to find the triggers. Jane Street isn’t even sure this is possible, but they’ve set aside $50,000 for the best attempts and write-ups. They’re accepting submissions until April 1st at janestreet.com/dwarkesh.
    * Mercury’s personal accounts make it easy to share finances with a partner, a roommate… or OpenClaw. Last week, I wanted to try OpenClaw for myself, so I used Mercury to spin up a virtual debit card with a small spend limit, and then I let my agent loose. No matter your use case, apply at mercury.com/personal-banking.
    Timestamps
    (00:00:00) - What exactly are we scaling?
    (00:12:36) - Is diffusion cope?
    (00:29:42) - Is continual learning necessary?
    (00:46:20) - If AGI is imminent, why not buy more compute?
    (00:58:49) - How will AI labs actually make profit?
    (01:31:19) - Will regulations destroy the boons of AGI?
    (01:47:41) - Why can’t China and America both have a country of geniuses in a datacenter?


    Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
  • Dwarkesh Podcast

    Elon Musk — "In 36 months, the cheapest place to put AI will be space”

    05-02-2026 | 2 u. 49 Min.
    In this episode, John and I got to do a real deep-dive with Elon. We discuss the economics of orbital data centers, the difficulties of scaling power on Earth, what it would take to manufacture humanoids at high-volume in America, xAI’s business and alignment plans, DOGE, and much more.
    Watch on YouTube; read the transcript.
    Sponsors
    * Mercury just started offering personal banking! I’m already banking with Mercury for business purposes, so getting to bank with them for my personal life makes everything so much simpler. Apply now at mercury.com/personal-banking
    * Jane Street sent me a new puzzle last week: they trained a neural net, shuffled all 96 layers, and asked me to put them back in order. I tried but… I didn’t quite nail it. If you’re curious, or if you think you can do better, you should take a stab at janestreet.com/dwarkesh
    * Labelbox can get you robotics and RL data at scale. Labelbox starts by helping you define your ideal data distribution, and then their massive Alignerr network collects frontier-grade data that you can use to train your models. Learn more at labelbox.com/dwarkesh
    Timestamps
    (00:00:00) - Orbital data centers
    (00:36:46) - Grok and alignment
    (00:59:56) - xAI’s business plan
    (01:17:21) - Optimus and humanoid manufacturing
    (01:30:22) - Does China win by default?
    (01:44:16) - Lessons from running SpaceX
    (02:20:08) - DOGE
    (02:38:28) - TeraFab


    Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
  • Dwarkesh Podcast

    Adam Marblestone — AI is missing something fundamental about the brain

    30-12-2025 | 1 u. 49 Min.
    Adam Marblestone is CEO of Convergent Research. He’s had a very interesting past life: he was a research scientist at Google Deepmind on their neuroscience team and has worked on everything from brain-computer interfaces to quantum computing to nanotech and even formal mathematics.
    In this episode, we discuss how the brain learns so much from so little, what the AI field can learn from neuroscience, and the answer to Ilya’s question: how does the genome encode abstract reward functions? Turns out, they’re all the same question.
    Watch on YouTube; read the transcript.
    Sponsors
    * Gemini 3 Pro recently helped me run an experiment to test multi-agent scaling: basically, if you have a fixed budget of compute, what is the optimal way to split it up across agents? Gemini was my colleague throughout the process — honestly, I couldn’t have investigated this question without it. Try Gemini 3 Pro today gemini.google.com
    * Labelbox helps you train agents to do economically-valuable, real-world tasks. Labelbox’s network of subject-matter experts ensures you get hyper-realistic RL environments, and their custom tooling lets you generate the highest-quality training data possible from those environments. Learn more at labelbox.com/dwarkesh
    To sponsor a future episode, visit dwarkesh.com/advertise.
    Timestamps
    (00:00:00) – The brain’s secret sauce is the reward functions, not the architecture
    (00:22:20) – Amortized inference and what the genome actually stores
    (00:42:42) – Model-based vs model-free RL in the brain
    (00:50:31) – Is biological hardware a limitation or an advantage?
    (01:03:59) – Why a map of the human brain is important
    (01:23:28) – What value will automating math have?
    (01:38:18) – Architecture of the brain
    Further reading
    Intro to Brain-Like-AGI Safety - Steven Byrnes’s theory of the learning vs steering subsystem; referenced throughout the episode.
    A Brief History of Intelligence - Great book by Max Bennett on connections between neuroscience and AI
    Adam’s blog, and Convergent Research’s blog on essential technologies.
    A Tutorial on Energy-Based Learning by Yann LeCun
    What Does It Mean to Understand a Neural Network? - Kording & Lillicrap
    E11 Bio and their brain connectomics approach
    Sam Gershman on what dopamine is doing in the brain
    Gwern’s proposal on training models on the brain’s hidden states


    Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
  • Dwarkesh Podcast

    Thoughts on AI progress (Dec 2025)

    23-12-2025 | 12 Min.
    Read the essay here.
    Timestamps
    00:00:00 What are we scaling?
    00:03:11 The value of human labor
    00:05:04 Economic diffusion lag is cope00:06:34 Goal-post shifting is justified
    00:08:23 RL scaling
    00:09:18 Broadly deployed intelligence explosion


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