『The Generalist』のカバーアート

The Generalist

The Generalist

著者： Mario Gabriele
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今ならプレミアムプランが3カ月 月額99円

2026年5月12日まで。4か月目以降は月額1,500円で自動更新します。

概要

 “The future is already here. It’s just not evenly distributed.” The Generalist Podcast brings you weekly conversations with the people who live in these pockets of the future – visionary founders, prescient investors, and original thinkers. Each episode is designed to introduce you to new ideas, technologies, and markets and help you prepare for the world of tomorrow.Mario Gabriele
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  • 30% Of Network Engineers Are Retiring. What Happens Next? (Anil Varanasi, Co-Founder & CEO of Meter)

    30% Of Network Engineers Are Retiring. What Happens Next? (Anil Varanasi, Co-Founder & CEO of Meter)
    2026/04/07
    Anil Varanasi, co-founder and CEO of Meter, is building a new kind of networking company for the AI era. Alongside his brother Sunil, he has helped raise more than $250 million to challenge incumbents like Cisco with a vertically integrated approach spanning hardware, software, deployment, and ongoing operations, all delivered through a utility-style model. His view is that networking has remained largely unchanged for decades, even as it has become foundational to everything from AI workloads to real-world infrastructure. Meter’s ambition is not just to improve existing networks, but to make them autonomous over time. Before starting the company, Anil and Sunil were deeply involved in filmmaking, a background that still shapes their philosophy of building with cathedral-level craft across every layer of the stack.Together we explore:The “burden of knowledge” and why progress is getting harder across fieldsWhy most companies over-index on technology and ignore business model innovationThe three ways companies create advantage: technology, delivery, and business modelHow Meter’s trade-in model borrows from the automotive industryWhy networking should function like electricity or water—not hardwareLessons from Japanese vending machine logistics for infrastructure deploymentThe hidden coordination problem behind vertically integrated companiesWhy Anil believes “common knowledge” is often wrongHow COVID forced Meter to abandon geographic constraints and scale nationallyThe case for fully autonomous networks in a world of exploding demand—Thank you to the partners who make this possible.tech domains: An identity for builders at their core.Granola: The app that might actually make you love meetings.Brex: The intelligent finance platform.—Transcript: https://www.generalist.com/p/the-case-for-autonomous-networks—Timestamps(00:00) Introduction to Anil Varanasi and Meter(03:52) The burden of knowledge and slowing innovation(08:18) Losing creativity vs gaining expertise(10:25) What Meter actually does(13:26) Early life, immigration, and upbringing(15:47) Parental influence(20:03) Film, storytelling, and creative influence(22:55) Why Anil didn’t pursue filmmaking(25:44) Parallels between company building and filmmaking(27:00) Early programming and building(28:05) George Mason and understanding systems(29:59) The dynamic of working with his brother as a co-founder(34:03) His first business and lessons learned (or lack thereof)(35:15) Lessons from successful companies(38:16) Japanese vending machines and logistics insight(41:10) Scrapping 18 months of work(42:40) Conviction and long-term company building(46:02) COVID shock and near-death moment(49:59) Building hardware like a cathedral(52:25) Rethinking the networking business model(57:06) Build vs buy and transaction costs(59:39) Networking as infrastructure and utility(01:01:30) The case for autonomous networks(01:03:25) Hiring, talent, and what actually matters(01:06:15) Big unanswered questions (sleep, science)(01:07:28) Rethinking education(01:09:02) Infinite games and long-term thinking—Follow Anil VaranasiLinkedIn: https://www.linkedin.com/in/anilcvX: https://x.com/acvWebsite: https://anilv.com—Resources and episode mentions: https://www.generalist.com/p/the-case-for-autonomous-networks⁠—Production and marketing by penname.co. For inquiries about sponsoring the podcast, email jordan@penname.co.
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    1 時間 11 分
  • Why One Superintelligence Is More Dangerous Than a Thousand (Vincent Weisser, CEO & Co-Founder of Prime Intellect)

    Why One Superintelligence Is More Dangerous Than a Thousand (Vincent Weisser, CEO & Co-Founder of Prime Intellect)
    2026/03/24
    Much of the fear around AI centers on misalignment – the idea that powerful systems might act against human interests. Vincent Weisser worries about something different: what happens if advanced AI systems are perfectly aligned with the interests of a small group of institutions? That concern led him to co-found Prime Intellect, a startup building open infrastructure for training and deploying advanced AI models. Before Prime Intellect, Weisser helped organize Vitalik Buterin’s Zuzalu experiment and worked in decentralized science, where he helped unlock roughly $40 million in funding for unconventional research. Today, he’s applying that same open ethos to AI, working to ensure the tools that shape superintelligence remain broadly accessible rather than concentrated in the hands of a few.—In our conversation, we explore:Why Vincent believes multiple superintelligences are safer than oneThe intellectual influences that shaped Vincent’s thinking about intelligence and progress, including David Deutsch and Nick BostromPrime Intellect’s evolution from distributed compute infrastructure to frontier model training and reinforcement learning toolsWhy Vincent believes open and decentralized science could accelerate discoveryThe Zuzalu experiment and what it suggests about the future of scientific communitiesThe role of aesthetics and craft in building technologyWhy Europe might have a cultural advantage in a post-superintelligence worldVincent’s predictions for the next five years of AI—Thank you to the partners who make this possibleGranola: The app that might actually make you love meetings.Brex: The intelligent finance platform.Rippling: Stop wasting time on admin tasks, build your startup faster.—Transcript: https://www.generalist.com/p/why-one-superintelligence-is-more—Timestamps(00:00) Introduction to Vincent Weisser(03:28) The book behind Prime Intellect’s name(07:35) The case for suffering(09:35) An overview of Prime Intellect(13:03) Why open source models matter(21:18) Vincent’s intellectual influences(25:17) Early years in the startup scene(31:48) Funding science outside traditional institutions(41:22) The past 6 months of AI progress(43:45) Deciding to build Prime Intellect(46:55) Why GPUs were the right starting point(51:39) Training models on Prime Intellect(59:48) Why beauty matters(1:03:48) The Zuzalu experiment(1:06:27) Prime Intellect’s AGI Easter egg(1:11:13) Predictions for the next five years(1:15:09) Final meditations—Follow Vincent WeisserLinkedIn: https://linkedin.com/in/vincentweisserX: https://x.com/vincentweisserGoodreads: https://www.goodreads.com/user/show/69248416-vincent-weisserWebsite: https://primeintellect.ai—Resources and episode mentions: https://www.generalist.com/p/why-one-superintelligence-is-more⁠—Production and marketing by penname.co. For inquiries about sponsoring the podcast, email jordan@penname.co.
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    1 時間 19 分
  • Why Robots Still Struggle With Simple Tasks (And What Might Finally Change That) | Karol Hausman, Co-Founder & CEO of Physical Intelligence

    Why Robots Still Struggle With Simple Tasks (And What Might Finally Change That) | Karol Hausman, Co-Founder & CEO of Physical Intelligence
    2026/03/17

    Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. The core thesis: the same scaling dynamics that transformed language models may also unlock robotic intelligence. But only if you resist every commercial pressure pushing you toward specialization. The central challenge isn’t mechanical design. It’s intelligence: how robots learn, generalize, and interact with a physical world that is far harder to simulate than it is to describe. Before launching Physical Intelligence, Karol worked at Google Brain and Stanford University, studying robot learning alongside researchers Sergey Levine and Chelsea Finn, who later became his co-founders.


    In our conversation, we explore:

    • How growing up in a small town in Poland and watching Star Wars sparked Karol’s fascination with robots
    • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning
    • Why robotics has historically lagged behind breakthroughs in language models
    • The case for building a general “AI brain” for the physical world rather than a single specialized robot
    • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel
    • The return of reinforcement learning and the parallels between human learning and robot training
    • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models

    —

    Thank you to the partners who make this possible

    Brex: The intelligent finance platform.

    Granola: The app that might actually make you love meetings.

    —

    Transcript: https://www.generalist.com/p/karol-hausman-physical-intelligence

    —

    Timestamps

    (00:00) Intro

    (04:05) Karol’s early fascination with robots

    (07:38) How Karol relates to Fei-Fei Li’s biography

    (08:52) What inspired Karol to build better robots

    (11:19) Philosophical influences

    (15:33) Parallels between The Inner Game of Tennis and robotics

    (18:21) Karol’s entry point to robotics and PhD program

    (25:49) Combining robotics with LLMs: The Taylor Swift demo

    (30:48) The 1970s SHRDLU AI experiment

    (32:33) Founding Physical Intelligence

    (35:13) How Lachy Groom got involved

    (39:40) How research shapes what Physical Intelligence builds

    (45:22) The importance of real-world data

    (49:07) The return of reinforcement learning in robotics

    (53:31) The risk of commercializing too early

    (55:47) Finding the right partners for the business

    (57:13) Open research questions

    (1:00:00) NVIDIA’s simulation engines

    (1:01:57) The surprising speed of progress

    (1:04:16) Reliability in robotics

    (1:07:31) Compensating for missing senses

    (1:12:28) Book recommendation

    —

    Follow Karol Hausman

    LinkedIn: https://www.linkedin.com/in/karolhausman

    X: https://x.com/hausman_k

    —

    Resources and episode mentions: https://www.generalist.com/p/karol-hausman-physical-intelligence

    —

    Production and marketing by penname.co. For inquiries about sponsoring the podcast, email jordan@penname.co.
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    1 時間 14 分
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