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AI is commoditizing specialization — and the move isn't to specialize harder. It's to elevate. AI is running the 250-year division of labor in reverse, collapsing roles that used to be separate into one.Since Adam Smith's pin factory in 1776, progress meant slicing work into ever-narrower specialties — Babbage extended it to cognitive work, Coase explained why firms hoard coordination to make it pay. Junior and Dr. Tim Clark argue AI has flipped the whole arc. When the cost of coordination falls toward zero and deep expertise gets commoditized by "computational cognition," labor stops dividing and starts converging. At LeaderFactor, three roles that once had nothing to do with each other are merging into one, and the org chart no longer looks conventional.So what do you actually do about it? The episode gives leaders and L&D a practical filter. Every task sorts into what AI can do autonomously, what needs a human in the loop, and what has to stay uniquely human. That maps onto two algorithms: the AI algorithm — process information, identify patterns, generate outputs — and the human algorithm — assign value, exercise judgment, bear responsibility. The instruction is direct: cede the AI algorithm's ground, elevate into the human one, and stop binding your identity to a role that's now perishable. The scarce trait is no longer domain expertise. It's high agency.Chapters00:00 — Is AI reversing the division of labor?01:36 — Adam Smith, the pin factory, and 250 years of specialization04:23 — Babbage brings the division of labor to cognitive work06:22 — Coase: why firms exist and what falling coordination costs change07:46 — When agents talk to agents, coordination cost goes to zero08:18 — The Grand Convergence: how LeaderFactor's org chart changed12:36 — Marginalization forces a choice: elevate or be displaced13:49 — Why "upskilling" is dead — it's access vs. motivation now15:26 — High agency beats domain expertise16:14 — The AI algorithm: process, pattern, generate18:49 — Don't trust the insulation: step changes are coming19:35 — The human algorithm: assign value, judge, bear responsibility20:11 — The practical move: objective → responsibilities → roles23:00 — Filtering roles: autonomous, augmented, uniquely human24:49 — The psychology of a role that keeps changing26:37 — Bind yourself to value creation, not a title28:19 — Recap and final thoughts29:29 — Read Leading Through AI + free skill previewsReady for more? Take a look at our resources below. 

Video Game profits collapse, PC sales boom & The Next Gen battle begins in Japan These stories and many more on this episode of the VGNRTM! This episode we will look back at the biggest stories in and around the video game industry in November 1994.  As always, we'll mostly be using magazine cover dates, and those are of course always a bit behind the actual events. Alex Smith of They Create Worlds is our cohost.  Check out his podcast here: https://www.theycreateworlds.com/ and order his book here: https://www.theycreateworlds.com/book Get us on your mobile device: Android:  https://www.google.com/podcasts?feed=aHR0cHM6Ly92aWRlb2dhbWVuZXdzcm9vbXRpbWVtYWNoaW5lLmxpYnN5bi5jb20vcnNz iOS:      https://podcasts.apple.com/de/podcast/video-game-newsroom-time-machine And if you like what we are doing here at the podcast, don't forget to like us on your podcasting app of choice, YouTube, and/or support us on patreon! https://www.patreon.com/VGNRTM Send comments on Mastodon @videogamenewsroomtimemachine@oldbytes.space Or twitter @videogamenewsr2 Or Instagram https://www.instagram.com/vgnrtm Or videogamenewsroomtimemachine@gmail.com Or Discord https://discord.gg/mYdkBJe8 Links: If you don't see all the links, find them here: https://www.patreon.com/VGNRTM/posts/november-1994-161464050      7 Minutes in Heaven: Jazz Jackrabbit Video Version: https://youtu.be/IIom2LSch6w     https://www.mobygames.com/game/902/jazz-jackrabbit/ Corrections: Ethan's fine site The History of How We Play: https://thehistoryofhowweplay.wordpress.com/ November 1984 Ep - https://www.patreon.com/VGNRTM/posts/november-1984-157521521     Great Exhibition Digital Recreation - https://www.youtube.com/watch?v=9wNEgZDetNk     https://en.wikipedia.org/wiki/Nintendo_VS._System     Moondust 7 Minutes - https://youtu.be/jT3QzYpUEck?si=MbYsjB7cDptFfIBY      November 1994: Video Game Industry profits plummet          Sega Reports 47 Percent Drop in Earnings in Fiscal First Half, Associated Press Worldstream, November 11, 1994; Friday 07:03 Eastern Time, Section: Financial pages       SEGA PROFITS PLUNGE 43PC AS VIDEO GAME RIVALRY HOTS UP, The Guardian (London), November 12, 1994, Section: THE GUARDIAN HOME PAGE; Pg. 1, Byline: Nicholas Bannister In London And Kevin Rafferty In Tokyo     EDITORS:Associated Press Worldstream, November 21, 1994; Monday 07:13 Eastern Time, Section: Financial pages     KOEI LOWERS PROFIT ESTIMATES, Jiji Press Ticker Service, NOVEMBER 4, 1994, FRIDAY     NAMCO SUFFERS LOWER PROFITS IN 1ST HALF, Jiji Press Ticker Service, NOVEMBER 4, 1994, FRIDAY     KONAMI TO LOG 3.1-B.-YEN LOSS FOR FY '94, Jiji Press Ticker Service, NOVEMBER 9, 1994, WEDNESDAY          KONAMI SLIPS INTO RED IN 1ST HALF, Jiji Press Ticker Service, NOVEMBER 25, 1994, FRIDAY     CAPCOM SUFFERS SHARP DROPS IN PROFIT, SALES, Jiji Press Ticker Service, NOVEMBER 24, 1994, THURSDAY     Play Meter November 1994, pg. 22     TAKARA RETURNS TO BLACK IN 1ST HALF, Jiji Press Ticker Service, NOVEMBER 18, 1994, FRIDAY     T-HQ announces third-quarter results, s'ipment of the XBAND Video Game, Modem aNd new equity financing, Business Wire, November 14, 1994, Monday     SOFTWARE ETC. STORES, INC. REPORTS THIRD QUARTER RESULTS, PR Newswire, November 10, 1994, Thursday - 13:59 Eastern time, Section: Financial News Software Etc. and Babbage's to merge         SOFTWARE ETC. STORES, INC. REPORTS THIRD QUARTER RESULTS, PR Newswire, November 10, 1994, Thursday - 13:59 Eastern time, Section: Financial News Big money bets against Atari     CBS rich with takeover rumors, USA TODAY, November 7, 1994, Monday, FINAL EDITION, Section: MONEY; Dan Dorfman; Pg. 4B, Byline: Dan Dorfman     https://mdsass.com/our-team/     ATARI RESPONDS TO DAN DORFMAN ARTICLE IN USA TODAY, PR Newswire, November 8, 1994, Tuesday - 09:10 Eastern Time     Atari stock plummets,The Financial Post (Toronto, Canada), November 8, 1994, Tuesday,DAILY EDITION, Section: SECTION 1, NEWS; Pg. 14; APPOINTMENT NOTICE, Byline: Bloomberg     ATARI CORP. ANNOUNCES THIRD QUARTER AND NINE MONTHS 1994 RESULTS, PR Newswire, November 14, 1994, Monday - 05:59 Eastern Time, Section: Financial News     COMPANY NEWS; ATARI STOCK RISES AS DEAL WITH SEGA IS COMPLETED, The New York Times, November 17, 1994, Thursday, Late Edition - Final, Distribution: Financial Desk , Section: Section D; ; Section D;  Page 4;  Column 1;  Financial Desk ; Column 1;  Siliwood deals abound!     Activision, Henson in Multimedia Muppets Deal, Ad Day, November 7, 1994, Section: NEWS ROUNDUP; Pg. 12         Move over, nerds - Hollywood's here, The Age (Melbourne, Australia), November 15, 1994 Tuesday, Late Edition, Section: COMPUTERS; Frontier Media; Pg. 43     https://archive.org/details/electronic-games-1994-11z Baby Bells form Multimedia Colossus         3 BABY BELLS FORM MULTIMEDIA COLOSSUS 1994, Reuters News Service, St. Louis Post-Dispatch (Missouri), November 1, 1994, TUESDAY, FIVE STAR Edition, Section: BUSINESS; Pg. 6C    TELEPHONE FIRMS AIM AT CABLE BELL ATLANTIC AND TWO OTHER, BABY BELLS PLAN A MULTIMEDIA VENTURE. , USERS COULD ORDER, VIDEOS.The Philadelphia Inquirer, November 1, 1994 Tuesday FINAL EDITION, Section: BUSINESS; Pg. C01         Info highway dream team / Ovitz wants Hollywood on high-tech map, USA TODAY, November 1, 1994, Tuesday, FINAL EDITION, Section: MONEY; Pg. 1B; Cover Stor      William Morris goes Interactive     William Morris Courts Agencies, ADWEEK, November 14, 1994, Western Edition, Byline: By Cathy Taylor Siemens gets into settop boxes    2 Companies Join Siemens In Video Plan, The New York Times, November 8, 1994, Tuesday, Late Edition - Final, Distribution: Financial Desk, Section: Section D; ; Section D; Page 5; Column 1; Financial Desk ; Column 1;Byline: By Bloomberg Business News     Intel, Backed On ITV, Sails For CablePort, Electronic Buyers News, November 28, 1994, Business and Industry, Section: Pg. 3; ISSN: 0164-6362, Byline: Jonathan Cassell TCI buys into Acclaim     TCI AND ACCLAIM FORM PARTNERSHIP FOR INTERACTIVE, ENTERTAINMENT SOFTWARE, M2 PRESSWIRE, November 4, 1994 TeleWest goes public         Time is right to float, says TeleWest, The Herald (Glasgow), November 8, 1994, Section: Pg. 25, Byline: Nicola Reeves BCE Holdings to buy Rage and Software Creations     NEW GAME PLAN AT £25M BCE, Daily Mail (London), November 2, 1994, Section: Pg. 65           BCE HOLDINGS TO BUY SOFTWARE CREATIONS (HOLDINGS): 2, Extel Examiner, November 1, 1994, Tuesday - 03:04 Eastern Time, Section: Company News; Takeovers and Acquisitions           COMPUTER GAMES MERGER GOES TO EUROPEAN LEVEL, The Guardian (London), November 5, 1994, Section: THE GUARDIAN CITY PAGE; Pg. 38, Byline: Jim Levi     Consoled by a £10m fortune, Mail on Sunday (London), November 6, 1994, Section: Pg. 5, Byline: Jason Nisse     Computer games 'set for surge in sales', The Times, November 9, 1994, Wednesday, Section: Business, Byline: By Neil Bennett Warner buys Renegade     Amiga Games, November 1994, pg. 34         https://en.wikipedia.org/wiki/Renegade_Software Mindscape to buy Atreid Concept     L'editeur Mindscape rachete Atreid Concept, Echos, November 19, 1994             https://www.mobygames.com/company/661/kalisto-entertainment-sa/      Video game ratings system still a thorn in coinop's side     Play Meter, November 1994, pg. 20     https://arcade.fandom.com/wiki/Parental_Advisory_System Sega goes big with VR-1     Japanese take virtual reality for a ride; Sega has combined fairground rides, with hi-tech wizardry, writes Arnold Redhead, The Independent (London), November 21, 1994, Monday, Section: NETWORK PAGE; Page 25, Byline: ARNOLD REDHEAD          https://en.wikipedia.org/wiki/VR-1        https://www.youtube.com/watch?v=L_rf9FiwBUk Japanese Next Gen Holiday Lineup Set     Video-game makers out to zap 32-bit rivals, Nikkei Weekly, November 7, 1994, Business and Industry, Section: Pg. 9; Vol. 32;     Multimedia video game wars begin, The Daily Yomiuri, November 8, 1994, Tuesday, Byline: Terumitsu Otsu; Daily Yomiuri Staff Writer     Nintendo's super 'game boy' from Dundee, The Scotsman, November 16, 1994, Wednesday, Section: Pg. 32          NINTENDO, U.S. FIRM TO DEVELOP 3-D SOFTWARE, Jiji Press Ticker Service, NOVEMBER 22, 1994, TUESDAY            https://nintendo.fandom.com/wiki/Paradigm_Entertainment Saturn release date set     INDUSTRY TREND: CONSUMER ELECTRONICS FIRMS JOIN VIDEO GAME, ORGY, Jiji Press Ticker Service, NOVEMBER 18, 1994, FRIDAY          New Video Machines Battle For Supremacy, The Associated Press, November 30, 1994, Wednesday, AM cycle, Section: Business News, Byline: By BRAVEN SMILLIE, Matsushita announces next gen system for 1995     Matsushita likely to market 64-bit game machines in '95, Japan Economic Newswire, NOVEMBER 15, 1994, TUESDAY      Matsushita and IBM team up     Matsushita, IBM in multimedia project, United Press International, November 20, 1994, Sunday, BC cycle        https://en.wikipedia.org/wiki/Panasonic_M2#Technical_specifications 3DO announces massive loss     Video Game Maker 3DO Reports $ 12.8 Million Second-Quarter Loss, The Associated Press, November 4, 1994, Friday, BC cycle          (THDO) 3DO announces second quarter financial results, Business Wire, November 4, 1994, Friday Goldstar launches 3DO in USA         Goldstar Co, Wall Street Journal (3 Star, Eastern (Princeton, NJ) Edition), November 8, 1994, Business and Industry, Section: Pg. B4; Vol. 224; No. 91; ISSN: 0099-9660 Creative to launch 3DO Blaster    Unveiling the latest in computer magic / Film and fun: Morphing and more, USA TODAY, November 17, 1994, Thursday, FINAL EDITION, Section: LIFE; Pg. 4D     3DO Blaster Video - Retro Collective - https://youtu.be/qaHAuGmN3Tk?si=OHf5v3Z9vfJ3RZfl Nintendo launches massive DKC blitz     https://youtu.be/SbHL8-XkXMA?si=d3GNpw2n57mTQBuL       https://youtu.be/OGqUF02zVt4?si=XFO_2LUnM157ayC5        Burnett Seeks to Make Donkey Kong King, AdWeek Midwest; AdWeek, November 21, 1994, Business and Industry, Section: Pg. 2; Vol. XXXV; No. 47;           Yen and old product cause slide in Nintendo profits, The Financial Post (Toronto, Canada), November 22, 1994, Tuesday,, DAILY EDITION, Section: SECTION 1, NEWS; Pg. 15; APPOINTMENT NOTICE           Video Games Showdown: Will Sega Zap Nintendo?, Christian Science Monitor 8Boston, MA), November 28, 1994, Monday, Section: ECONOMY; Pg. 4, Byline: Mark Trumbull, Staff writer of The Christian Science Monitor     Nation Goes Ape For Donkey Kong Country; Runaway Sales for Hit Video, Game Exceed Box Office Gross for Current Number One Movie, Business Wire, November 30, 1994, Wednesda Atari to spend big in Europe     Atari Tackles Games Giants In Pounds 5m Spend, Marketing, November 10, 1994 Jaguar launches in Japan     Atari's Jaguar Enters Japanese Retail Markets 11/22/94, Newsbytes News Network, November 22, 1994     https://forums.atariage.com/topic/330871-the-japanese-atari-jaguar/ Sony announces Liverpool dev centre     SONY CREATES 250 NEW JOBS FOR MERSEYSIDE, Press Association, November 7, 1994, Monday          SONY ELECTRONIC INVESTMENT IN, The Guardian (London), November 8, 1994, Section: THE GUARDIAN CITY PAGE; Pg. 14, Byline: Martyn Halsall, Northern          SONY TO SET UP U.K. GAME SOFTWARE CENTER, Jiji Press Ticker Service, NOVEMBER 8, 1994, TUESDAY        JAPANESE GIANT TO MAKE GAMES AND 250 JOBS ON MERSEYSIDE, M2 PRESSWIRE, November 28, 1994 Nintendo signs Russian Distribution deal     Russia: Nintendo has selected Steepler as an exclusive distributor of Nintendo video games., Kommersant, November 1, 1994          https://bootleggames.fandom.com/wiki/Steepler_Ltd.#1994:_Dendy:_The_New_Reality,_partnership_with_Nintendo          https://en.wikipedia.org/wiki/Dendy           Mortal Kombat 2 launch is massive     Mortal moral: Gore sells, money yells,  Computer Retail Week, November 14, 1994, Business and Industry, Section: Pg. 116; Vol. 4;     Ad budget Rises    Ad/Media Bulletin: Computer games ad push targets grown-ups, Marketing, November 17, 1994 Movie tie-ins getting tighter         (SNAPSHOT), The Age (Melbourne, Australia), November 12, 1994 Saturday, Late Edition, Section: SATURDAY EXTRA; SNAPSHOT; Pg. 15     Another Big U.S. Deal Turns Sour for Japanese Firm, Associated Press Worldstream, November 18, 1994; Friday 06:09 Eastern Time, Section: International news, Byline: PETER LANDERS           Hard lessons from Sony's software underbelly, The Independent (London), November 18, 1994, Friday, Section: BUSINESS & CITY PAGE; Page 34, Byline: HAMISH McRAE     Leisure Concepts reports third quarter, nine-month results, Business Wire, November 14, 1994, Monday             https://en.wikipedia.org/wiki/GoldenEye        https://en.wikipedia.org/wiki/GoldenEye_007  Nintendo premiers VirtualBoy     Nintendo Unveils Virtual Reality Game, The Associated Press , November 14, 1994, Monday, AM cycle, Section: Business News          https://www.linkedin.com/in/kerry-ganofsky-15873/     Nintendo announces investment in Reflection Technology Inc.; home video game leader also acquires exclusive worldwide license for proprietary LED, display technology, Business Wire, November 14, 1994, Monday     VIRTUALITY PLAYS DOWN IMPACT OF RIVAL NINTENDO PRODUCT, Extel Examiner,November 16, 1994, Wednesday - 07:16 Eastern Time, Section: Company News; Other     https://en.wikipedia.org/wiki/Virtual_Boy      PC sales boom     Spurred by many factors, home PC sales are soaring, Star Tribune (Minneapolis, MN), November 10, 1994, Metro Edition, Section: Special; Pg. 2S, Byline: Steve Alexander; Staff Writer Bandai and Apple team up for children's PC     BANDAI, APPLE TO JOINTLY DEVELOP PC FOR CHILDREN, Jiji Press Ticker Service, NOVEMBER 10, 1994, THURSDAY       https://en.wikipedia.org/wiki/Apple_Pippin     Apple sets sights on video games, The Financial Post (Toronto, Canada), November 11, 1994, Friday,, DAILY EDITION, Section: SECTION 1, NEWS; Pg. 5; COLUMN Apple to sell MacOS at retail     MICROFILE, The Guardian (London), November 17, 1994, Section: THE GUARDIAN ONLINE PAGE; Pg. 7     https://www.youtube.com/watch?v=8v4BaWwoyA0 Commodore Sale delayed... AGAIN!     DELAY IN THE SALE OF COMMODORE CREATES ANXIETY A LONG WAIT COULD KILL PROSPECTS FOR THE FIRM'S AMIGA COMPUTERS. AT LEAST, THAT'S WHAT ITS ADHERENTS SAY., The Philadelphia Inquirer, November 7, 1994 Monday FINAL EDITION, Section: PHILADELPHIA BUSINESS; Pg. G01, byline: Dan Stets,     Amiga Games, November 1994, pg. 19 Australia funds multimedia development         Multimedia funding is welcome news, The Age (Melbourne, Australia), November 1, 1994 Tuesday, Late Edition, Section: COMPUTERS; Frontier Media; Pg. 34 Korea invests in games     Korea Makes Huge Game Industry Investment, Newsbytes, November 21, 1994, Monday Looking Glass goes VC     LOOKINGGLASS RECEIVES $3.8 MILLION IN VENTURE CAPITAL FROM INSTITUTIONAL VENTURE PARTNERS, MATRIX PARTNERS, PR Newswire, November 21, 1994, Monday - 14:24 Eastern Time, Section: Entertainment, Television, and Culture     PC Player November 1994, pg. 17 Humongous bets on hand drawn art     "FREDDI FISH AND THE CASE OF THE MISSING KELP SEEDS(TM) SWIMS INTO STORES,PR Newswire, November 7, 1994, Monday - 12:52 Eastern Time" Staples stocks games     Office Superstores Emphasize 'Play" with Software, Discount Store News, November 7, 1994, Business and Industry, Section: Pg. S4; Vol. 33; No. 21; ISSN: 0012-3587 Amstrad targets direct market     Marketing Technique: Key movers - Publishers are still paying mega bucks for titles on mega bytes. So why does computer publishing continue to thrive, asks Michael Kavanagh, Marketing, November 24, 1994, Byline: By MICHAEL KAVANAGH IBM moves to online software distribution     IBM to beam up satellite-based software delivery, Network World, November 7, 1994, Section: TOP NEWS; Pg. 10, Byline: Michael Cooney IBM introduces multilevel disc     IBM's multilevel optical disk named "Best of What's New", Business Wire, November 9, 1994, Wednesday        https://research.ibm.com/publications/multilevel-volumetric-optical-storage AT&T buys Imagination network     AT&T buys interactive computer games unit, Financial Times (London,England), November 16, 1994, Wednesday, Section: International Company News; Pg. 34, Byline: By LOUISE KEHOE and REUTER      Xband launches     PERSONAL TECHNOLOGY New video game service for kids ready to come on line Thursday, The Atlanta Journal and Constitution, November 13, 1994, Sunday, Section: BUSINESS; Section R; Page 3, Byline: By Kris Jensen STAFF WRITER Sega Channel to get nationwide rollout     Sega Channel test a success -- service prepares for national rollout in December; Final test results far exceed expectations, Business Wire, November 30, 1994, Wednesday Jaguar to go online     CUC BUYS ITS WAY INTO INTERNET TRANSACTIONS; IMAGINE AT&T OWNING THE COMPANY; NOT MOSAIC, NETSCAPE; COMMERCE THROUGH COMPUSERVE; OTHER NEWS: Advertising Age, November 21, 1994, Section: Pg. 15 Sega goes online     Sega goes on-line with CompuServe & World Wide Web; real-time conferences, video clips, contests, chat rooms all part of new interactive, services for Sega fans, Business Wire, November 2, 1994, Wednesday     "CHRYSLER CD-ROMS GROOVE TO GENERATION X; TREKKING TO THE INTERNET; ONLINE VIDEOGAME NETWORK BOWS; AOL BOOSTS INTERNET STRATEGY; OTHER NEWS: Advertising Age, November 14, 1994, Section: Pg. 22" Mosaic Communications changes name to Netscape     CUC BUYS ITS WAY INTO INTERNET TRANSACTIONS; IMAGINE AT&T OWNING THE COMPANY; NOT MOSAIC, NETSCAPE; COMMERCE THROUGH COMPUSERVE; OTHER NEWS: Advertising Age, November 21, 1994, Section: Pg. 15 AOL goes shopping     "CHRYSLER CD-ROMS GROOVE TO GENERATION X; TREKKING TO THE INTERNET; ONLINE VIDEOGAME NETWORK BOWS; AOL BOOSTS INTERNET STRATEGY; OTHER NEWS: Advertising Age, November 14, 1994, Section: Pg. 22" CUC buys netMarket     CUC BUYS ITS WAY INTO INTERNET TRANSACTIONS; IMAGINE AT&T OWNING THE COMPANY; NOT MOSAIC, NETSCAPE; COMMERCE THROUGH COMPUSERVE; OTHER NEWS: Advertising Age, November 21, 1994, Section: Pg. 15 Paul Allen invests in Cnet     Vulcan gets C/NET, The Financial Post (Toronto, Canada), November 4, 1994, Friday,, DAILY EDITION, Section: SECTION 1, NEWS; Pg. 47, Business Briefs; CORRECTION Bill Gates touts information future at Comdex     https://youtu.be/7fJWMsgxzvA?si=VzEkgqkFwbDHUzRz         Microsoft chief sees new era in computing, St. Petersburg Times (Florida), November 21, 1994, Monday, City Edition, Times Publishing Company, Section: BUSINESS; TECHNOLOGY; TECH TALK; Pg. 8; DIGEST, Byline: DAVE GUSSOW Publishing     Pearson Buys Future     PEARSON ACQUIRES FUTURE PUBLISHING, M2 PRESSWIRE, November 28, 1994 Street Fighter the RPG     Play Meter November 1994, pg. 170      Fighter History suit settled     Computer game makers settle copyright dispute, Japan Economic Newswire, NOVEMBER 1, 1994, TUESDAY     https://en.wikipedia.org/wiki/Data_East_USA,_Inc._v._Epyx,_Inc.     https://en.wikipedia.org/wiki/Capcom_U.S.A._Inc._v._Data_East_Corp.      Nintendo wins again     NINTENDO WINS THIRD SUMMARY JUDGMENT THIS YEAR IN PATENT INFRINGEMENT CASE, PR Newswire, November 30, 1994, Wednesday - 14:45 Eastern Time, Section: Financial News GATT changes coming     BAN ON CD, GAMES HIRE, The Sydney Morning Herald, November 20, 1994 Sunday, Late Edition, Section: BUSINESS; Pg. 58, Byline: BRUCE JONES          VOTES IN FAVOR OF GATT, Congressional Press Releases, November 29, 1994, Tuesday, Section: PRESS RELEASE, Byline: STEPHEN HORN     https://en.wikipedia.org/wiki/General_Agreement_on_Tariffs_and_Trade US Government to fund Software Protection Efforts in China         Business Report ON TECHNOLOGY China shines as new market, The Atlanta Journal and Constitution, November 2, 1994, Wednesday, Section: BUSINESS; Section G; Page 2, Byline: By Bill Husted Cyber crime booming     Crimes of the 'Net', Newsweek, November 14, 1994 , UNITED STATES EDITION, Section: BUSINESS; Software; Pg. 46 Internet Cafe profiled    Are You Ready For The Future?, The Sunday Times (London), November 20, 1994, Sunday, Section: Features, Byline: Christopher Lloyd Hate moves online     Report Assesses Extremist Groups in Europe, Associated Press Worldstream, November 15, 1994; Tuesday 10:34 Eastern Time, Section: International news, Byline: MARILYN AUGUST      Cybermania 94 awards     Interactivities, Playback, November 07, 1994, Section: Pg.9, Byline: Pamela David Lego awards video game resistance     Lego awards annual prize for services to children, Agence France Presse -- English, November 15, 1994 11:14 Eastern Time, Section: International news CNN visits Brittannia Manor     Haunted House Owner Goes All Out to Create Hell at Home, CNN NEWS 3:14 am ET, November 1, 1994 VR goes Dental     "https://vrarwiki.com/wiki/Virtual_i-O_i-glasses!   Dentist's drill or a 3D thrill The Age (Melbourne, Australia), November 8, 1994 Tuesday, Late Edition, Section: COMPUTERS; Pg. 50, Byline: Alan Sayre" Casio debuts digital camera     New still camera puts your memories on silicon chips, The Vancouver Sun (British Columbia), November 17, 1994, Thursday, FINAL EDITION, Section: BUSINESS; Pg. D4      Interview with game translator     PC Joker, pg. 61 William A. Higinbotham has passed     William A. Higinbotham, 84; Helped Build First Atomic Bomb, The New York Times, November 15, 1994, Tuesday, Late Edition - Final, Distribution: National Desk , Section: Section D; ; Section D;  Page 29;  Column 5;  National Desk ; Column 5; ; Obituary (Obit); Biography, Byline: William A. Higinbotham      https://en.wikipedia.org/wiki/William_Higinbotham       https://archive.org/details/sim_creative-computing_1982-10_8_10/page/190/mode/1up  Recommended Links: The History of How We Play: https://thehistoryofhowweplay.wordpress.com/ Gaming Alexandria: https://www.gamingalexandria.com/wp/ They Create Worlds: https://tcwpodcast.podbean.com/ Digital Antiquarian: https://www.filfre.net/ The Arcade Blogger: https://arcadeblogger.com/ Retro Asylum: http://retroasylum.com/category/all-posts/ Retro Game Squad: http://retrogamesquad.libsyn.com/ Playthrough Podcast: https://playthroughpod.com/ Retromags.com: https://www.retromags.com/ Games That Weren't - https://www.gamesthatwerent.com/ Sound Effects by Ethan Johnson of History of How We Play. Copyright Karl Kuras

Teddy Svoronos describes how today's agentic AI changes what and how we teach on episode 617 of the Teaching in Higher Ed podcast. Quotes from the episode An AI agent is an LLM that runs tools in a loop to achieve a goal. -Teddy Svoronos The process of having a task, write a report, use a tool, web search, and do it over and over again until you feel like you’ve gotten the full sort of spectrum of things—that I think is what an agent really is. -Teddy Svoronos These LLMs are now becoming like this intermediary between me and the actual content. And so I’m optimizing in a different way than I used to. -Teddy Svoronos I think there’s an analogy with these tools that I’ve been thinking of as cognitive debt, which is that as you offload to them, there are things that they’ll do that you won’t quite understand. -Teddy Svoronos Resources Agentic Everything: How the latest set of models changes things, by Teddy Svoronos Course Corrections: Redesigning my course for AI, by Teddy Svoronos Pray, Mr. Babbage, by Teddy Svoronos Episode 590: Deep Background – Using AI as a Co-Reasoning Partner with Mike Caulfield Episode 234: A New Lens for Learning Outcomes with Maria Andersen José Antonio Bowen’s AI Detector False Positive Calculator Episode 605: Teaching with AI – The Good, the Bad, the Ugly, and the Future with José Bowen MacWhisper The Checklist Manifesto, by Atul Gawande

This episode is a masterclass in leadership, scale, and hard-earned business wisdom. Jess sits down with Gary Kusin—founder of GameStop (formerly Babbage's), former CEO of Kinko's, and a leader behind one of the most impressive turnarounds in retail history—to unpack the lessons that shaped his career. Gary shares the real story behind taking Kinko's from negative EBITDA to a $2.4B acquisition by FedEx, what it was like working directly with Fred Smith, and the leadership principles he developed through decades of building and advising companies. This conversation goes far beyond business tactics—it dives into accountability, culture, and what it truly takes to build teams that win. From raising capital to making tough calls as a CEO, Gary breaks down the difference between theory and reality in leadership. If you're a founder, operator, or aspiring leader, this episode will challenge how you think about performance, people, and long-term success. Learn more about your ad choices. Visit megaphone.fm/adchoices

The New START nuclear deal was signed in 2010 to restrict the number of strategic warheads and missiles America and Russia could amass. Will there be a new deal – and what will happen if not? How social media has helped fuel recruitment to cults. And our baldness correspondent bristles at some hairy questions.Listen back to "The Bomb", our Babbage series on America's quest to modernise its nuclear arsenal.  Listen to what matters most, from global politics and business to science and technology—Subscribe to Economist Podcasts+For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account.  Hosted on Acast. See acast.com/privacy for more information.

The New START nuclear deal was signed in 2010 to restrict the number of strategic warheads and missiles America and Russia could amass. Will there be a new deal – and what will happen if not? How social media has helped fuel recruitment to cults. And our baldness correspondent bristles at some hairy questions.Listen back to "The Bomb", our Babbage series on America's quest to modernise its nuclear arsenal.  Listen to what matters most, from global politics and business to science and technology—Subscribe to Economist Podcasts+For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account.  Hosted on Acast. See acast.com/privacy for more information.

Brian Gutekunst speaks to the media Wednesday at 12:30pm CT, and Ryan breaks down what questions actually deserve to be asked—and which ones are completely unfair. Plus, the Packers reportedly hire 49ers DB coach Daniel Bullocks, completing what might be an upgraded defensive staff across the board. Ryan pushes back hard on critics demanding answers for the Kenny Clark trade and cornerback situation, pointing out that teams can have roster deficiencies and still compete at a high level—just look at the Rams and Seahawks. The Vikings' stunning dismissal of GM Kwesi Adofo-Mensah reveals coaching staff influence may have been the real problem in Minnesota. And with Gannon, Babbage, Seefus, and now Bullocks in place, there's genuine reason for optimism about this Packers defense heading into 2025. Subscribe and leave a review to help the show grow! This episode is brought to you by PrizePicks! Use code PACKDADDY to get started with America's #1 fantasy sports app. https://prizepicks.onelink.me/LME0/PACKDADDY To advertise on this podcast please email: ad-sales@libsyn.com Or go to: https://advertising.libsyn.com/packernetpodcast Help keep the show growing and check out everything I'm building across the Packers and NFL world: Support: Patreon: www.patreon.com/pack_daddy Venmo: @Packernetpodcast CashApp: $packpod Projects: Grade NFL Players ➜ fanfocus-teamgrades.lovable.app Packers Hub ➜ packersgames.com Create NFL Draft Big Boards ➜ nfldraftgrades.com Watch Draft Prospects ➜ draftflix.com Screen Record ➜ pause-play-capture.lovable.app Global Economics Hub ➜ global-economic-insight-hub.lovable.app

Brian Gutekunst speaks to the media Wednesday at 12:30pm CT, and Ryan breaks down what questions actually deserve to be asked—and which ones are completely unfair. Plus, the Packers reportedly hire 49ers DB coach Daniel Bullocks, completing what might be an upgraded defensive staff across the board. Ryan pushes back hard on critics demanding answers for the Kenny Clark trade and cornerback situation, pointing out that teams can have roster deficiencies and still compete at a high level—just look at the Rams and Seahawks. The Vikings' stunning dismissal of GM Kwesi Adofo-Mensah reveals coaching staff influence may have been the real problem in Minnesota. And with Gannon, Babbage, Seefus, and now Bullocks in place, there's genuine reason for optimism about this Packers defense heading into 2025. Subscribe and leave a review to help the show grow! This episode is brought to you by PrizePicks! Use code PACKDADDY to get started with America's #1 fantasy sports app. https://prizepicks.onelink.me/LME0/PACKDADDY To advertise on this podcast please email: ad-sales@libsyn.com Or go to: https://advertising.libsyn.com/packernetpodcast Help keep the show growing and check out everything I'm building across the Packers and NFL world: Support: Patreon: www.patreon.com/pack_daddy Venmo: @Packernetpodcast CashApp: $packpod Projects: Grade NFL Players ➜ fanfocus-teamgrades.lovable.app Packers Hub ➜ packersgames.com Create NFL Draft Big Boards ➜ nfldraftgrades.com Watch Draft Prospects ➜ draftflix.com Screen Record ➜ pause-play-capture.lovable.app Global Economics Hub ➜ global-economic-insight-hub.lovable.app

The Courts of Heaven for Beginners: Challenging Fears That Hinder by Lisa Noel Babbage PhD https://www.amazon.com/Courts-Heaven-Beginners-Challenging-Hinder/dp/B0CWSF6XS2 Lisanoelbabbage.com The gifts of the Spirit are without repentance, but yours may be held up in court. Are you ready to petition for what Christ died for you to have? Within the last one hundred years, we have seen God move in distinct ways. The current church age requires spiritual maturity and authority beyond the five-fold ministry. Every generation has a special assignment, or calling, from the throne room. Ours is to operate in the courts of heaven. The Courts of Heaven for Beginners demonstrates the significance of this movement while escorting you into a realm you’ve only read about. Gain insight, operate in the gift of faith, take your authority liberally to work with God’s power, and see your world change because of the judgment in your favor as a believer in Christ. About the author Lisa Noël Babbage was born in Philadelphia and grew up in Atlanta, Georgia. She started her second career as an educator in DeKalb County Public Schools and has gone on to become a adjunct professor and veteran teacher. She wrote her autobiography “333 Miracles” in 2011, which was rereleased in 2018 under her own publishing company, Botany Bay. She is the founder of Maranatha House Ministries, a Georgia based nonprofit organization, and works with various organizations including Voices Against Trafficking and Catalyst Coalition.

Stability isn't something you earn once you're “big enough” or “finally staffed up.” It's something you design on purpose—or you pay for it later in burnout, panic fundraising, and house-of-cards vibes.In this episode, Brooke Richie-Babbage is back to flip the script on what capacity really means. Capacity is about changing the conditions under which your work happens, so the how of the work gets easier, less fragile + way more sustainable.We're talking broken mugs, creaky floors, cash cliffs, “build years” vs. “growth years,” and why “stability is a leadership choice” might be the most freeing (and challenging) mindset shift you make in 2026. If you've ever thought, “We'll feel stable when we finally _______,” this episode's your loving interruption.You'll walk away with clarity + next steps to build real capacity, including how to:Redefine capacity + stability as design problems, not personal failures → Shift from “I just need the right people / next grant / better tool” to “Where is our organization fragile, and how do we strengthen the container—systems, rhythms, decision-making—so the work doesn't require heroics?”Narrow priorities + clean up decision-making so everything stops bottlenecking at the leader → Get practical about choosing fewer, deeper priorities; naming what you're not doing this year; and mapping who actually owns which decisions—so your ED (or you) isn't secretly holding six out of ten critical calls.Build stability through simple financial + operational rhythms (not just more hires) → Learn how to read your own “financial weather patterns,” plan for cash cliffs before they hit, decouple capacity from FTEs, and tap tools, fractional support, your board + community as legitimate capacity—not just “nice to haves.”Episode Highlights:Dive Deeper: Episode 614: https://www.weareforgood.com/episode/614Episode 464: https://www.weareforgood.com/episode/463Thank you to our partners

Stephen Wolfram answers questions from his viewers about the history of science and technology as part of an unscripted livestream series, also available on YouTube here: https://wolfr.am/youtube-sw-qaTopics: How languages (and Wolfram Language) evolved - Leibniz, Babbage and early "computer science" ideas - Ancient civilizations and computational thinking

Congressman Massie lays out how he expects Friday's deadline to release the Epstein files will go, Bob Babbage and Trey Grayson go Inside Kentucky Politics with Renee Shaw, and a toy drive in Eastern Kentucky.

America's attacks on possible drug boats in the Caribbean is already controversial. Now critics are questioning the legality of one particular strike in September. What does this mean for the US secretary of war, Pete Hegseth? Why American firms are raising funding to explore gene-editing babies. And women in Japan face a long fight to play the national sport: sumo. In “Babbage” earlier this year we interviewed Chinese scientist He Jiankui, whose use of gene-editing technology on babies landed him a three-year prison sentence.Listen to what matters most, from global politics and business to science and technology—Subscribe to Economist Podcasts+For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account. Hosted on Acast. See acast.com/privacy for more information.

America's attacks on possible drug boats in the Caribbean is already controversial. Now critics are questioning the legality of one particular strike in September. What does this mean for the US secretary of war, Pete Hegseth? Why American firms are raising funding to explore gene-editing babies. And women in Japan face a long fight to play the national sport: sumo. In “Babbage” earlier this year we interviewed Chinese scientist He Jiankui, whose use of gene-editing technology on babies landed him a three-year prison sentence.Listen to what matters most, from global politics and business to science and technology—Subscribe to Economist Podcasts+For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account. Hosted on Acast. See acast.com/privacy for more information.

It is telling and troubling that the annual climate talking-shop's outcome did not even mention fossil fuels. We ask whether the COP process is still fit for purpose. Cryptocurrencies could be heading for an almighty fall: what would they take down with them? And the revealing vowels and diphthongs of whale communications. (Hear much more on animal communication in our series on “Babbage”: part 1 asks whether animals truly have language, and part 2 whether AI could translate it.) Additional audio courtesy of Project CETI. Get a world of insights by subscribing to Economist Podcasts+. For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account. Hosted on Acast. See acast.com/privacy for more information.

It is telling and troubling that the annual climate talking-shop's outcome did not even mention fossil fuels. We ask whether the COP process is still fit for purpose. Cryptocurrencies could be heading for an almighty fall: what would they take down with them? And the revealing vowels and diphthongs of whale communications. (Hear much more on animal communication in our series on “Babbage”: part 1 asks whether animals truly have language, and part 2 whether AI could translate it.) Additional audio courtesy of Project CETI. Get a world of insights by subscribing to Economist Podcasts+. For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account. Hosted on Acast. See acast.com/privacy for more information.

In my interview with Gary Kusin, co-founder of Babbage's — the company that eventually became GameStop. We discuss how he and his co-founder built the business from the ground up, the challenges they faced in the early days, and Gary's insights on where he sees technology heading in the future.

In this episode of the Defence Connect Podcast, host Steve Kuper is joined by Mike Pezzullo, former secretary of the Department of Home Affairs, and Ross Babbage, CEO of Strategic Forum and a Non-Resident Senior Fellow of the Center for Strategic and Budgetary Assessments, to discuss Australia's role and challenges in the deteriorating global order. The trio discuss a range of issues facing Australia and the broader Western alliance network at a time when authoritarian powers are on the march across the globe, including:  The triumph and importance of American power in securing a peace deal between Israel and Palestine that has continued to rage since 7 October 2023.  Predictions about Prime Minister Anthony Albanese's first official bilateral meeting with US President Donald Trump. Mounting political and public concern about Australia's lack of economic complexity and industrial capacity and its impact on national security and sovereignty. Real world examples of reindustrialisation in action across the United States and other like-minded nations that can provide models for Australia to emulate. Policy measures our leaders can implement to facilitate the rebuilding of Australia's industrial base and enhance our national security.  Enjoy the podcast,  The Defence Connect team

Benjamin Wallace's new book is The Mysterious Mr. Nakamoto: A Fifteen-Year Quest to Unmask the Secret Genius Behind Crypto.   It's the greatest whodunit. Whoever created Bitcoin became the world's richest person, yet we don't know who he is. In fact, we don't even know if it's one person. There have been other cases where identities have been hidden for a while: Mysterious Whistleblowers (Deep Throat) Mysterious Authors (Ferrante, Klein, Publius) Mysterious Artists (Banksy) Mysterious Spies / Hackers (Cambridge Five, QAnon figureheads, Cicada 3301) However, nothing tops the enigma of Satoshi Nakamoto. Watch my interview with Benjamin Wallace on the WanderLearn Show: Watch the Video Interview Questions for Benjamin Wallace In 60 seconds, tell us why we should be curious about who Satoshi Nakamoto was. What's the percentage chance that Satoshi Nakamoto is more than one person? What's the percentage chance that Satoshi Nakamoto is dead? Assuming he's alive, what's the percentage chance that Satoshi Nakamoto will voluntarily reveal himself in his old age or via a dead man's switch video? Who are your top 4 candidates for Satoshi Nakamoto? If those 4 candidates are in a pie chart, how big is the 5th piece of the pie: the Someone Else slice?  Although Nakamoto's OPSEC was impeccable, is it realistic to believe that he faked his Britishisms, his double-spacing after periods, and potentially running his prose & code through a stylometry mixer because he was certain that Bitcoin would become a multi-trillion-dollar asset? What new insights have you had since you wrote the book? What's the percentage chance that we will definitively solve this mystery like we solved the Deep Throat mystery? Or will the ending be more like Forrest Fenn (e.g., a partial conclusion because we know the treasure was found and by whom, but we don't know where)?  What surprised you in your investigation? It seems you want Nakamoto to be Hal Finney, but it's hard to believe he didn't tap into the fortune when his life was on the line. And why not admit to being Nakamoto when he was on his deathbed? Perhaps to protect his family from assaults? Perhaps because he collaborated with someone else and doesn't want to unmask him. But then he could admit that he was part of the Satoshi team and leave it at that. Who is Satoshi Nakamoto? In his book, Wallace writes that any plausible Nakamoto candidate should have the following characteristics: Software tools Coding quirks Age Geography Schedule Use of English Nationality Prose style Politics Life circumstances (How had Nakamoto found the time to launch Bitcoin? Why had he left the project when he did?" Resume ("I'm not a lawyer.") Emotional range (humble, confident, testy, appreciative) Motivation to create Bitcoin Rationale, and the foresight and skill, to create a bulletproof pseudonym (Who would bother wiping a crime scene clean before it was a crime scene? Who was already that good at privacy in 2008?) Monkish capacity to renounce a fortune Although this list severely restricts who Satoshi Nakamoto could be, it still leaves countless possibilities. Wallace, who has been trying to crack this mystery for 15 years, has yet to meet a candidate who checks all the boxes. Wallace refrains from declaring that he has solved the mystery, even though countless "detectives" have already done so. He interviews people who tell him, with 100% certainty, that Satoshi Nakamoto is: Nick Szabo James A. Donald Adam Back Hal Finney Peter Todd (according to HBO) Elon Musk Numerous other options It's tempting to select what you think is the most viable candidate, throw in a heavy dose of confirmation bias, and declare, "Mystery solved, Sherlock!" Plenty have done so. It requires great restraint to resist the temptation of calling it a day, and instead, persevere pugnaciously like Wallace has in what is the greatest whodunit of the 21st century.  Many suspects seem highly implausible. Elon Musk, for example, is a bombastic self-promoter who would love to proclaim he was the genius behind Bitcoin. It's unimaginable why he would keep his mouth shut. Hal Finney was a sincere, honest, and good guy. As he said many times when he was dying of ALS, he had no reason NOT to reveal that he was Satoshi Nakamoto. Therefore, it's not him, even though it would provide a neat explanation as to why the old Satoshi Nakamoto bitcoins haven't moved.  Adam Back is plausible, although ex-cypherpunk Jon Callas says, "The primary argument against Adam Back is he couldn't keep his mouth shut." Still, an engrossing 3-part documentary argues that Nakamoto is Adam Back. Here's the final episode: https://www.youtube.com/watch?v=XfcvX0P1b5g  Is Nick Szabo Satoshi Nakamoto? For several years, I believed Nick Szabo was Satoshi Nakamoto. It was an unoriginal deduction since Szabo is a popular choice among amateur Nakamoto detectives. Indeed, Szabo was one of Wallace's prime candidates for a long time. However, in his book, Wallace explains why Szabo has too many strikes against him: Szabo is a scatterbrain when it comes to projects. He doesn't focus on one thing for years. He juggles 150 balls. Nakamoto was laser-focused for 18 months. He told Jeremy Clark that Szabo "seemed to think that his bit gold was better" than Bitcoin. Clark also said Szabo is an "incoherent" presenter, whereas Nakamoto was "lucid."  Although Szabo is intensely private, he's not a complete recluse. He likes sharing ideas and getting public recognition.  Minor point: Satoshi Nakamoto wrote, "I'm not a lawyer," but Szabo is one. Although these points suggest Szabo is unlikely to be Satoshi, Szabo remains a strong Nakamoto candidate, given the absence of a perfect candidate. Besides, Clark's points are easily refuted. Just because Szabo implied Bitgold was better than Bitcoin means little. Szabo could say that to shake off people who think he's Satoshi. Or he could genuinely believe that aspects of Bitgold were superior to Bitcoin. Clark said Szabo "seemed to think..." He didn't say, "Szabo emphatically said..." Also, I listened to Szabo speak for 2.5 hours on the Tim Ferriss Show, and he sounded plenty lucid to me.  Szabo is a decent speaker. Naturally, Szabo always denies he's Satoshi. As Wallace says, denying you're not the guy proves nothing. Mark Felt was an obvious suspect for being the Deep Throat in the Watergate scandal. He denied for decades. And guess what? He was Deep Throat! Sometimes the most obvious suspect is the criminal (think O.J. Simpson). Is James A. Donald Satoshi Nakamoto? After reading The Mysterious Mr. Nakamoto, I added another suspect to my short list: James A. Donald. Satoshi Nakamoto used the rare term "hosed" a few times. Donald did so twice.  Furthermore, Donald was the first person to respond to Satoshi Nakamoto's original Bitcoin post, albeit in a critical way. He has various other attributes that Satoshi Nakamoto shares (read the book to see them all).  However, Donald is rough around the edges, whereas Satoshi Nakamoto was silky smooth, polite, and unoffensive. Again, James A. Donald is no slam dunk candidate. Nobody is. Hence, the mystery endures.  The only negative aspect about this book is that it may provide too much detail for the casual reader with limited interest in this mystery. If you're just looking for the answer, I'll tell you now: we do not know who Satoshi Nakamoto is.  For Satoshi sleuths, there is no better resource than The Mysterious Mr. Nakamoto: A Fifteen-Year Quest to Unmask the Secret Genius Behind Crypto. It delves deeper and wider than any video, article, or book about the identity of Satoshi Nakamoto. Believe me, I've gone down that rabbit hole. Why should we care who Satoshi Nakamoto is? Many argue we don't need to know who Satoshi Nakamoto is because: Knowing his identity could taint the "immaculate conception" of Bitcoin because we might learn that Satoshi Nakamoto was an asshole. We should respect Satoshi Nakamoto's right to privacy. He obviously wanted to be pseudonymous, so let him be. If Satoshi Nakamoto is alive, it would imbue him with too much power, especially over the Bitcoin protocol.  I strongly disagree with this lack of curiosity. Why? There's a chance that in the 25th century, historians will consider Bitcoin one of the top 10 inventions of all time. I'm not saying that Bitcoin will be around in the 25th century, but something like it will exist and be the global currency, and historians will link its existence to Bitcoin. In 2001, Arthur C. Clarke predicted that by 2016, "All existing currencies are abolished. A universal currency is adopted based on the 'megawatt hour.'" Eight years before Clarke's prediction, Bitcoin was created. Although Clarke was wrong about other currencies being abolished,  Bitcoin's value is loosely correlated with its energy consumption. I explain why Bitcoin is worth anything. Consider the Top 10 Inventions and Their Inventors Imagine if we didn't know who these inventors were: The Printing Press - Johannes Gutenberg (c. 1440): This invention revolutionized communication, allowing for the mass production of books and the widespread dissemination of knowledge, leading to the Renaissance and the Scientific Revolution. The Electric Light Bulb - Thomas Edison (1879): While others experimented with electric lighting, Edison created a practical, long-lasting, and commercially viable incandescent light bulb, which transformed society by extending the day and enabling new industries. The Telephone - Alexander Graham Bell (1876): The telephone revolutionized long-distance communication, enabling people to speak to each other across vast distances in real time. The Steam Engine - James Watt (1778): Watt's improvements to earlier steam engines significantly increased their efficiency, powering the Industrial Revolution and leading to the mechanization of factories, transportation, and other industries. The Automobile - Karl Benz (1885): Benz is credited with creating the first practical automobile powered by an internal combustion engine, ushering in the age of personal transportation and reshaping urban and rural life. Alternating Current (AC) Electrical System - Nikola Tesla (late 1880s): While Edison championed direct current (DC), Tesla's work on AC made it possible to transmit electricity over long distances, laying the groundwork for modern electrical grids. The Airplane - Orville and Wilbur Wright (1903): The Wright brothers achieved the first successful controlled, powered flight of a heavier-than-air aircraft, fundamentally changing travel, commerce, and warfare. Penicillin - Alexander Fleming (1928): Fleming's discovery of the first antibiotic revolutionized medicine by providing a cure for many bacterial infections, saving millions of lives. The Internet / World Wide Web - Vint Cerf and Bob Kahn (Internet, 1970s) & Tim Berners-Lee (World Wide Web, 1989): These inventions created a global network of information and communication, transforming almost every aspect of modern society, from business and education to personal life. The Computer - Charles Babbage (early 19th century): Babbage's designs for the "Analytical Engine" laid the theoretical groundwork for modern computers. Later, inventors like John Atanasoff, Alan Turing, and others developed the first electronic and programmable computers. Imagine if we had no clue who invented penicillin or the telephone. Wouldn't historians do their best to figure that out, especially since they were recent and impactful inventions? Would you just shrug your shoulders and say, "Who cares? My telephone works." Sure, many wouldn't give a shit. However, for other, more curious minds, we'd like to know.  Major Inventions with Unknown Inventors Here are four major inventions whose creator is a mystery: The Wheel: The invention of the wheel is one of the most important technological advancements in human history, enabling transportation and mechanization. Archaeological evidence suggests it originated in Mesopotamia around 3500 BC, but there is no record of who first conceived of it. The challenge wasn't just creating the wheel itself, but also the wheel-and-axle system, which required precise engineering. Writing: The development of writing systems enabled the permanent storage and transmission of information, transforming human society. The earliest known writing system, cuneiform, emerged in Sumer (ancient Mesopotamia) around 3400 BC. However, like the wheel, it was likely the result of a gradual process of development by many different people, not the work of a single inventor. Fire making: Some person probably rubbed two sticks together, and the rest is history. Since we can't know who that individual was, it would still be fascinating to know where it started and if it was developed in more than one place independently, like Calculus.  Bitcoin: Yeah, it's a major invention. It's been the best-performing asset since 2010, it's worth more than any company, and Satoshi Nakamoto is the wealthiest person ever. It has sparked a multi-trillion-dollar industry in just 15 years. So, yes, it's important, and yet we don't know who created it. Verdict: 10 out of 10 stars! Admittedly, I'm a Bitcoin fan who has produced many videos and articles about the first cryptocurrency, so I'm biased. Still, if you love a perplexing mystery, you will love trying to solve this one. The good news is that we haven't solved it yet.  My Satoshi Nakamoto Fantasy There's a good chance that Satoshi Nakamoto is around my age. If so, he also has a 30-year life expectancy.  I hope that in 2050, a video appears on the Internet that shows an old man who says, "I am Satoshi Nakamoto. To prove it, I will do what no Satoshi pretender has been able to do: move the 'Satoshi' coins that have been dormant since I mined them in 2009." He records himself and his computer screen, and with a few clicks and keyboard taps, the transactions get broadcast onto the Bitcoin blockchain for all to see.    Next, he says, "I am donating my one million bitcoins to the Bitcoin Core for ongoing maintenance and to the following charities." Or perhaps he'll use the one million Bitcoins to create a Bitcoin node on the Moon. Or perhaps he will "burn" his Bitcoin, reducing the total BTC supply to 20 million coins, not 21 million. Regardless, I hope Nakamoto will finally unmask himself, just like Mark Felt (aka Deep Throat) did when he was 91 (he died at 95).  Yeah, this fantasy is unlikely, but we can dream, can't we? Connect Send me an anonymous voicemail at SpeakPipe.com/FTapon You can post comments, ask questions, and sign up for my newsletter at https://wanderlearn.com. If you like this podcast, subscribe and share!  On social media, my username is always FTapon. Connect with me on: Facebook Twitter YouTube Instagram TikTok LinkedIn Pinterest Tumblr   Sponsors 1. My Patrons sponsored this show! Claim your monthly reward by becoming a patron for as little as $2/month at https://Patreon.com/FTapon 2. For the best travel credit card, get one of the Chase Sapphire cards and get 75-100k bonus miles! 3. Get $5 when you sign up for Roamless, my favorite global eSIM with its unlimited hotspot & data that never expires! Use code LR32K 4. Or get 5% off when you sign up with Saily, another global eSIM with a built-in VPN & ad blocker. 5. Get 25% off when you sign up for Trusted Housesitters, a site that helps you find sitters or homes to sit in. 6. Start your podcast with my company, Podbean, and get one month free! 7. In the United States, I recommend trading cryptocurrency with Kraken.  8. Outside the USA, trade crypto with Binance and get 5% off your trading fees! 9. For backpacking gear, buy from Gossamer Gear.

This week on Nonprofit Lowdown, I'm joined by my business bestie and returning favorite, Brooke Richie-Babbage — strategist, executive, founder of Bending Arc, and all-around AI powerhouse.We're diving deep into how AI is actually being used in the nonprofit space — beyond the hype.We discuss:How AI can support (but not replace) your thinkingUse cases for AI in finance, fundraising, and data analysisTools for donor segmentation and engagementWhy donor trust and ethical data use matter more than everHow to create guardrails, policies, and inclusive practices around AIWe also talk about the messy but necessary process of innovation, how to lead AI adoption with integrity, and why nonprofits must be part of the AI conversation — or risk being left behind.If you're curious about AI but unsure where to start, this one's packed with insights, real examples, and practical tools you can use right away.Important Links:Zeffy: https://www.zeffy.com/register?&utm_source=Rhea_Wong Connect with Brooke: https://www.linkedin.com/in/bjrichiebabbage/ Fast Forward: https://www.ffwd.org/ai-for-humanity How to Train ChatGPT:  https://go.rheawong.com/annual-fundraising-plan-tracker1-3127-4300 Upcoming Events: https://www.rheawong.com/events/ My Big Ask Gifts Program: https://go.rheawong.com/big-ask-gifts-program My Book, Get That Money Honey: https://go.rheawong.com/get-that-money-honey My Newsletter: https://www.rheawong.com/ 

Courage is Contagious: Voices Uniting Against Human Trafficking Synopsis: Teresa Velardi sits down with author Andi Buerger and contributing authors Lisa Babbage, Chris Meek, and Eric Caron to discuss the powerful new book, Voices Against Trafficking: Courage is Contagious – Uniting Voices and Nations in the War Against Human Slavery. At a time when true heroes can seem scarce, Voices Against Trafficking brings together extraordinary accounts from ordinary people who refused to look away in the face of injustice. These first-hand narratives spotlight individuals who saw something, said something, and took action—changing the course of lives forever. The stories remind us that the courage of a single person can create ripples of hope that reach across communities and even nations. Andi Buerger, a survivor of brutal child sex trafficking, shares her journey from victim to internationally recognized advocate who has rescued hundreds of at-risk teens through her nonprofit work. Lisa Babbage brings her expertise as an educator, nonprofit leader, and survivor of abuse, working to restore dignity to women and children. Chris Meek, co-founder of SoldierStrong, combines lessons on leadership, resilience, and humanitarian service from decades of working with U.S. veterans and global causes. Eric Caron, a decorated former U.S. Special Agent, offers a law enforcement and national security perspective on dismantling trafficking networks and rescuing victims. Together, they discuss the harsh realities of human trafficking, the systemic challenges in combating it, and the urgent need to unite voices from all walks of life in this fight. This compelling conversation will challenge listeners to confront the uncomfortable truth about modern-day slavery—and inspire them to believe that courage truly is contagious. Guests Andi Burger: Andi Buerger, JD is an international speaker, author, and advocate for victims of human trafficking & exploitation.  Andi herself was a victim of child sex trafficking and unspeakable abuses by family members for 17 years.She founded Beulah's Place, which provided temporary shelter services to at-risk unsheltered teens for 14 years.  300+ youth were successfully rescued and assisted earning national recognition. Andi later founded Voices Against Trafficking(VAT) to speak for those who cannot speak for themselves — the voiceless victims of human trafficking and exploitation. VAT advocates for the protection of every human's rights regardless of race, gender, culture, or socio-economic status.  Voices Against Trafficking-The Strength of Many Voices Speaking As One, gives a portion of proceeds from each sale to survivors of child abuse and trafficking, as does Andi's first book,  A Fragile Thread of Hope - One Survivor's Quest to Rescue. Andi launched Voices Of Courage magazine in 2023.  It is distributed internationally and accepted into the U.S. Library of Congress. It honors everyday heroes who selflessly fight to protect human rights. These champions come from all walks of life to change communities and the world for the better. A television series by the same title debuts in 2025. Chris Meek: Dr. Chris Meek is co-founder, chairman, and CEO of SoldierStrong, a 501(c)(3) charitable organization that focuses on helping America's servicemen, women, and veterans take their next steps forward. He has been recognized for his work in philanthropy with the President's Call to Service Award (2011), March of Dimes Franklin Delano Roosevelt Outstanding Corporate Citizen Award (2012), Syracuse University's Orange Circle Award (2014), the ACT-IAC “Game Changer” Award (2020), and was named a “Face of Philanthropy” by the Chronicle of Philanthropy (2021). In addition to Meek's work as a philanthropist, he has been a financial services executive for over 25 years working at S&P Global, State Street Global Advisors, and Goldman Sachs. He holds a BA in economics and political science from Syracuse University, an MBA in financial management from Pace University in New York City, and an MPA from the Maxwell School at Syracuse University. He is a doctoral candidate in organizational change and leadership at the University of Southern California. Meek serves as adjunct professor at the Maxwell School of Citizenship and Public Affairs at Syracuse University, where he teaches graduate and undergraduate courses on nonprofit management and board governance. He shares his experiences and discusses resiliency, empowerment, and leadership through adversity on his weekly podcast, “Next Steps Forward with Chris Meek,” via the VoiceAmerica network's Empowerment Channel. Next Steps Forward is his first book.  Lisa Babbage: For the past decade, Lisa Babbage has been involved with a variety of causes all aimed at restoring women and children through education & needs-based support, and workforce development. This passion emerged from her own need, recovering from childhood sexual abuse and homelessness. Since working through her personal trauma, Lisa went on to receive a doctorate in Public Policy and Nonprofit Leadership and is recently received her second Masters, this time in STEM Education. After twenty years of educating Georgia's children as a K-12 educator and TEACH Gwinnett Supervisor, and over ten years in the mission field of Atlanta, Lisa says her work has only just begun. She is a Charter member of Voices Against Trafficking and works to provide temporary housing for at-risk women in her city through her own nonprofit Maranatha House. As the current Vice President of the Christian Institute of Public Theology, her focus is on enforcing Georgia's Character Education Laws. She has partnered with countless other organizations to provide food, resources, tutoring, Ndestructible 7 Life Coaching, and encouragement to hundreds. She is the author of over twenty books, most of which are focused on restoration, and is a documentary filmmaker. In 2020, she became an Emancipation Brand Ambassador for COL1972 and spokesperson for GAE Coalition. Previously, Lisa served in an Executive Board capacity for state affiliates of No Left Turn in Education, Women for Trump, and Rotary International. Rev. Dr. Babbage is the current First Vice Chair of the Georgia Black Republican Council.  Eric Caron: Eric J. Caron is a distinguished former U.S. Special Agent and diplomat known for spearheading impactful covert operations on a global scale, focusing on transnational crime and national security. Eric has been instrumental in bringing dangerous criminals to justice and rescuing dozens of children from the horrors of human trafficking. Currently, as the Special Liaison for law enforcement at Voices Against Trafficking and co-founder of the Stop Child Soldiers Foundation, Eric's passion for public safety is matched only by his expertise as an international security consultant preventing human & wildlife trafficking in the U.S. & Africa. His unwavering commitment has earned him prestigious accolades, including the U.S. Attorney General's Award for National Security and a Citation from the Secretary General of INTERPOL. A highly sought-after authority in national security, Eric's perspectives resonate in major publications like the Washington Times, Epoch Times and Voices of Courage. He has also made guest appearances on Newsmax, One America News Network (OAN), Christian Broadcast Network (CBN), and numerous podcasts. In his compelling book, Switched On: The Heart and Mind of a Special Agent, Eric invites readers into a world of intrigue and courage, sharing gripping stories and invaluable life lessons from his extraordinary career. From investigating the CIA and countering the ambitions of nations like Russia and China regarding weapons of mass destruction, to navigating the complexities of Dubai and Afghanistan, his narrative not only captivates but also inspires audiences to live a life that is truly "Switched On."  Purchase the Book: https://amzn.to/4oVSiXm Video Version: https://www.youtube.com/live/LhxsKDNYUuE?si=v3n5MxPf5UHTppsu Chat with Teresa during Live Show with Video Stream: write a question on YouTube Learn more about Teresa here: https://www.webebookspublishing.com    http://authenticendeavorspublishing.com/

I have to say a big thank you to Adi and Janice who hosted me at their farm Kalmoesfontein this week as part of the Swartland Revolution events they're running— I was invited to give a little talk about Jan Smuts of the Swartland and relished the opportunity to delve deeply into a Great South African's early life. And to the folks that came to ask questions and be part of the event, thank you too for such a warn reception. We're going to deal with two main topics in the years 1871 leading into 1872 - One was the installation of Sir John Molteno as the First Prime Minister of the Cape of Good Hope which marked the start of responsible government in the territory. But the other really big event of 1872 was the death of Zulu king Mpande kaSenzangakhona, leaving the way open for Cetshwayo kaMpande to seize the reins of power. It wasn't going to be that simple of course. Let's have a quick squizz at what was going on globally in 1871. The Franco-Prussian war ended, leading to the Proclamation the German Empire in January. The North German federation and South German States were united in a single nation state and the King of Prussia was declared as the German Emperor Wilhem the first. Germany officially came into being for the first time. Otto von Bismarck would soon become the First Chancellor of the German Empire. In French Algeria, the Mokrani Rebellion against colonial rule broke out in March 71, in March the Paris Commune was formally established in France. The Commune governed Paris for two months, promoting an anti-religious system, an eclectic mix of many 19th-century schools of thought. Policies included the separation of church and state, the reduction of rent and the abolition of child labor. The Commune closed all Catholic churches and schools in Paris and a mix of reformism and revolutionism took hold — a hodge podge of folks who pushed back against the French establishment. By late May 71 the commune had been crushed in the semaine sanglante, the Bloody Week, where at least 15 000 communards were executed by loyalist troops. More than 43 000 communards were imprisoned. The Paris Commune left an indelible mark on Karl Marx and Friedrich Engels — two men who, in turn, would go on to cast a long, indirect shadow over the course of world history. In June 1871, the United States launched an assault on the Han River forts in Korea, hoping to pry open Korean markets for American trade. Washington wasn't bothering with tariffs that year — gunboats were quicker. Charles Babbage died on boxing Day, 26 December 1871. A man of many labels—mathematician, philosopher, inventor, mechanical engineer—but one overriding legacy: he imagined the computer before electricity even entered the equation. Babbage's difference engine was the first mechanical attempt to automate calculation - it was his analytical engine that quietly cracked open the future. It carried, in brass and gears, the essential ideas of the modern digital computer—logic, memory, and even programmability. His inspiration? The Jacquard loom, which used punched cards to weave patterns into silk. Babbage observed this and thought: if a loom could follow instructions to weave flowers, why not numbers? Hidden in that question was the dawn of the information age—and even the first glimmer of a printer. The popular movement towards responsible government had arisen in the early 1860s, led by John Molteno - and in a future podcast I will spend more time on his life - a fascinating character who was the first South Africa to attempt to export fruit. He married a coloured woman called Maria in 1841 but catastrophe struck when she and their young son died in childbirth and stricken by grief, he joined a Boer Commando fighting in one of the early Frontier Wars. So it was then that on 22nd October 1872 Cetshwayo summoned all the indunas and izikhulu to kwaNondwengu to announce that King Mpande had died.

In this episode of Mainframe Coven, Jessielaine Punongbayan (Product Manager, Dynatrace) and Richelle Anne Craw (Software Engineer, Beta Systems Software) look back at a time when women were central to computing and examine how and why that changed, even though the work didn't. Together they reflect on software engineering, cultural bias, institutional gatekeeping, and the motivation to rewrite the narrative.Mainframe Coven is a 10-part mini-series honoring the past, present, and future women of IT. It's about real stories from the essential yet unseen minds behind the machines.The podcast is sponsored by the Open Mainframe Project, a Linux Foundation project that aims to build community and adoption of Open Source on the mainframe by eliminating barriers to Open Source adoption on the mainframe, demonstrating the value of the mainframe.For a transcript of this episode, visit https://openmainframeproject.org/mainframe-coven/mainframe-coven-when-computers-wore-skirtsLinks and Resources Mentioned in the Episode:- She Was a Computer When Computers Wore Skirts: https://www.nasa.gov/centers-and-facilities/langley/she-was-a-computer-when-computers-wore-skirts/- Zeros and Ones: Digital Women and the New Technoculture by Sadie Plant: https://www.4thestate.co.uk/products/zeros-and-ones-digital-women-and-the-new-technoculture-sadie-plant-9781857026986/- Lovelace & Babbage and the creation of the 1843 'notes' by J. Fuegi and J. Francis, in IEEE Annals of the History of Computing, vol. 25, no. 4, pp. 16-26, Oct.-Dec. 2003: https://doi.org/10.1109/MAHC.2003.1253887- Broad Band: The Untold Story of the Women Who Made the Internet by Claire Evans: https://www.penguinrandomhouse.com/books/545427/broad-band-by-claire-l-evans/- Pioneer Programmer: Jean Jennings Bartik and the Computer That Changed the World by Jean Jennings Bartik: https://www.amazon.com/Pioneer-Programmer-Jennings-Computer-Changed/dp/1612480861/- The women of ENIAC by W. B. Fritz, in IEEE Annals of the History of Computing, vol. 18, no. 3, pp. 13-28, Fall 1996: https://doi.org/10.1109/85.511940- Jean J. Bartik and Frances E. “Betty” Snyder Holberton, interview by Henry Tropp, April 1973, Computer Oral History Collection, Archives Center, National Museum of American History, Smithsonian Institution: https://mads.si.edu/mads/id/NMAH-AC0196_bart730427/- When Computers Were Women by Jennifer S. Light, Technology and Culture, vol. 40, no. 3, 1999: https://www.jstor.org/stable/25147356- ENIAC Programmers Project: https://eniacprogrammers.org/- Great Unsung Women of Computing: The Computers, The Coders and The Future Makers: https://www.wmm.com/catalog/film/great-unsung-women-of-computing-the-computers-the-coders-and-the-future-makers/- The Untold History of Women in Science and Technology (White House Archives): https://obamawhitehouse.archives.gov/women-in-stem/- The Queen of Code, directed by Gillian Jacobs. FiveThirtyEight, 2015: https://vimeo.com/118556349/- “Making Programming Masculine” In Gender Codes: Why Women Are Leaving Computing by Nathan Ensmenger: https://homes.luddy.indiana.edu/nensmeng/posts/2010/09/09/misa2010/- The Computer Boys Take Over: Computers, Programmers, and the Politics of Technical Expertise by Nathan Ensmenger: https://thecomputerboys.com/

Send us a textNonprofit leaders feeling the weight of challenging times need more than grit to thrive—they need resilient organizations built on sustainable systems and supportive networks. Brooke Ritchie-Babbage shares her S.T.R.O.N.G. framework for building nonprofit stability while growing impact.• Strategic clarity keeps everyone focused on the "cathedral" they're building beyond daily brick-laying work• Well-designed tools and systems create the interstitial tissue connecting teams without bottlenecks• Resources include not just funding but sustainable approaches like monthly giving programs • Ownership means everyone understands their role and has appropriate decision-making authority• Networked capacity extends organizational roots beyond staff to partners, advisors, and collaborators• Governance provides appropriate oversight and accountability that evolves as organizations grow• Growth and stability aren't competing priorities—stability is the foundation for sustained growth• Burnout isn't a badge of honor or personal failing but a structural mismatch requiring systemic solutions• Building recovery and assessment into organizational rhythms is essential for long-term impact• No leader should try to go it alone—find coaches, mentors, and peer communities for supportCheck out Brooke's podcast at https://brookerichiebabbage.com/podcast/Brooke's BioBrooke Richie-Babbage is a nonprofit growth strategist and social impact advisor. She is the founder and CEO of Bending Arc, a social impact strategy firm that supports the launch and sustainable growth of high-impact nonprofits, and the host of Nonprofit Mastermind Podcast.For the past 23 years, Brooke has worked as a lawyer, nonprofit leader, and social entrepreneur. She has founded and led multiple successful organizations and initiatives, including the Resilience Advocacy Project (RAP), where she served as founder and Executive Director for 11 years, the Sterling Network NYC and the NetLab Initiative, both initiatives of the Robert Sterling Clark Foundation, where she served as Director of Network Initiatives for six years, and the Social Justice Accelerator (SJA), an initiative of the Urban Justice Center, where she has served as SJA Director since 2019.  Brooke received her JD and MPP from Harvard and her BA from Yale. She lives in Brooklyn with her husband and two sons.Brooke Richie-Babbage | LinkedIn Like what you heard? Please like and share wherever you get your podcasts! Connect with Ann: Community Evaluation Solutions How Ann can help: · Support the evaluation capacity of your coalition or community-based organization. · Help you create a strategic plan that doesn't stress you and your group out, doesn't take all year to design, and is actionable. · Engage your group in equitable discussions about difficult conversations. · Facilitate a workshop to plan for action and get your group moving. · Create a workshop that energizes and excites your group for action. · Speak at your conference or event. Have a question or want to know more? Book a call with Ann .Be sure and check out our updated resource page! Let us know what was helpful. Music by Zach Price: Zachpricet@gmail.com

The world is changing—fast. As a nonprofit leader, how do you hold fast to your mission when everything feels uncertain? In this episode of our new Hold Fast series, we're joined by Brooke Richie Babbage, founder of Bending Arc Consulting and host of the Nonprofit Mastermind Podcast, to break down what real resilience looks like in practice.Brooke introduces her powerful framework for building a resilient organization—one that doesn't just survive uncertainty but thrives through it. She shares the three core elements of resilience (Strategic Clarity, Capacity, and Capital) and walks us through her 4-step process for designing organizations that are strong, adaptable, and built to last.If you're leading a small team with limited capacity, feeling overwhelmed by uncertainty, or struggling to scale your impact, this episode is for you. You'll walk away with practical, actionable steps to stabilize and grow your organization.Plus, Brooke's “One Good Thing” will change the way you think about resilience in your daily work.Tune in to learn: ✅ What resilience really means for nonprofit organizations ✅ How to build clarity, capacity, and capital—even with a small team ✅ A step-by-step approach to designing a resilient, high-impact nonprofit ✅ How to move from feeling stuck to executing with confidenceLet's build something that lasts. Listen now.

Will AI take our jobs? Does AI boost economic productivity? Is it a choice of going green or going digital? Our host Paul Gordon talks to ECB colleagues António Dias da Silva, Guzmán González-Torres Fernández and Miles Parker to find out what AI means for the economy, especially for productivity, job prospects and energy supply. The views expressed are those of the speakers and not necessarily those of the European Central Bank. Published on 9 April 2025 and recorded on 3 April 2025. In this episode: 00:55 Is AI replacing jobs? Can AI ever replace jobs in journalism or film-making? Will AI be our next podcast host? 02:14 Is AI a job changer? How are new technologies changing our jobs? 04:00 Age, education and gender Who uses AI and how do people feel about it? Does AI usage differ based on age, education and gender? 06:57 Sectors with the highest AI usage Which sectors use AI the most? What's behind the different attitudes towards AI in different areas of the economy? 09:00 Corporate usage of AI What do companies need to effectively use AI? 11:02 How does AI affect productivity? How can AI be put to good use? To what extent can Europe's economy grow with current AI usage? Is the world ready for AI? 13:29 The role of policymakers How can policymakers make it easier for companies to use AI? 15:10 AI and energy consumption How much energy does AI need? How much energy does ChatGPT use for one search? Will energy demand go up or down? 17:15 Go green or go digital? Do we need to choose or does AI allow both? How could AI help the green transition? 19:10 Obstacles What are the roadblocks to the green and digital transitions? What investment is needed to make these transitions a success? What else needs to be done? 21:50 Our guests' hot tips António, Guzmán and Miles share their hot tips with our listeners. Further reading: The ECB Blog: AI adoption and employment prospects https://www.ecb.europa.eu/press/blog/date/2025/html/ecb.blog20250321~6af1337b6b.en.html The ECB Blog: AI versus green: clash of the transitions? https://www.ecb.europa.eu/press/blog/date/2025/html/ecb.blog20250325~ed12b0ff35.en.html The ECB Blog: AI can boost productivity – if firms use it https://www.ecb.europa.eu/press/blog/date/2025/html/ecb.blog20250328~60c0a587f7.en.html Hot tip from António: Tech-focused podcast Babbage by The Economist https://www.economist.com/audio/podcasts/babbage Hot tip from Guzmán: The ECB recent conference on “The transformative power of AI: economic implications and challenges” https://www.ecb.europa.eu/press/conferences/html/20250401_transformative_power_of_ai.en.html Hot tip from Miles: International Energy Agency website https://www.iea.org/ ECB Instagram https://www.instagram.com/europeancentralbank/ European Central Bank www.ecb.europa.eu ECB Banking Supervision https://www.bankingsupervision.europa.eu/home/html/index.en.html

In this episode of the Matthews Mentality Podcast, host Kyle Matthews sits down with legendary entrepreneur and business leader Gary Kusin. Gary shares his inspiring journey from co-founding globally recognized brands like Babbage's (which evolved into GameStop) and Laura Mercier Cosmetics to leading Kinko's transformation and its eventual sale to FedEx. This discussion covers Gary's early life experiences, pivotal career decisions, the challenges and triumphs in the business world, and his unwavering commitment to giving back to underserved communities. Tune in to hear about his incredible professional arc, key lessons learned, and his insights on fostering a success-driven mentality.

It's the UConn Popcast, and when did we really start dreaming about the promise, and the danger, of artificial intelligence? When ChatGPT was released in 2022? When IBMs Deep Blue defeated Chess world champion Garry Kasparov in 1997? When Stanley Kubrick introduced us to HAL 9000 in 1968? Or perhaps you think it was much earlier. Maybe we have had the dream of AI since the development of the first computers by Von Neumann, or even earlier, by Babbage. Or maybe you think the dawning of the age of science itself is ground zero for our thoughts of artificial intelligence. Kevin LaGrandeur traces our dreams - and fears - of artificial intelligence back way further than this. LaGrandeur argues that ideas of artificial slaves can be found in the writing of Aristotle, in the Renaissance-era idea of the Homunculus, in the Jewish legend of the Golem. LaGrandeur, a longtime professor at the New York Institute of Technology and now an independent scholar and Director of Research at the Global AI Ethics Institute, has more than 25 years of experience teaching, writing and speaking about technology and society. We were thrilled to be able to have a wide-ranging conversation with Professor LaGrandeur about his pathbreaking research on Androids and intelligent networks in early modern culture, and his current work on the ethics and implications of AI. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

It's the UConn Popcast, and when did we really start dreaming about the promise, and the danger, of artificial intelligence? When ChatGPT was released in 2022? When IBMs Deep Blue defeated Chess world champion Garry Kasparov in 1997? When Stanley Kubrick introduced us to HAL 9000 in 1968? Or perhaps you think it was much earlier. Maybe we have had the dream of AI since the development of the first computers by Von Neumann, or even earlier, by Babbage. Or maybe you think the dawning of the age of science itself is ground zero for our thoughts of artificial intelligence. Kevin LaGrandeur traces our dreams - and fears - of artificial intelligence back way further than this. LaGrandeur argues that ideas of artificial slaves can be found in the writing of Aristotle, in the Renaissance-era idea of the Homunculus, in the Jewish legend of the Golem. LaGrandeur, a longtime professor at the New York Institute of Technology and now an independent scholar and Director of Research at the Global AI Ethics Institute, has more than 25 years of experience teaching, writing and speaking about technology and society. We were thrilled to be able to have a wide-ranging conversation with Professor LaGrandeur about his pathbreaking research on Androids and intelligent networks in early modern culture, and his current work on the ethics and implications of AI. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/history

It's the UConn Popcast, and when did we really start dreaming about the promise, and the danger, of artificial intelligence? When ChatGPT was released in 2022? When IBMs Deep Blue defeated Chess world champion Garry Kasparov in 1997? When Stanley Kubrick introduced us to HAL 9000 in 1968? Or perhaps you think it was much earlier. Maybe we have had the dream of AI since the development of the first computers by Von Neumann, or even earlier, by Babbage. Or maybe you think the dawning of the age of science itself is ground zero for our thoughts of artificial intelligence. Kevin LaGrandeur traces our dreams - and fears - of artificial intelligence back way further than this. LaGrandeur argues that ideas of artificial slaves can be found in the writing of Aristotle, in the Renaissance-era idea of the Homunculus, in the Jewish legend of the Golem. LaGrandeur, a longtime professor at the New York Institute of Technology and now an independent scholar and Director of Research at the Global AI Ethics Institute, has more than 25 years of experience teaching, writing and speaking about technology and society. We were thrilled to be able to have a wide-ranging conversation with Professor LaGrandeur about his pathbreaking research on Androids and intelligent networks in early modern culture, and his current work on the ethics and implications of AI. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/intellectual-history

Babbage, C. (1830). Reflections on the Decline of Science in England: And on Some of Its Causes. B. Fellowes. Sokal, A. D. (1996). Transgressing the Boundaries: Toward a Transformative Hermeneutics of Quantum Gravity. Social Text, 46/47, 217. https://doi.org/10.2307/466856 Grievance studies: https://en.wikipedia.org/wiki/Grievance_studies_affair It is legal to own and/or read Mein Kampf in The Netherlands (and Germany). Hand, D. (2007). Deception and dishonesty with data: Fraud in science. Significance, 4(1), 22–25. https://doi.org/10.1111/j.1740-9713.2007.00215.x Gross, C. (2016). Scientific Misconduct. Annual Review of Psychology, 67(Volume 67, 2016), 693–711. https://doi.org/10.1146/annurev-psych-122414-033437 Paolo Macchiarini: https://www.science.org/content/article/macchiarini-guilty-misconduct-whistleblowers-share-blame-new-karolinska-institute The Truth about China's Cash-for-Publication Policy: https://www.technologyreview.com/2017/07/12/150506/the-truth-about-chinas-cash-for-publication-policy/ Claudine Gay plagiarism: https://www.plagiarismtoday.com/2024/01/22/harvard-releases-details-of-claudine-gay-investigation/ Many Co-Authors: https://manycoauthors.org/ Paper describing a replication study where students make up data: Azrin, N. H., Holz, W., Ulrich, R., & Goldiamond, I. (1961). The control of the content of conversation through reinforcement. Journal of the Experimental Analysis of Behavior, 4, 25–30. Francesca Gino defamation case dismissed: https://www.thecrimson.com/article/2024/9/12/judge-dismisses-gino-lawsuit-defamation-charges/ Retractions in Social Influence of the work of Guéguen: https://www.tandfonline.com/doi/full/10.1080/15534510.2024.2431408, https://www.tandfonline.com/doi/full/10.1080/15534510.2024.2431415, https://www.tandfonline.com/doi/full/10.1080/15534510.2024.2431421   Diederik Stapel's book: http://nick.brown.free.fr/stapel/FakingScience-20161115.pdf   Merton, R. K. (1957). Priorities in Scientific Discovery: A Chapter in the Sociology of Science. American Sociological Review, 22(6), 635–659. https://doi.org/10.2307/2089193

We are recording our next big recap episode and taking questions! Submit questions and messages on Speakpipe here for a chance to appear on the show!Also subscribe to our calendar for our Singapore, NeurIPS, and all upcoming meetups!In our first ever episode with Logan Kilpatrick we called out the two hottest LLM frameworks at the time: LangChain and Dust. We've had Harrison from LangChain on twice (as a guest and as a co-host), and we've now finally come full circle as Stanislas from Dust joined us in the studio.After stints at Oracle and Stripe, Stan had joined OpenAI to work on mathematical reasoning capabilities. He describes his time at OpenAI as "the PhD I always wanted to do" while acknowledging the challenges of research work: "You're digging into a field all day long for weeks and weeks, and you find something, you get super excited for 12 seconds. And at the 13 seconds, you're like, 'oh, yeah, that was obvious.' And you go back to digging." This experience, combined with early access to GPT-4's capabilities, shaped his decision to start Dust: "If we believe in AGI and if we believe the timelines might not be too long, it's actually the last train leaving the station to start a company. After that, it's going to be computers all the way down."The History of DustDust's journey can be broken down into three phases:* Developer Framework (2022): Initially positioned as a competitor to LangChain, Dust started as a developer tooling platform. While both were open source, their approaches differed – LangChain focused on broad community adoption and integration as a pure developer experience, while Dust emphasized UI-driven development and better observability that wasn't just `print` statements.* Browser Extension (Early 2023): The company pivoted to building XP1, a browser extension that could interact with web content. This experiment helped validate user interaction patterns with AI, even while using less capable models than GPT-4.* Enterprise Platform (Current): Today, Dust has evolved into an infrastructure platform for deploying AI agents within companies, with impressive metrics like 88% daily active users in some deployments.The Case for Being HorizontalThe big discussion for early stage companies today is whether or not to be horizontal or vertical. Since models are so good at general tasks, a lot of companies are building vertical products that take care of a workflow end-to-end in order to offer more value and becoming more of “Services as Software”. Dust on the other hand is a platform for the users to build their own experiences, which has had a few advantages:* Maximum Penetration: Dust reports 60-70% weekly active users across entire companies, demonstrating the potential reach of horizontal solutions rather than selling into a single team.* Emergent Use Cases: By allowing non-technical users to create agents, Dust enables use cases to emerge organically from actual business needs rather than prescribed solutions.* Infrastructure Value: The platform approach creates lasting value through maintained integrations and connections, similar to how Stripe's value lies in maintaining payment infrastructure. Rather than relying on third-party integration providers, Dust maintains its own connections to ensure proper handling of different data types and structures.The Vertical ChallengeHowever, this approach comes with trade-offs:* Harder Go-to-Market: As Stan talked about: "We spike at penetration... but it makes our go-to-market much harder. Vertical solutions have a go-to-market that is much easier because they're like, 'oh, I'm going to solve the lawyer stuff.'"* Complex Infrastructure: Building a horizontal platform requires maintaining numerous integrations and handling diverse data types appropriately – from structured Salesforce data to unstructured Notion pages. As you scale integrations, the cost of maintaining them also scales. * Product Surface Complexity: Creating an interface that's both powerful and accessible to non-technical users requires careful design decisions, down to avoiding technical terms like "system prompt" in favor of "instructions." The Future of AI PlatformsStan initially predicted we'd see the first billion-dollar single-person company in 2023 (a prediction later echoed by Sam Altman), but he's now more focused on a different milestone: billion-dollar companies with engineering teams of just 20 people, enabled by AI assistance.This vision aligns with Dust's horizontal platform approach – building the infrastructure that allows small teams to achieve outsized impact through AI augmentation. Rather than replacing entire job functions (the vertical approach), they're betting on augmenting existing workflows across organizations.Full YouTube EpisodeChapters* 00:00:00 Introductions* 00:04:33 Joining OpenAI from Paris* 00:09:54 Research evolution and compute allocation at OpenAI* 00:13:12 Working with Ilya Sutskever and OpenAI's vision* 00:15:51 Leaving OpenAI to start Dust* 00:18:15 Early focus on browser extension and WebGPT-like functionality* 00:20:20 Dust as the infrastructure for agents* 00:24:03 Challenges of building with early AI models* 00:28:17 LLMs and Workflow Automation* 00:35:28 Building dependency graphs of agents* 00:37:34 Simulating API endpoints* 00:40:41 State of AI models* 00:43:19 Running evals* 00:46:36 Challenges in building AI agents infra* 00:49:21 Buy vs. build decisions for infrastructure components* 00:51:02 Future of SaaS and AI's Impact on Software* 00:53:07 The single employee $1B company race* 00:56:32 Horizontal vs. vertical approaches to AI agentsTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.Swyx [00:00:11]: Hey, and today we're in a studio with Stanislas, welcome.Stan [00:00:14]: Thank you very much for having me.Swyx [00:00:16]: Visiting from Paris.Stan [00:00:17]: Paris.Swyx [00:00:18]: And you have had a very distinguished career. It's very hard to summarize, but you went to college in both Ecopolytechnique and Stanford, and then you worked in a number of places, Oracle, Totems, Stripe, and then OpenAI pre-ChatGPT. We'll talk, we'll spend a little bit of time about that. About two years ago, you left OpenAI to start Dust. I think you were one of the first OpenAI alum founders.Stan [00:00:40]: Yeah, I think it was about at the same time as the Adept guys, so that first wave.Swyx [00:00:46]: Yeah, and people really loved our David episode. We love a few sort of OpenAI stories, you know, for back in the day, like we're talking about pre-recording. Probably the statute of limitations on some of those stories has expired, so you can talk a little bit more freely without them coming after you. But maybe we'll just talk about, like, what was your journey into AI? You know, you were at Stripe for almost five years, there are a lot of Stripe alums going into OpenAI. I think the Stripe culture has come into OpenAI quite a bit.Stan [00:01:11]: Yeah, so I think the buses of Stripe people really started flowing in, I guess, after ChatGPT. But, yeah, my journey into AI is a... I mean, Greg Brockman. Yeah, yeah. From Greg, of course. And Daniela, actually, back in the days, Daniela Amodei.Swyx [00:01:27]: Yes, she was COO, I mean, she is COO, yeah. She had a pretty high job at OpenAI at the time, yeah, for sure.Stan [00:01:34]: My journey started as anybody else, you're fascinated with computer science and you want to make them think, it's awesome, but it doesn't work. I mean, it was a long time ago, it was like maybe 16, so it was 25 years ago. Then the first big exposure to AI would be at Stanford, and I'm going to, like, disclose a whole lamb, because at the time it was a class taught by Andrew Ng, and there was no deep learning. It was half features for vision and a star algorithm. So it was fun. But it was the early days of deep learning. At the time, I think a few years after, it was the first project at Google. But you know, that cat face or the human face trained from many images. I went to, hesitated doing a PhD, more in systems, eventually decided to go into getting a job. Went at Oracle, started a company, did a gazillion mistakes, got acquired by Stripe, worked with Greg Buckman there. And at the end of Stripe, I started interesting myself in AI again, felt like it was the time, you had the Atari games, you had the self-driving craziness at the time. And I started exploring projects, it felt like the Atari games were incredible, but there were still games. And I was looking into exploring projects that would have an impact on the world. And so I decided to explore three things, self-driving cars, cybersecurity and AI, and math and AI. It's like I sing it by a decreasing order of impact on the world, I guess.Swyx [00:03:01]: Discovering new math would be very foundational.Stan [00:03:03]: It is extremely foundational, but it's not as direct as driving people around.Swyx [00:03:07]: Sorry, you're doing this at Stripe, you're like thinking about your next move.Stan [00:03:09]: No, it was at Stripe, kind of a bit of time where I started exploring. I did a bunch of work with friends on trying to get RC cars to drive autonomously. Almost started a company in France or Europe about self-driving trucks. We decided to not go for it because it was probably very operational. And I think the idea of the company, of the team wasn't there. And also I realized that if I wake up a day and because of a bug I wrote, I killed a family, it would be a bad experience. And so I just decided like, no, that's just too crazy. And then I explored cybersecurity with a friend. We're trying to apply transformers to cut fuzzing. So cut fuzzing, you have kind of an algorithm that goes really fast and tries to mutate the inputs of a library to find bugs. And we tried to apply a transformer to that and do reinforcement learning with the signal of how much you propagate within the binary. Didn't work at all because the transformers are so slow compared to evolutionary algorithms that it kind of didn't work. Then I started interested in math and AI and started working on SAT solving with AI. And at the same time, OpenAI was kind of starting the reasoning team that were tackling that project as well. I was in touch with Greg and eventually got in touch with Ilya and finally found my way to OpenAI. I don't know how much you want to dig into that. The way to find your way to OpenAI when you're in Paris was kind of an interesting adventure as well.Swyx [00:04:33]: Please. And I want to note, this was a two-month journey. You did all this in two months.Stan [00:04:38]: The search.Swyx [00:04:40]: Your search for your next thing, because you left in July 2019 and then you joined OpenAI in September.Stan [00:04:45]: I'm going to be ashamed to say that.Swyx [00:04:47]: You were searching before. I was searching before.Stan [00:04:49]: I mean, it's normal. No, the truth is that I moved back to Paris through Stripe and I just felt the hardship of being remote from your team nine hours away. And so it kind of freed a bit of time for me to start the exploration before. Sorry, Patrick. Sorry, John.Swyx [00:05:05]: Hopefully they're listening. So you joined OpenAI from Paris and from like, obviously you had worked with Greg, but notStan [00:05:13]: anyone else. No. Yeah. So I had worked with Greg, but not Ilya, but I had started chatting with Ilya and Ilya was kind of excited because he knew that I was a good engineer through Greg, I presume, but I was not a trained researcher, didn't do a PhD, never did research. And I started chatting and he was excited all the way to the point where he was like, hey, come pass interviews, it's going to be fun. I think he didn't care where I was, he just wanted to try working together. So I go to SF, go through the interview process, get an offer. And so I get Bob McGrew on the phone for the first time, he's like, hey, Stan, it's awesome. You've got an offer. When are you coming to SF? I'm like, hey, it's awesome. I'm not coming to the SF. I'm based in Paris and we just moved. He was like, hey, it's awesome. Well, you don't have an offer anymore. Oh, my God. No, it wasn't as hard as that. But that's basically the idea. And it took me like maybe a couple more time to keep chatting and they eventually decided to try a contractor set up. And that's how I kind of started working at OpenAI, officially as a contractor, but in practice really felt like being an employee.Swyx [00:06:14]: What did you work on?Stan [00:06:15]: So it was solely focused on math and AI. And in particular in the application, so the study of the larger grid models, mathematical reasoning capabilities, and in particular in the context of formal mathematics. The motivation was simple, transformers are very creative, but yet they do mistakes. Formal math systems are of the ability to verify a proof and the tactics they can use to solve problems are very mechanical, so you miss the creativity. And so the idea was to try to explore both together. You would get the creativity of the LLMs and the kind of verification capabilities of the formal system. A formal system, just to give a little bit of context, is a system in which a proof is a program and the formal system is a type system, a type system that is so evolved that you can verify the program. If the type checks, it means that the program is correct.Swyx [00:07:06]: Is the verification much faster than actually executing the program?Stan [00:07:12]: Verification is instantaneous, basically. So the truth is that what you code in involves tactics that may involve computation to search for solutions. So it's not instantaneous. You do have to do the computation to expand the tactics into the actual proof. The verification of the proof at the very low level is instantaneous.Swyx [00:07:32]: How quickly do you run into like, you know, halting problem PNP type things, like impossibilities where you're just like that?Stan [00:07:39]: I mean, you don't run into it at the time. It was really trying to solve very easy problems. So I think the... Can you give an example of easy? Yeah, so that's the mass benchmark that everybody knows today. The Dan Hendricks one. The Dan Hendricks one, yeah. And I think it was the low end part of the mass benchmark at the time, because that mass benchmark includes AMC problems, AMC 8, AMC 10, 12. So these are the easy ones. Then AIME problems, somewhat harder, and some IMO problems, like Crazy Arm.Swyx [00:08:07]: For our listeners, we covered this in our Benchmarks 101 episode. AMC is literally the grade of like high school, grade 8, grade 10, grade 12. So you can solve this. Just briefly to mention this, because I don't think we'll touch on this again. There's a bit of work with like Lean, and then with, you know, more recently with DeepMind doing like scoring like silver on the IMO. Any commentary on like how math has evolved from your early work to today?Stan [00:08:34]: I mean, that result is mind blowing. I mean, from my perspective, spent three years on that. At the same time, Guillaume Lampe in Paris, we were both in Paris, actually. He was at FAIR, was working on some problems. We were pushing the boundaries, and the goal was the IMO. And we cracked a few problems here and there. But the idea of getting a medal at an IMO was like just remote. So this is an impressive result. And we can, I think the DeepMind team just did a good job of scaling. I think there's nothing too magical in their approach, even if it hasn't been published. There's a Dan Silver talk from seven days ago where it goes a little bit into more details. It feels like there's nothing magical there. It's really applying reinforcement learning and scaling up the amount of data that can generate through autoformalization. So we can dig into what autoformalization means if you want.Alessio [00:09:26]: Let's talk about the tail end, maybe, of the OpenAI. So you joined, and you're like, I'm going to work on math and do all of these things. I saw on one of your blog posts, you mentioned you fine-tuned over 10,000 models at OpenAI using 10 million A100 hours. How did the research evolve from the GPD 2, and then getting closer to DaVinci 003? And then you left just before ChatGPD was released, but tell people a bit more about the research path that took you there.Stan [00:09:54]: I can give you my perspective of it. I think at OpenAI, there's always been a large chunk of the compute that was reserved to train the GPTs, which makes sense. So it was pre-entropic splits. Most of the compute was going to a product called Nest, which was basically GPT-3. And then you had a bunch of, let's say, remote, not core research teams that were trying to explore maybe more specific problems or maybe the algorithm part of it. The interesting part, I don't know if it was where your question was going, is that in those labs, you're managing researchers. So by definition, you shouldn't be managing them. But in that space, there's a managing tool that is great, which is compute allocation. Basically by managing the compute allocation, you can message the team of where you think the priority should go. And so it was really a question of, you were free as a researcher to work on whatever you wanted. But if it was not aligned with OpenAI mission, and that's fair, you wouldn't get the compute allocation. As it happens, solving math was very much aligned with the direction of OpenAI. And so I was lucky to generally get the compute I needed to make good progress.Swyx [00:11:06]: What do you need to show as incremental results to get funded for further results?Stan [00:11:12]: It's an imperfect process because there's a bit of a... If you're working on math and AI, obviously there's kind of a prior that it's going to be aligned with the company. So it's much easier than to go into something much more risky, much riskier, I guess. You have to show incremental progress, I guess. It's like you ask for a certain amount of compute and you deliver a few weeks after and you demonstrate that you have a progress. Progress might be a positive result. Progress might be a strong negative result. And a strong negative result is actually often much harder to get or much more interesting than a positive result. And then it generally goes into, as any organization, you would have people finding your project or any other project cool and fancy. And so you would have that kind of phase of growing up compute allocation for it all the way to a point. And then maybe you reach an apex and then maybe you go back mostly to zero and restart the process because you're going in a different direction or something else. That's how I felt. Explore, exploit. Yeah, exactly. Exactly. Exactly. It's a reinforcement learning approach.Swyx [00:12:14]: Classic PhD student search process.Alessio [00:12:17]: And you were reporting to Ilya, like the results you were kind of bringing back to him or like what's the structure? It's almost like when you're doing such cutting edge research, you need to report to somebody who is actually really smart to understand that the direction is right.Stan [00:12:29]: So we had a reasoning team, which was working on reasoning, obviously, and so math in general. And that team had a manager, but Ilya was extremely involved in the team as an advisor, I guess. Since he brought me in OpenAI, I was lucky to mostly during the first years to have kind of a direct access to him. He would really coach me as a trainee researcher, I guess, with good engineering skills. And Ilya, I think at OpenAI, he was the one showing the North Star, right? He was his job and I think he really enjoyed it and he did it super well, was going through the teams and saying, this is where we should be going and trying to, you know, flock the different teams together towards an objective.Swyx [00:13:12]: I would say like the public perception of him is that he was the strongest believer in scaling. Oh, yeah. Obviously, he has always pursued the compression thesis. You have worked with him personally, what does the public not know about how he works?Stan [00:13:26]: I think he's really focused on building the vision and communicating the vision within the company, which was extremely useful. I was personally surprised that he spent so much time, you know, working on communicating that vision and getting the teams to work together versus...Swyx [00:13:40]: To be specific, vision is AGI? Oh, yeah.Stan [00:13:42]: Vision is like, yeah, it's the belief in compression and scanning computes. I remember when I started working on the Reasoning team, the excitement was really about scaling the compute around Reasoning and that was really the belief we wanted to ingrain in the team. And that's what has been useful to the team and with the DeepMind results shows that it was the right approach with the success of GPT-4 and stuff shows that it was the right approach.Swyx [00:14:06]: Was it according to the neural scaling laws, the Kaplan paper that was published?Stan [00:14:12]: I think it was before that, because those ones came with GPT-3, basically at the time of GPT-3 being released or being ready internally. But before that, there really was a strong belief in scale. I think it was just the belief that the transformer was a generic enough architecture that you could learn anything. And that was just a question of scaling.Alessio [00:14:33]: Any other fun stories you want to tell? Sam Altman, Greg, you know, anything.Stan [00:14:37]: Weirdly, I didn't work that much with Greg when I was at OpenAI. He had always been mostly focused on training the GPTs and rightfully so. One thing about Sam Altman, he really impressed me because when I joined, he had joined not that long ago and it felt like he was kind of a very high level CEO. And I was mind blown by how deep he was able to go into the subjects within a year or something, all the way to a situation where when I was having lunch by year two, I was at OpenAI with him. He would just quite know deeply what I was doing. With no ML background. Yeah, with no ML background, but I didn't have any either, so I guess that explains why. But I think it's a question about, you don't necessarily need to understand the very technicalities of how things are done, but you need to understand what's the goal and what's being done and what are the recent results and all of that in you. And we could have kind of a very productive discussion. And that really impressed me, given the size at the time of OpenAI, which was not negligible.Swyx [00:15:44]: Yeah. I mean, you've been a, you were a founder before, you're a founder now, and you've seen Sam as a founder. How has he affected you as a founder?Stan [00:15:51]: I think having that capability of changing the scale of your attention in the company, because most of the time you operate at a very high level, but being able to go deep down and being in the known of what's happening on the ground is something that I feel is really enlightening. That's not a place in which I ever was as a founder, because first company, we went all the way to 10 people. Current company, there's 25 of us. So the high level, the sky and the ground are pretty much at the same place. No, you're being too humble.Swyx [00:16:21]: I mean, Stripe was also like a huge rocket ship.Stan [00:16:23]: Stripe, I was a founder. So I was, like at OpenAI, I was really happy being on the ground, pushing the machine, making it work. Yeah.Swyx [00:16:31]: Last OpenAI question. The Anthropic split you mentioned, you were around for that. Very dramatic. David also left around that time, you left. This year, we've also had a similar management shakeup, let's just call it. Can you compare what it was like going through that split during that time? And then like, does that have any similarities now? Like, are we going to see a new Anthropic emerge from these folks that just left?Stan [00:16:54]: That I really, really don't know. At the time, the split was pretty surprising because they had been trying GPT-3, it was a success. And to be completely transparent, I wasn't in the weeds of the splits. What I understood of it is that there was a disagreement of the commercialization of that technology. I think the focal point of that disagreement was the fact that we started working on the API and wanted to make those models available through an API. Is that really the core disagreement? I don't know.Swyx [00:17:25]: Was it safety?Stan [00:17:26]: Was it commercialization?Swyx [00:17:27]: Or did they just want to start a company?Stan [00:17:28]: Exactly. Exactly. That I don't know. But I think what I was surprised of is how quickly OpenAI recovered at the time. And I think it's just because we were mostly a research org and the mission was so clear that some divergence in some teams, some people leave, the mission is still there. We have the compute. We have a site. So it just keeps going.Swyx [00:17:50]: Very deep bench. Like just a lot of talent. Yeah.Alessio [00:17:53]: So that was the OpenAI part of the history. Exactly. So then you leave OpenAI in September 2022. And I would say in Silicon Valley, the two hottest companies at the time were you and Lanktrain. What was that start like and why did you decide to start with a more developer focused kind of like an AI engineer tool rather than going back into some more research and something else?Stan [00:18:15]: Yeah. First, I'm not a trained researcher. So going through OpenAI was really kind of the PhD I always wanted to do. But research is hard. You're digging into a field all day long for weeks and weeks and weeks, and you find something, you get super excited for 12 seconds. And at the 13 seconds, you're like, oh, yeah, that was obvious. And you go back to digging. I'm not a trained, like formally trained researcher, and it wasn't kind of a necessarily an ambition of me of creating, of having a research career. And I felt the hardness of it. I enjoyed a lot of like that a ton. But at the time, I decided that I wanted to go back to something more productive. And the other fun motivation was like, I mean, if we believe in AGI and if we believe the timelines might not be too long, it's actually the last train leaving the station to start a company. After that, it's going to be computers all the way down. And so that was kind of the true motivation for like trying to go there. So that's kind of the core motivation at the beginning of personally. And the motivation for starting a company was pretty simple. I had seen GPT-4 internally at the time, it was September 2022. So it was pre-GPT, but GPT-4 was ready since, I mean, I'd been ready for a few months internally. I was like, okay, that's obvious, the capabilities are there to create an insane amount of value to the world. And yet the deployment is not there yet. The revenue of OpenAI at the time were ridiculously small compared to what it is today. So the thesis was, there's probably a lot to be done at the product level to unlock the usage.Alessio [00:19:49]: Yeah. Let's talk a bit more about the form factor, maybe. I think one of the first successes you had was kind of like the WebGPT-like thing, like using the models to traverse the web and like summarize things. And the browser was really the interface. Why did you start with the browser? Like what was it important? And then you built XP1, which was kind of like the browser extension.Stan [00:20:09]: So the starting point at the time was, if you wanted to talk about LLMs, it was still a rather small community, a community of mostly researchers and to some extent, very early adopters, very early engineers. It was almost inconceivable to just build a product and go sell it to the enterprise, though at the time there was a few companies doing that. The one on marketing, I don't remember its name, Jasper. But so the natural first intention, the first, first, first intention was to go to the developers and try to create tooling for them to create product on top of those models. And so that's what Dust was originally. It was quite different than Lanchain, and Lanchain just beat the s**t out of us, which is great. It's a choice.Swyx [00:20:53]: You were cloud, in closed source. They were open source.Stan [00:20:56]: Yeah. So technically we were open source and we still are open source, but I think that doesn't really matter. I had the strong belief from my research time that you cannot create an LLM-based workflow on just one example. Basically, if you just have one example, you overfit. So as you develop your interaction, your orchestration around the LLM, you need a dozen examples. Obviously, if you're running a dozen examples on a multi-step workflow, you start paralyzing stuff. And if you do that in the console, you just have like a messy stream of tokens going out and it's very hard to observe what's going there. And so the idea was to go with an UI so that you could kind of introspect easily the output of each interaction with the model and dig into there through an UI, which is-Swyx [00:21:42]: Was that open source? I actually didn't come across it.Stan [00:21:44]: Oh yeah, it wasn't. I mean, Dust is entirely open source even today. We're not going for an open source-Swyx [00:21:48]: If it matters, I didn't know that.Stan [00:21:49]: No, no, no, no, no. The reason why is because we're not open source because we're not doing an open source strategy. It's not an open source go-to-market at all. We're open source because we can and it's fun.Swyx [00:21:59]: Open source is marketing. You have all the downsides of open source, which is like people can clone you.Stan [00:22:03]: But I think that downside is a big fallacy. Okay. Yes, anybody can clone Dust today, but the value of Dust is not the current state. The value of Dust is the number of eyeballs and hands of developers that are creating to it in the future. And so yes, anybody can clone it today, but that wouldn't change anything. There is some value in being open source. In a discussion with the security team, you can be extremely transparent and just show the code. When you have discussion with users and there's a bug or a feature missing, you can just point to the issue, show the pull request, show the, show the, exactly, oh, PR welcome. That doesn't happen that much, but you can show the progress if the person that you're chatting with is a little bit technical, they really enjoy seeing the pull request advancing and seeing all the way to deploy. And then the downsides are mostly around security. You never want to do security by obfuscation. But the truth is that your vector of attack is facilitated by you being open source. But at the same time, it's a good thing because if you're doing anything like a bug bountying or stuff like that, you just give much more tools to the bug bountiers so that their output is much better. So there's many, many, many trade-offs. I don't believe in the value of the code base per se. I think it's really the people that are on the code base that have the value and go to market and the product and all of those things that are around the code base. Obviously, that's not true for every code base. If you're working on a very secret kernel to accelerate the inference of LLMs, I would buy that you don't want to be open source. But for product stuff, I really think there's very little risk. Yeah.Alessio [00:23:39]: I signed up for XP1, I was looking, January 2023. I think at the time you were on DaVinci 003. Given that you had seen GPD 4, how did you feel having to push a product out that was using this model that was so inferior? And you're like, please, just use it today. I promise it's going to get better. Just overall, as a founder, how do you build something that maybe doesn't quite work with the model today, but you're just expecting the new model to be better?Stan [00:24:03]: Yeah, so actually, XP1 was even on a smaller one that was the post-GDPT release, small version, so it was... Ada, Babbage... No, no, no, not that far away. But it was the small version of GDPT, basically. I don't remember its name. Yes, you have a frustration there. But at the same time, I think XP1 was designed, was an experiment, but was designed as a way to be useful at the current capability of the model. If you just want to extract data from a LinkedIn page, that model was just fine. If you want to summarize an article on a newspaper, that model was just fine. And so it was really a question of trying to find a product that works with the current capability, knowing that you will always have tailwinds as models get better and faster and cheaper. So that was kind of a... There's a bit of a frustration because you know what's out there and you know that you don't have access to it yet. It's also interesting to try to find a product that works with the current capability.Alessio [00:24:55]: And we highlighted XP1 in our anatomy of autonomy post in April of last year, which was, you know, where are all the agents, right? So now we spent 30 minutes getting to what you're building now. So you basically had a developer framework, then you had a browser extension, then you had all these things, and then you kind of got to where Dust is today. So maybe just give people an overview of what Dust is today and the courtesies behind it. Yeah, of course.Stan [00:25:20]: So Dust, we really want to build the infrastructure so that companies can deploy agents within their teams. We are horizontal by nature because we strongly believe in the emergence of use cases from the people having access to creating an agent that don't need to be developers. They have to be thinkers. They have to be curious. But anybody can create an agent that will solve an operational thing that they're doing in their day-to-day job. And to make those agents useful, there's two focus, which is interesting. The first one is an infrastructure focus. You have to build the pipes so that the agent has access to the data. You have to build the pipes such that the agents can take action, can access the web, et cetera. So that's really an infrastructure play. Maintaining connections to Notion, Slack, GitHub, all of them is a lot of work. It is boring work, boring infrastructure work, but that's something that we know is extremely valuable in the same way that Stripe is extremely valuable because it maintains the pipes. And we have that dual focus because we're also building the product for people to use it. And there it's fascinating because everything started from the conversational interface, obviously, which is a great starting point. But we're only scratching the surface, right? I think we are at the pong level of LLM productization. And we haven't invented the C3. We haven't invented Counter-Strike. We haven't invented Cyberpunk 2077. So this is really our mission is to really create the product that lets people equip themselves to just get away all the work that can be automated or assisted by LLMs.Alessio [00:26:57]: And can you just comment on different takes that people had? So maybe the most open is like auto-GPT. It's just kind of like just trying to do anything. It's like it's all magic. There's no way for you to do anything. Then you had the ADAPT, you know, we had David on the podcast. They're very like super hands-on with each individual customer to build super tailored. How do you decide where to draw the line between this is magic? This is exposed to you, especially in a market where most people don't know how to build with AI at all. So if you expect them to do the thing, they're probably not going to do it. Yeah, exactly.Stan [00:27:29]: So the auto-GPT approach obviously is extremely exciting, but we know that the agentic capability of models are not quite there yet. It just gets lost. So we're starting, we're starting where it works. Same with the XP one. And where it works is pretty simple. It's like simple workflows that involve a couple tools where you don't even need to have the model decide which tools it's used in the sense of you just want people to put it in the instructions. It's like take that page, do that search, pick up that document, do the work that I want in the format I want, and give me the results. There's no smartness there, right? In terms of orchestrating the tools, it's mostly using English for people to program a workflow where you don't have the constraint of having compatible API between the two.Swyx [00:28:17]: That kind of personal automation, would you say it's kind of like an LLM Zapier type ofStan [00:28:22]: thing?Swyx [00:28:22]: Like if this, then that, and then, you know, do this, then this. You're programming with English?Stan [00:28:28]: So you're programming with English. So you're just saying, oh, do this and then that. You can even create some form of APIs. You say, when I give you the command X, do this. When I give you the command Y, do this. And you describe the workflow. But you don't have to create boxes and create the workflow explicitly. It just needs to describe what are the tasks supposed to be and make the tool available to the agent. The tool can be a semantic search. The tool can be querying into a structured database. The tool can be searching on the web. And obviously, the interesting tools that we're only starting to scratch are actually creating external actions like reimbursing something on Stripe, sending an email, clicking on a button in the admin or something like that.Swyx [00:29:11]: Do you maintain all these integrations?Stan [00:29:13]: Today, we maintain most of the integrations. We do always have an escape hatch for people to kind of custom integrate. But the reality is that the reality of the market today is that people just want it to work, right? And so it's mostly us maintaining the integration. As an example, a very good source of information that is tricky to productize is Salesforce. Because Salesforce is basically a database and a UI. And they do the f**k they want with it. And so every company has different models and stuff like that. So right now, we don't support it natively. And the type of support or real native support will be slightly more complex than just osing into it, like is the case with Slack as an example. Because it's probably going to be, oh, you want to connect your Salesforce to us? Give us the SQL. That's the Salesforce QL language. Give us the queries you want us to run on it and inject in the context of dust. So that's interesting how not only integrations are cool, and some of them require a bit of work on the user. And for some of them that are really valuable to our users, but we don't support yet, they can just build them internally and push the data to us.Swyx [00:30:18]: I think I understand the Salesforce thing. But let me just clarify, are you using browser automation because there's no API for something?Stan [00:30:24]: No, no, no, no. In that case, so we do have browser automation for all the use cases and apply the public web. But for most of the integration with the internal system of the company, it really runs through API.Swyx [00:30:35]: Haven't you felt the pull to RPA, browser automation, that kind of stuff?Stan [00:30:39]: I mean, what I've been saying for a long time, maybe I'm wrong, is that if the future is that you're going to stand in front of a computer and looking at an agent clicking on stuff, then I'll hit my computer. And my computer is a big Lenovo. It's black. Doesn't sound good at all compared to a Mac. And if the APIs are there, we should use them. There is going to be a long tail of stuff that don't have APIs, but as the world is moving forward, that's disappearing. So the core API value in the past has really been, oh, this old 90s product doesn't have an API. So I need to use the UI to automate. I think for most of the ICP companies, the companies that ICP for us, the scale ups that are between 500 and 5,000 people, tech companies, most of the SaaS they use have APIs. Now there's an interesting question for the open web, because there are stuff that you want to do that involve websites that don't necessarily have APIs. And the current state of web integration from, which is us and OpenAI and Anthropic, I don't even know if they have web navigation, but I don't think so. The current state of affair is really, really broken because you have what? You have basically search and headless browsing. But headless browsing, I think everybody's doing basically body.innertext and fill that into the model, right?Swyx [00:31:56]: MARK MIRCHANDANI There's parsers into Markdown and stuff.Stan [00:31:58]: FRANCESC CAMPOY I'm super excited by the companies that are exploring the capability of rendering a web page into a way that is compatible for a model, being able to maintain the selector. So that's basically the place where to click in the page through that process, expose the actions to the model, have the model select an action in a way that is compatible with model, which is not a big page of a full DOM that is very noisy, and then being able to decompress that back to the original page and take the action. And that's something that is really exciting and that will kind of change the level of things that agents can do on the web. That I feel exciting, but I also feel that the bulk of the useful stuff that you can do within the company can be done through API. The data can be retrieved by API. The actions can be taken through API.Swyx [00:32:44]: For listeners, I'll note that you're basically completely disagreeing with David Wan. FRANCESC CAMPOY Exactly, exactly. I've seen it since it's summer. ADEPT is where it is, and Dust is where it is. So Dust is still standing.Alessio [00:32:55]: Can we just quickly comment on function calling? You mentioned you don't need the models to be that smart to actually pick the tools. Have you seen the models not be good enough? Or is it just like, you just don't want to put the complexity in there? Like, is there any room for improvement left in function calling? Or do you feel you usually consistently get always the right response, the right parametersStan [00:33:13]: and all of that?Alessio [00:33:13]: FRANCESC CAMPOY So that's a tricky product question.Stan [00:33:15]: Because if the instructions are good and precise, then you don't have any issue, because it's scripted for you. And the model will just look at the scripts and just follow and say, oh, he's probably talking about that action, and I'm going to use it. And the parameters are kind of abused from the state of the conversation. I'll just go with it. If you provide a very high level, kind of an auto-GPT-esque level in the instructions and provide 16 different tools to your model, yes, we're seeing the models in that state making mistakes. And there is obviously some progress can be made on the capabilities. But the interesting part is that there is already so much work that can assist, augment, accelerate by just going with pretty simply scripted for actions agents. What I'm excited about by pushing our users to create rather simple agents is that once you have those working really well, you can create meta agents that use the agents as actions. And all of a sudden, you can kind of have a hierarchy of responsibility that will probably get you almost to the point of the auto-GPT value. It requires the construction of intermediary artifacts, but you're probably going to be able to achieve something great. I'll give you some example. We have our incidents are shared in Slack in a specific channel, or shipped are shared in Slack. We have a weekly meeting where we have a table about incidents and shipped stuff. We're not writing that weekly meeting table anymore. We have an assistant that just go find the right data on Slack and create the table for us. And that assistant works perfectly. It's trivially simple, right? Take one week of data from that channel and just create the table. And then we have in that weekly meeting, obviously some graphs and reporting about our financials and our progress and our ARR. And we've created assistants to generate those graphs directly. And those assistants works great. By creating those assistants that cover those small parts of that weekly meeting, slowly we're getting to in a world where we'll have a weekly meeting assistance. We'll just call it. You don't need to prompt it. You don't need to say anything. It's going to run those different assistants and get that notion page just ready. And by doing that, if you get there, and that's an objective for us to us using Dust, get there, you're saving an hour of company time every time you run it. Yeah.Alessio [00:35:28]: That's my pet topic of NPM for agents. How do you build dependency graphs of agents? And how do you share them? Because why do I have to rebuild some of the smaller levels of what you built already?Swyx [00:35:40]: I have a quick follow-up question on agents managing other agents. It's a topic of a lot of research, both from Microsoft and even in startups. What you've discovered best practice for, let's say like a manager agent controlling a bunch of small agents. It's two-way communication. I don't know if there should be a protocol format.Stan [00:35:59]: To be completely honest, the state we are at right now is creating the simple agents. So we haven't even explored yet the meta agents. We know it's there. We know it's going to be valuable. We know it's going to be awesome. But we're starting there because it's the simplest place to start. And it's also what the market understands. If you go to a company, random SaaS B2B company, not necessarily specialized in AI, and you take an operational team and you tell them, build some tooling for yourself, they'll understand the small agents. If you tell them, build AutoGP, they'll be like, Auto what?Swyx [00:36:31]: And I noticed that in your language, you're very much focused on non-technical users. You don't really mention API here. You mention instruction instead of system prompt, right? That's very conscious.Stan [00:36:41]: Yeah, it's very conscious. It's a mark of our designer, Ed, who kind of pushed us to create a friendly product. I was knee-deep into AI when I started, obviously. And my co-founder, Gabriel, was a Stripe as well. We started a company together that got acquired by Stripe 15 years ago. It was at Alain, a healthcare company in Paris. After that, it was a little bit less so knee-deep in AI, but really focused on product. And I didn't realize how important it is to make that technology not scary to end users. It didn't feel scary to me, but it was really seen by Ed, our designer, that it was feeling scary to the users. And so we were very proactive and very deliberate about creating a brand that feels not too scary and creating a wording and a language, as you say, that really tried to communicate the fact that it's going to be fine. It's going to be easy. You're going to make it.Alessio [00:37:34]: And another big point that David had about ADAPT is we need to build an environment for the agents to act. And then if you have the environment, you can simulate what they do. How's that different when you're interacting with APIs and you're kind of touching systems that you cannot really simulate? If you call it the Salesforce API, you're just calling it.Stan [00:37:52]: So I think that goes back to the DNA of the companies that are very different. ADAPT, I think, was a product company with a very strong research DNA, and they were still doing research. One of their goals was building a model. And that's why they raised a large amount of money, et cetera. We are 100% deliberately a product company. We don't do research. We don't train models. We don't even run GPUs. We're using the models that exist, and we try to push the product boundary as far as possible with the existing models. So that creates an issue. Indeed, so to answer your question, when you're interacting in the real world, well, you cannot simulate, so you cannot improve the models. Even improving your instructions is complicated for a builder. The hope is that you can use models to evaluate the conversations so that you can get at least feedback and you could get contradictive information about the performance of the assistance. But if you take actual trace of interaction of humans with those agents, it is even for us humans extremely hard to decide whether it was a productive interaction or a really bad interaction. You don't know why the person left. You don't know if they left happy or not. So being extremely, extremely, extremely pragmatic here, it becomes a product issue. We have to build a product that identifies the end users to provide feedback so that as a first step, the person that is building the agent can iterate on it. As a second step, maybe later when we start training model and post-training, et cetera, we can optimize around that for each of those companies. Yeah.Alessio [00:39:17]: Do you see in the future products offering kind of like a simulation environment, the same way all SaaS now kind of offers APIs to build programmatically? Like in cybersecurity, there are a lot of companies working on building simulative environments so that then you can use agents like Red Team, but I haven't really seen that.Stan [00:39:34]: Yeah, no, me neither. That's a super interesting question. I think it's really going to depend on how much, because you need to simulate to generate data, you need to train data to train models. And the question at the end is, are we going to be training models or are we just going to be using frontier models as they are? On that question, I don't have a strong opinion. It might be the case that we'll be training models because in all of those AI first products, the model is so close to the product surface that as you get big and you want to really own your product, you're going to have to own the model as well. Owning the model doesn't mean doing the pre-training, that would be crazy. But at least having an internal post-training realignment loop, it makes a lot of sense. And so if we see many companies going towards that all the time, then there might be incentives for the SaaS's of the world to provide assistance in getting there. But at the same time, there's a tension because those SaaS, they don't want to be interacted by agents, they want the human to click on the button. Yeah, they got to sell seats. Exactly.Swyx [00:40:41]: Just a quick question on models. I'm sure you've used many, probably not just OpenAI. Would you characterize some models as better than others? Do you use any open source models? What have been the trends in models over the last two years?Stan [00:40:53]: We've seen over the past two years kind of a bit of a race in between models. And at times, it's the OpenAI model that is the best. At times, it's the Anthropic models that is the best. Our take on that is that we are agnostic and we let our users pick their model. Oh, they choose? Yeah, so when you create an assistant or an agent, you can just say, oh, I'm going to run it on GP4, GP4 Turbo, or...Swyx [00:41:16]: Don't you think for the non-technical user, that is actually an abstraction that you should take away from them?Stan [00:41:20]: We have a sane default. So we move the default to the latest model that is cool. And we have a sane default, and it's actually not very visible. In our flow to create an agent, you would have to go in advance and go pick your model. So this is something that the technical person will care about. But that's something that obviously is a bit too complicated for the...Swyx [00:41:40]: And do you care most about function calling or instruction following or something else?Stan [00:41:44]: I think we care most for function calling because you want to... There's nothing worse than a function call, including incorrect parameters or being a bit off because it just drives the whole interaction off.Swyx [00:41:56]: Yeah, so got the Berkeley function calling.Stan [00:42:00]: These days, it's funny how the comparison between GP4O and GP4 Turbo is still up in the air on function calling. I personally don't have proof, but I know many people, and I'm probably part of them, to think that GP4 Turbo is still better than GP4O on function calling. Wow. We'll see what comes out of the O1 class if it ever gets function calling. And Cloud 3.5 Summit is great as well. They kind of innovated in an interesting way, which was never quite publicized. But it's that they have that kind of chain of thought step whenever you use a Cloud model or Summit model with function calling. That chain of thought step doesn't exist when you just interact with it just for answering questions. But when you use function calling, you get that step, and it really helps getting better function calling.Swyx [00:42:43]: Yeah, we actually just recorded a podcast with the Berkeley team that runs that leaderboard this week. So they just released V3.Stan [00:42:49]: Yeah.Swyx [00:42:49]: It was V1 like two months ago, and then they V2, V3. Turbo is on top.Stan [00:42:53]: Turbo is on top. Turbo is over 4.0.Swyx [00:42:54]: And then the third place is XLAM from Salesforce, which is a large action model they've been trying to popularize.Stan [00:43:01]: Yep.Swyx [00:43:01]: O1 Mini is actually on here, I think. O1 Mini is number 11.Stan [00:43:05]: But arguably, O1 Mini has been in a line for that. Yeah.Alessio [00:43:09]: Do you use leaderboards? Do you have your own evals? I mean, this is kind of intuitive, right? Like using the older model is better. I think most people just upgrade. Yeah. What's the eval process like?Stan [00:43:19]: It's funny because I've been doing research for three years, and we have bigger stuff to cook. When you're deploying in a company, one thing where we really spike is that when we manage to activate the company, we have a crazy penetration. The highest penetration we have is 88% daily active users within the entire employee of the company. The kind of average penetration and activation we have in our current enterprise customers is something like more like 60% to 70% weekly active. So we basically have the entire company interacting with us. And when you're there, there is so many stuff that matters most than getting evals, getting the best model. Because there is so many places where you can create products or do stuff that will give you the 80% with the work you do. Whereas deciding if it's GPT-4 or GPT-4 Turbo or et cetera, you know, it'll just give you the 5% improvement. But the reality is that you want to focus on the places where you can really change the direction or change the interaction more drastically. But that's something that we'll have to do eventually because we still want to be serious people.Swyx [00:44:24]: It's funny because in some ways, the model labs are competing for you, right? You don't have to do any effort. You just switch model and then it'll grow. What are you really limited by? Is it additional sources?Stan [00:44:36]: It's not models, right?Swyx [00:44:37]: You're not really limited by quality of model.Stan [00:44:40]: Right now, we are limited by the infrastructure part, which is the ability to connect easily for users to all the data they need to do the job they want to do.Swyx [00:44:51]: Because you maintain all your own stuff.Stan [00:44:53]: You know, there are companies out thereSwyx [00:44:54]: that are starting to provide integrations as a service, right? I used to work in an integrations company. Yeah, I know.Stan [00:44:59]: It's just that there is some intricacies about how you chunk stuff and how you process information from one platform to the other. If you look at the end of the spectrum, you could think of, you could say, oh, I'm going to support AirByte and AirByte has- I used to work at AirByte.Swyx [00:45:12]: Oh, really?Stan [00:45:13]: That makes sense.Swyx [00:45:14]: They're the French founders as well.Stan [00:45:15]: I know Jean very well. I'm seeing him today. And the reality is that if you look at Notion, AirByte does the job of taking Notion and putting it in a structured way. But that's the way it is not really usable to actually make it available to models in a useful way. Because you get all the blocks, details, et cetera, which is useful for many use cases.Swyx [00:45:35]: It's also for data scientists and not for AI.Stan [00:45:38]: The reality of Notion is that sometimes you have a- so when you have a page, there's a lot of structure in it and you want to capture the structure and chunk the information in a way that respects that structure. In Notion, you have databases. Sometimes those databases are real tabular data. Sometimes those databases are full of text. You want to get the distinction and understand that this database should be considered like text information, whereas this other one is actually quantitative information. And to really get a very high quality interaction with that piece of information, I haven't found a solution that will work without us owning the connection end-to-end.Swyx [00:46:15]: That's why I don't invest in, there's Composio, there's All Hands from Graham Newbig. There's all these other companies that are like, we will do the integrations for you. You just, we have the open source community. We'll do off the shelf. But then you are so specific in your needs that you want to own it.Swyx [00:46:28]: Yeah, exactly.Stan [00:46:29]: You can talk to Michel about that.Swyx [00:46:30]: You know, he wants to put the AI in there, but you know. Yeah, I will. I will.Stan [00:46:35]: Cool. What are we missing?Alessio [00:46:36]: You know, what are like the things that are like sneakily hard that you're tackling that maybe people don't even realize they're like really hard?Stan [00:46:43]: The real parts as we kind of touch base throughout the conversation is really building the infra that works for those agents because it's a tenuous walk. It's an evergreen piece of work because you always have an extra integration that will be useful to a non-negligible set of your users. I'm super excited about is that there's so many interactions that shouldn't be conversational interactions and that could be very useful. Basically, know that we have the firehose of information of those companies and there's not going to be that many companies that capture the firehose of information. When you have the firehose of information, you can do a ton of stuff with models that are just not accelerating people, but giving them superhuman capability, even with the current model capability because you can just sift through much more information. An example is documentation repair. If I have the firehose of Slack messages and new Notion pages, if somebody says, I own that page, I want to be updated when there is a piece of information that should update that page, this is not possible. You get an email saying, oh, look at that Slack message. It says the opposite of what you have in that paragraph. Maybe you want to update or just ping that person. I think there is a lot to be explored on the product layer in terms of what it means to interact productively with those models. And that's a problem that's extremely hard and extremely exciting.Swyx [00:48:00]: One thing you keep mentioning about infra work, obviously, Dust is building that infra and serving that in a very consumer-friendly way. You always talk about infra being additional sources, additional connectors. That is very important. But I'm also interested in the vertical infra. There is an orchestrator underlying all these things where you're doing asynchronous work. For example, the simplest one is a cron job. You just schedule things. But also, for if this and that, you have to wait for something to be executed and proceed to the next task. I used to work on an orchestrator as well, Temporal.Stan [00:48:31]: We used Temporal. Oh, you used Temporal? Yeah. Oh, how was the experience?Swyx [00:48:34]: I need the NPS.Stan [00:48:36]: We're doing a self-discovery call now.Swyx [00:48:39]: But you can also complain to me because I don't work there anymore.Stan [00:48:42]: No, we love Temporal. There's some edges that are a bit rough, surprisingly rough. And you would say, why is it so complicated?Swyx [00:48:49]: It's always versioning.Stan [00:48:50]: Yeah, stuff like that. But we really love it. And we use it for exactly what you said, like managing the entire set of stuff that needs to happen so that in semi-real time, we get all the updates from Slack or Notion or GitHub into the system. And whenever we see that piece of information goes through, maybe trigger workflows to run agents because they need to provide alerts to users and stuff like that. And Temporal is great. Love it.Swyx [00:49:17]: You haven't evaluated others. You don't want to build your own. You're happy with...Stan [00:49:21]: Oh, no, we're not in the business of replacing Temporal. And Temporal is so... I mean, it is or any other competitive product. They're very general. If it's there, there's an interesting theory about buy versus build. I think in that case, when you're a high-growth company, your buy-build trade-off is very much on the side of buy. Because if you have the capability, you're just going to be saving time, you can focus on your core competency, etc. And it's funny because we're seeing, we're starting to see the post-high-growth company, post-SKF company, going back on that trade-off, interestingly. So that's the cloud news about removing Zendesk and Salesforce. Do you believe that, by the way?Alessio [00:49:56]: Yeah, I did a podcast with them.Stan [00:49:58]: Oh, yeah?Alessio [00:49:58]: It's true.Swyx [00:49:59]: No, no, I know.Stan [00:50:00]: Of course they say it's true,Swyx [00:50:00]: but also how well is it going to go?Stan [00:50:02]: So I'm not talking about deflecting the customer traffic. I'm talking about building AI on top of Salesforce and Zendesk, basically, if I understand correctly. And all of a sudden, your product surface becomes much smaller because you're interacting with an AI system that will take some actions. And so all of a sudden, you don't need the product layer anymore. And you realize that, oh, those things are just databases that I pay a hundred times the price, right? Because you're a post-SKF company and you have tech capabilities, you are incentivized to reduce your costs and you have the capability to do so. And then it makes sense to just scratch the SaaS away. So it's interesting that we might see kind of a bad time for SaaS in post-hyper-growth tech companies. So it's still a big market, but it's not that big because if you're not a tech company, you don't have the capabilities to reduce that cost. If you're a high-growth company, always going to be buying because you go faster with that. But that's an interesting new space, new category of companies that might remove some SaaS. Yeah, Alessio's firmSwyx [00:51:02]: has an interesting thesis on the future of SaaS in AI.Alessio [00:51:05]: Service as a software, we call it. It's basically like, well, the most extreme is like, why is there any software at all? You know, ideally, it's all a labor interface where you're asking somebody to do something for you, whether that's a person, an AI agent or whatnot.Stan [00:51:17]: Yeah, yeah, that's interesting. I have to ask.Swyx [00:51:19]: Are you paying for Temporal Cloud or are you self-hosting?Stan [00:51:22]: Oh, no, no, we're paying, we're paying. Oh, okay, interesting.Swyx [00:51:24]: We're paying way too much.Stan [00:51:26]: It's crazy expensive, but it makes us-Swyx [00:51:28]: That's why as a shareholder, I like to hear that. It makes us go faster,Stan [00:51:31]: so we're happy to pay.Swyx [00:51:33]: Other things in the infrastack, I just want a list for other founders to think about. Ops, API gateway, evals, you know, anything interesting there that you build or buy?Stan [00:51:41]: I mean, there's always an interesting question. We've been building a lot around the interface between models and because Dust, the original version, was an orchestration platform and we basically provide a unified interface to every model providers.Swyx [00:51:56]: That's what I call gateway.Stan [00:51:57]: That we add because Dust was that and so we continued building upon and we own it. But that's an interesting question was in you, you want to build that or buy it?Swyx [00:52:06]: Yeah, I always say light LLM is the current open source consensus.Stan [00:52:09]: Exactly, yeah. There's an interesting question there.Swyx [00:52:12]: Ops, Datadog, just tracking.Stan [00:52:14]: Oh yeah, so Datadog is an obvious... What are the mistakes that I regret? I started as pure JavaScript, not TypeScript, and I think you want to, if you're wondering, oh, I want to go fast, I'll do a little bit of JavaScript. No, don't, just start with TypeScript. I see, okay.Swyx [00:52:30]: So interesting, you are a research engineer that came out of OpenAI that bet on TypeScript.Stan [00:52:36]: Well, the reality is that if you're building a product, you're going to be doing a lot of JavaScript, right? And Next, we're using Next as an example. It's

Meet Amy, Nelvin, Erin + Brooke. Are you looking for insights on future-proofing your organization against the ever-evolving landscape of challenges and opportunities in the sector? They've got you covered in this Responsive Nonprofit Summit Replay. From embracing failure to building resilient teams and fostering innovation, discover actionable approaches to ensure your nonprofit remains agile, sustainable, and impactful in the face of uncertainty.

Gary Kusin is a mentor, investor, entrepreneur, and business advisor. He today advises an array of public and private companies, large and small, on strategy, management, and growth issues. In addition, Gary continues his full mentoring schedule and has mentored well over 500 individuals during his career. Mr. Kusin co-founded two companies, Babbage's, operating as GameStop (NYSE: GME), and Laura Mercier Cosmetics, which are well-known global brands today. Gary spent 13 years as a senior advisor to the global private equity firm TPG, including a large amount of his time mentoring CEOs of TPG portfolio companies. He served from 2001-2006 as president and chief executive officer of Kinko's, today operating as FedEx Office. Mr. Kusin was responsible for the turnaround, strategic growth, and transformation of Kinko's and oversaw the ultimate sale to FedEx, directly reporting to Fred Smith, founder of FedEx, for the 2 years required to integrate Kinko's into FedEx and be renamed FedEx Office. An Inc. magazine “Entrepreneur of Year” award winner, he has served many public and private firms in America and abroad, including Electronic Arts, Petco, Sabre, and Myer Department Stores in Australia.Mr. Kusin has been very involved in Dallas community activities throughout his career. A representative sample of organizations and positions include the St. Mark's School of Texas Board of Trustees, the Dallas Young Presidents' Organization (YPO) chairman, the Dallas Citizens Council Board of Directors, and the Southwestern Medical School Foundation.​A member of the University of Texas McCombs School of Business Hall of Fame, Mr. Kusin earned a BA from the University of Texas at Austin and an MBA from the Harvard Business School. A native of Texarkana, Texas, Gary lives in Dallas with his wife Karleen. Their four children, spouses, and 11 grandchildren live from coast to coast with most pursuing their own entrepreneurial journeys.Follow Travis on:– IG

Segment 1 with Gary Kusin starting at 0:00.I remember when my kids were growing up, my youngest son Daniel visited Gamestop the day it opened up near our home. He said that now since we were so close to Gamestop, our home would be more valuable. I am not sure about that but certainly GameStop has had a big effect on the gaming industry and the stock market.Gary Kusin, co-founded two companies, Babbage's, operating today as GameStop (NYSE: GME), and Laura Mercier Cosmetics. He served from 2001-2006 as President and Chief Executive Officer of Kinko's, today operating as FedEx Office. He was responsible for the turnaround, strategic growth and transformation of Kinko's and oversaw the ultimate sale to FedEx, directly reporting to Fred Smith, founder of FedEx, for the 2 years required to integrate Kinko's into FedEx and be renamed FedEx Office.Segment 2 with Tami Cannizzaro starting at 19:05.How should small business owners use AI?Tami Cannizzaro is the Chief Marketing Officer of Thryv, provider of the leading do-it-all small business software platform empowering small businesses to modernize how they work. Thryv offers small business owners everything they need to communicate effectively, manage their day-to-day operations, and grow — all in one place.

If you listen to our show, odds are you're over 30, which means you can go into any restaurant you want. It also means you probably remember when GameStop was a place people actually used to buy games from, rather than a means of manipulating the stock market. If you really paid attention, you might even remember when it was still Babbage's and FuncoLand. Learn more about your ad choices. Visit podcastchoices.com/adchoicesSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

If you listen to our show, odds are you're over 30, which means you can go into any restaurant you want. It also means you probably remember when GameStop was a place people actually used to buy games from, rather than a means of manipulating the stock market. If you really paid attention, you might even remember when it was still Babbage's and FuncoLand. Learn more about your ad choices. Visit megaphone.fm/adchoices

Go behind the scenes of how Gary Kusin systematically built Babbage's, which later became GameStop, into a market leader and how Gary led a major turnaround of Kinko's, taking EBITDA from -$11M to +$180M in just 3 years. Learn how he made difficult decisions like closing stores and reducing headcount, while aligning the team around new leadership principles and business lessons learned directly from iconic leaders like Fred Smith of FedEx, Jack Welch of GE, and Ross Perot. Learn more about your ad choices. Visit megaphone.fm/adchoices

Ever since there have been smartphones and social media, there have been concerns about how they might be affecting children. Over the past decade, doctors have seen a decline in mental health in the young in much of the rich world. But whether that rise can be attributed to technology is still a matter of fierce debate. Nevertheless, demands are growing to proactively restrict teenagers' access to phones and social media, just in case. How concerned should parents and teachers be? Or is this just another moral panic? Host: Alok Jha, The Economist's science and technology editor. Contributors: Tom Wainwright, The Economist's technology and media editor; Clare Fernyhough, co-founder of Smartphone Free Childhood; Carol Vidal of Johns Hopkins University; Pete Etchells, a psychologist at Bath Spa University and the author of “Unlocked: The Real Science of Screen Time”.Listen to what matters most, from global politics and business to science and technology—subscribe to Economist Podcasts+For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account. Hosted on Acast. See acast.com/privacy for more information.

Ever since there have been smartphones and social media, there have been concerns about how they might be affecting children. Over the past decade, doctors have seen a decline in mental health in the young in much of the rich world. But whether that rise can be attributed to technology is still a matter of fierce debate. Nevertheless, demands are growing to proactively restrict teenagers' access to phones and social media, just in case. How concerned should parents and teachers be? Or is this just another moral panic? Host: Alok Jha, The Economist's science and technology editor. Contributors: Tom Wainwright, The Economist's technology and media editor; Clare Fernyhough, co-founder of Smartphone Free Childhood; Carol Vidal of Johns Hopkins University; Pete Etchells, a psychologist at Bath Spa University and the author of “Unlocked: The Real Science of Screen Time”.Listen to what matters most, from global politics and business to science and technology—subscribe to Economist Podcasts+For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account.

What is intelligence? In the middle of the 20th century, the inner workings of the human brain inspired computer scientists to build the first “thinking machines”. But how does human intelligence actually relate to the artificial kind?This is the first episode in a four-part series on the evolution of modern generative AI. What were the scientific and technological developments that took the very first, clunky artificial neurons and ended up with the astonishingly powerful large language models that power apps such as ChatGPT?Host: Alok Jha, The Economist's science and technology editor. Contributors: Ainslie Johnstone, The Economist's data journalist and science correspondent; Dawood Dassu and Steve Garratt of UK Biobank; Daniel Glaser, a neuroscientist at London's Institute of Philosophy; Daniela Rus, director of MIT's Computer Science and Artificial Intelligence Laboratory; Yoshua Bengio of the University of Montréal, who is known as one of the “godfathers” of modern AI.On Thursday April 4th, we're hosting a live event where we'll answer as many of your questions on AI as possible, following this Babbage series. If you're a subscriber, you can submit your question and find out more at economist.com/aievent. Get a world of insights for 50% off—subscribe to Economist Podcasts+If you're already a subscriber to The Economist, you'll have full access to all our shows as part of your subscription. For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account. Hosted on Acast. See acast.com/privacy for more information.

What is intelligence? In the middle of the 20th century, the inner workings of the human brain inspired computer scientists to build the first “thinking machines”. But how does human intelligence actually relate to the artificial kind?This is the first episode in a four-part series on the evolution of modern generative AI. What were the scientific and technological developments that took the very first, clunky artificial neurons and ended up with the astonishingly powerful large language models that power apps such as ChatGPT?Host: Alok Jha, The Economist's science and technology editor. Contributors: Ainslie Johnstone, The Economist's data journalist and science correspondent; Dawood Dassu and Steve Garratt of UK Biobank; Daniel Glaser, a neuroscientist at London's Institute of Philosophy; Daniela Rus, director of MIT's Computer Science and Artificial Intelligence Laboratory; Yoshua Bengio of the University of Montréal, who is known as one of the “godfathers” of modern AI.On Thursday April 4th, we're hosting a live event where we'll answer as many of your questions on AI as possible, following this Babbage series. If you're a subscriber, you can submit your question and find out more at economist.com/aievent. Get a world of insights for 50% off—subscribe to Economist Podcasts+If you're already a subscriber to The Economist, you'll have full access to all our shows as part of your subscription. For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account. Hosted on Acast. See acast.com/privacy for more information.

Dark matter is thought to make up around a quarter of the universe, but so far it has eluded detection by all scientific instruments. Scientists know it must exist because of the ways galaxies move and it also explains the large-scale structure of the modern universe. But no-one knows what dark matter actually is.Scientists have been hunting for dark matter particles for decades, but have so far had no luck. At the annual meeting of the American Association for the Advancement of Science, held recently in Denver, a new generation of researchers presented their latest tools, techniques and ideas to step up the search for this mysterious substance. Will they finally detect the undetectable? Host: Alok Jha, The Economist's science and technology editor. Contributors: Don Lincoln, senior scientist at Fermi National Accelerator Laboratory; Christopher Karwin, a fellow at NASA's Goddard Space Flight Center; Josef Aschbacher, boss of the European Space Agency; Michael Murra of Columbia University; Jodi Cooley, executive director of SNOLAB; Deborah Pinna of University of Wisconsin and CERN.Get a world of insights for 50% off—subscribe to Economist Podcasts+If you're already a subscriber to The Economist, you'll have full access to all our shows as part of your subscription. For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account. Hosted on Acast. See acast.com/privacy for more information.

Dark matter is thought to make up around a quarter of the universe, but so far it has eluded detection by all scientific instruments. Scientists know it must exist because of the ways galaxies move and it also explains the large-scale structure of the modern universe. But no-one knows what dark matter actually is.Scientists have been hunting for dark matter particles for decades, but have so far had no luck. At the annual meeting of the American Association for the Advancement of Science, held recently in Denver, a new generation of researchers presented their latest tools, techniques and ideas to step up the search for this mysterious substance. Will they finally detect the undetectable? Host: Alok Jha, The Economist's science and technology editor. Contributors: Don Lincoln, senior scientist at Fermi National Accelerator Laboratory; Christopher Karwin, a fellow at NASA's Goddard Space Flight Center; Josef Aschbacher, boss of the European Space Agency; Michael Murra of Columbia University; Jodi Cooley, executive director of SNOLAB; Deborah Pinna of University of Wisconsin and CERN.Get a world of insights for 50% off—subscribe to Economist Podcasts+If you're already a subscriber to The Economist, you'll have full access to all our shows as part of your subscription. For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account. Hosted on Acast. See acast.com/privacy for more information.

OpenAI and Microsoft are leaders in generative artificial intelligence (AI). OpenAI has built GPT-4, one of the world's most sophisticated large language models (LLMs) and Microsoft is injecting those algorithms into its products, from Word to Windows. At the World Economic Forum in Davos last week, Zanny Minton Beddoes, The Economist's editor-in-chief, interviewed Sam Altman and Satya Nadella, who run OpenAI and Microsoft respectively. They explained their vision for humanity's future with AI and addressed some thorny questions looming over the field, such as how AI that is better than humans at doing tasks might affect productivity and how to ensure that the technology doesn't pose existential risks to society.Host: Alok Jha, The Economist's science and technology editor. Contributors: Zanny Minton Beddoes, editor-in-chief of The Economist; Ludwig Siegele, The Economist's senior editor, AI initiatives; Sam Altman, chief executive of OpenAI; Satya Nadella, chief executive of Microsoft. If you subscribe to The Economist, you can watch the full interview on our website or app. Essential listening, from our archive:“Daniel Dennett on intelligence, both human and artificial”, December 27th 2023“Fei-Fei Li on how to really think about the future of AI”, November 22nd 2023“Mustafa Suleyman on how to prepare for the age of AI”, September 13th 2023“Vint Cerf on how to wisely regulate AI”, July 5th 2023“Is GPT-4 the dawn of true artificial intelligence?”, with Gary Marcus, March 22nd 2023Sign up for a free trial of Economist Podcasts+. If you're already a subscriber to The Economist, you'll have full access to all our shows as part of your subscription. For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account. Hosted on Acast. See acast.com/privacy for more information.

Books are the original medium for communicating science to the masses. In a holiday special, producer Kunal Patel asks Babbage's family of correspondents about the books that have inspired them in their careers as science journalists.Host: Alok Jha, The Economist's science and technology editor. Contributors: Rachel Dobbs, The Economist's climate correspondent; Kenneth Cukier, our deputy executive editor; The Economist's Emilie Steinmark; Geoff Carr, our senior editor for science and technology; and Abby Bertics, The Economist's science correspondent. Reading list: “The Periodic Table” by Primo Levi; “When We Cease to Understand the World” by Benjamín Labatut; “A Theory of Everyone” by Michael Muthukrishna; “Madame Curie” by Ève Curie; “Sociobiology” by E. O. Wilson; “The Selfish Gene” by Richard Dawkins; “Why Fish Don't Exist” by Lulu Miller; and “How Far the Light Reaches” by Sabrina Imbler.Sign up for a free trial of Economist Podcasts+. If you're already a subscriber to The Economist, you'll have full access to all our shows as part of your subscription. For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account. Hosted on Acast. See acast.com/privacy for more information.

A year ago, the public launch of ChatGPT took the world by storm and it was followed by many more generative artificial intelligence tools, all with remarkable, human-like abilities. Fears over the existential risks posed by AI have dominated the global conversation around the technology ever since. A pioneer that helped lay the groundwork that underpins generative AI models, Fei-Fei Li, takes a more nuanced approach to. She's pushing for a human-centred way of dealing with AI—treating it as a tool to help enhance—and not replace—humanity, while focussing on the pressing challenges of disinformation, bias and job disruption.Fei-Fei Li, a pioneer that helped lay the groundwork that underpins modern generative AI models, takes a more nuanced approach. She's pushing for a human-centred way of dealing with AI—treating it as a tool to help enhance—and not replace—humanity, while focussing on the pressing challenges of disinformation, bias and job disruption.Fei-Fei Li is the founding co-director of Stanford University's Institute for Human-Centred Artificial Intelligence. Fei-Fei and her research group created ImageNet, a huge database of images that enabled computers scientists to build algorithms that were able to see and recognise objects in the real world. That endeavour also introduced the world to deep learning, a type of machine learning that is fundamental part of how large-language and image-creation models work.Host: Alok Jha, The Economist's science and technology editor. Sign up for a free trial of Economist Podcasts+. If you're already a subscriber to The Economist, you'll have full access to all our shows as part of your subscription. For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account. Hosted on Acast. See acast.com/privacy for more information.

In the coming decades, electric vehicles will dominate the roads and renewables will provide energy to homes. But for the green transition to be successful, unprecedented amounts of energy storage is needed. Batteries will be used everywhere—from powering electric vehicles, to providing electricity when the sun doesn't shine or the wind doesn't blow. The current generation of batteries are lacking in capacity and are too reliant on rare metals, though. Many analysts worry about material shortages. How can technology help? Host: Alok Jha, The Economist's science and technology editor. Contributors: Paul Markillie, our innovation editor; Matthieu Favas, our finance correspondent; Anjani Trivedi, our global business correspondent. Sign up for Economist Podcasts+ now and get 50% off your subscription with our limited time offer. If you're already a subscriber to The Economist, you'll have full access to all our shows as part of your subscription. For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account. Hosted on Acast. See acast.com/privacy for more information.

Chronic pain is thought to affect around a third of people. For one in ten of these, the pain is severe enough to be disabling—making it the leading cause of disability worldwide. Some forms of chronic pain are particularly mysterious—with clinicians unable to treat the pain, nor understand its causal mechanisms—presenting a huge challenge for societies. How can this burden be eased, for both healthcare systems and the individuals living with pain? Host: Alok Jha, The Economist's science and technology editor, with Gilead Amit, our science correspondent. Contributors: Catherine Charlwood, who lives with chronic pain; Francis Keefe, director of the Pain Prevention and Treatment Research Program at Duke University; Matt Evans, a clinical lecturer at Chelsea and Westminster Hospital and Imperial College London; Jan Vollert, a pain researcher at the University of Exeter.Sign up for Economist Podcasts+ now and get 50% off your subscription with our limited time offer. You will not be charged until Economist Podcasts+ launches.If you're already a subscriber to The Economist, you'll have full access to all our shows as part of your subscription. For more information about Economist Podcasts+, including how to get access, please visit our FAQs page. Hosted on Acast. See acast.com/privacy for more information.