Podcasts about Cobalt

In the leadership and communications section, Why Every CISO Should Be Gunning For A Seat At The Board Table, The Innovation We Need is Strategic, Not Technical , The Best Leaders Ask the Right Questions, and more! This segment is sponsored by Fortra. Visit https://securityweekly.com/fortrarsac to learn more about them! Fortra is successfully reducing the unauthorized use of Cobalt Strike among cybercriminals through partnerships with Microsoft, Operation MORPHEUS, and the Pall Mall Process, among others. Since 2023 specifically, Fortra's collaborations have resulted in an 80% drop in Cobalt Strike misuse in the wild. Additionally, the time between detecting cracked copies and mitigation has been reduced to less than one week in the United States and less than two weeks worldwide. Segment Resources: https://www.cobaltstrike.com/blog/update-stopping-cybercriminals-from-abusing-cobalt-strike This segment is sponsored by LevelBlue. Visit https://securityweekly.com/levelbluersac to learn more about them! Uncover how organizations are building business confidence through cyber resilience, how alignment of cybersecurity and business goals impacts business, how collaboration creates a proactive culture, and how emerging attacks are evolving. Visit https://www.securityweekly.com/bsw for all the latest episodes! Show Notes: https://securityweekly.com/bsw-396

In the leadership and communications section, Why Every CISO Should Be Gunning For A Seat At The Board Table, The Innovation We Need is Strategic, Not Technical , The Best Leaders Ask the Right Questions, and more! This segment is sponsored by Fortra. Visit https://securityweekly.com/fortrarsac to learn more about them! Fortra is successfully reducing the unauthorized use of Cobalt Strike among cybercriminals through partnerships with Microsoft, Operation MORPHEUS, and the Pall Mall Process, among others. Since 2023 specifically, Fortra's collaborations have resulted in an 80% drop in Cobalt Strike misuse in the wild. Additionally, the time between detecting cracked copies and mitigation has been reduced to less than one week in the United States and less than two weeks worldwide. Segment Resources: https://www.cobaltstrike.com/blog/update-stopping-cybercriminals-from-abusing-cobalt-strike This segment is sponsored by LevelBlue. Visit https://securityweekly.com/levelbluersac to learn more about them! Uncover how organizations are building business confidence through cyber resilience, how alignment of cybersecurity and business goals impacts business, how collaboration creates a proactive culture, and how emerging attacks are evolving. Visit https://www.securityweekly.com/bsw for all the latest episodes! Show Notes: https://securityweekly.com/bsw-396

Morgan Lekstrom, CEO of Premium Resources (TSX.V:PREM), outlines how the company is unlocking value from two past-producing, high-grade critical metal assets in Botswana.   In this KE Report company introduction, we feature Premium Resources as it advances the Selkirk open-pit and Selebi underground Projects, both with a history of production.    Morgan highlights why Botswana is a top-tier jurisdiction for mining, detailing the company's aggressive strategy to scale up both assets through expansion drilling, re-assays, metallurgy, and parallel economic studies.   The Selebi Project hosts 30Mt @ 3.35% CuEq underground, while Selkirk is undergoing a low-cost re-assay campaign aimed at validating a historic 130Mt+ resource.    With over $46M recently raised and debt eliminated via equity conversion with major shareholder EdgePoint, Premium is fully funded to aggressively advance both projects in tandem.   Morgan also discusses the company's upcoming uplisting to the Nasdaq, a key strategic move to broaden institutional access and increase liquidity. He introduces members of the team and board, including recent additions with experience at BlackRock, Gatos Silver, and Freeport-McMoRan.   Upcoming news flow includes drilling, resource updates, metallurgical work, and early-stage economics through 2025.   Any follow up questions for Morgan? Please comment below or email me directly at Fleck@kereport.com.    Click here to visit the Premium Resources website.

In the leadership and communications section, How CISOs can talk cybersecurity so it makes sense to executives, Firms to spend more on GenAI than security in 2025, Europe leads shift from cyber security ‘headcount gap' to skills-based hiring, and more! Next, pre-recorded interviews from RSAC Conference 2025, including: This segment is sponsored by Fortinet. Visit https://securityweekly.com/fortinetrsac to learn more about them! Unpacking the latest annual report from Fortinet's FortiGuard Labs. We're talking with Derek Manky, Chief Security Strategist and Global VP Threat Intelligence, Fortinet's FortiGuard Labs, to get a snapshot of the active threat landscape and trends from 2024, including a comprehensive analysis across all tactics used in cyberattacks, as outlined in the MITRE ATT&CK framework. The report reveals that threat actors are increasingly harnessing automation, commoditized tools, and AI to systematically erode the traditional advantages held by defenders. Read the full report at https://securityweekly.com/fortinetrsac. This segment is sponsored by Cobalt. Visit https://securityweekly.com/cobaltrsac to learn more about them! In this interview, Gunter Ollmann, Chief Technology Officer at Cobalt, unpacks the findings from the State of Pentesting Report 2025, spotlighting both measurable security progress and the rising challenges introduced by generative AI (genAI). While the report shows that organizations are resolving vulnerabilities faster than ever, genAI systems stand out as a growing security blind spot: only 21% of serious genAI vulnerabilities identified during penetration testing are fixed, compared to over 75% for API flaws and 68% for cloud vulnerabilities. Nearly 32% of genAI-related findings were classified as high risk — more than double the average across other systems. And although 98% of organizations are adopting genAI-powered features, only 66% are running regular security assessments on those systems. Segment Resources: https://www.cobalt.io/blog/key-takeaways-state-of-pentesting-report-2025 https://resource.cobalt.io/state-of-pentesting-2025?gl=1*zwbjgz*gclaw*R0NMLjE3MzcwNTU5ODMuQ2owS0NRaUEtYUs4QmhDREFSSXNBTF8tSDltRlB0X2FmSVhnQnBzSjYxOHlRZ1dhcmRMQ0lHalo3eVgxcTh1cHVnWFVwV0todHFPSDFZZ2FBb0hNRUFMd193Y0I.*gcl_au*MTc4MjQwMTAwNC4xNzQ0NjM0MTgz Visit https://www.securityweekly.com/bsw for all the latest episodes! Show Notes: https://securityweekly.com/bsw-395

In the leadership and communications section, How CISOs can talk cybersecurity so it makes sense to executives, Firms to spend more on GenAI than security in 2025, Europe leads shift from cyber security ‘headcount gap' to skills-based hiring, and more! Next, pre-recorded interviews from RSAC Conference 2025, including: This segment is sponsored by Fortinet. Visit https://securityweekly.com/fortinetrsac to learn more about them! Unpacking the latest annual report from Fortinet's FortiGuard Labs. We're talking with Derek Manky, Chief Security Strategist and Global VP Threat Intelligence, Fortinet's FortiGuard Labs, to get a snapshot of the active threat landscape and trends from 2024, including a comprehensive analysis across all tactics used in cyberattacks, as outlined in the MITRE ATT&CK framework. The report reveals that threat actors are increasingly harnessing automation, commoditized tools, and AI to systematically erode the traditional advantages held by defenders. Read the full report at https://securityweekly.com/fortinetrsac. This segment is sponsored by Cobalt. Visit https://securityweekly.com/cobaltrsac to learn more about them! In this interview, Gunter Ollmann, Chief Technology Officer at Cobalt, unpacks the findings from the State of Pentesting Report 2025, spotlighting both measurable security progress and the rising challenges introduced by generative AI (genAI). While the report shows that organizations are resolving vulnerabilities faster than ever, genAI systems stand out as a growing security blind spot: only 21% of serious genAI vulnerabilities identified during penetration testing are fixed, compared to over 75% for API flaws and 68% for cloud vulnerabilities. Nearly 32% of genAI-related findings were classified as high risk — more than double the average across other systems. And although 98% of organizations are adopting genAI-powered features, only 66% are running regular security assessments on those systems. Segment Resources: https://www.cobalt.io/blog/key-takeaways-state-of-pentesting-report-2025 https://resource.cobalt.io/state-of-pentesting-2025?gl=1*zwbjgz*gclaw*R0NMLjE3MzcwNTU5ODMuQ2owS0NRaUEtYUs4QmhDREFSSXNBTF8tSDltRlB0X2FmSVhnQnBzSjYxOHlRZ1dhcmRMQ0lHalo3eVgxcTh1cHVnWFVwV0todHFPSDFZZ2FBb0hNRUFMd193Y0I.*gcl_au*MTc4MjQwMTAwNC4xNzQ0NjM0MTgz Visit https://www.securityweekly.com/bsw for all the latest episodes! Show Notes: https://securityweekly.com/bsw-395

In the leadership and communications section, How CISOs can talk cybersecurity so it makes sense to executives, Firms to spend more on GenAI than security in 2025, Europe leads shift from cyber security ‘headcount gap' to skills-based hiring, and more! Next, pre-recorded interviews from RSAC Conference 2025, including: This segment is sponsored by Fortinet. Visit https://securityweekly.com/fortinetrsac to learn more about them! Unpacking the latest annual report from Fortinet's FortiGuard Labs. We're talking with Derek Manky, Chief Security Strategist and Global VP Threat Intelligence, Fortinet's FortiGuard Labs, to get a snapshot of the active threat landscape and trends from 2024, including a comprehensive analysis across all tactics used in cyberattacks, as outlined in the MITRE ATT&CK framework. The report reveals that threat actors are increasingly harnessing automation, commoditized tools, and AI to systematically erode the traditional advantages held by defenders. Read the full report at https://securityweekly.com/fortinetrsac. This segment is sponsored by Cobalt. Visit https://securityweekly.com/cobaltrsac to learn more about them! In this interview, Gunter Ollmann, Chief Technology Officer at Cobalt, unpacks the findings from the State of Pentesting Report 2025, spotlighting both measurable security progress and the rising challenges introduced by generative AI (genAI). While the report shows that organizations are resolving vulnerabilities faster than ever, genAI systems stand out as a growing security blind spot: only 21% of serious genAI vulnerabilities identified during penetration testing are fixed, compared to over 75% for API flaws and 68% for cloud vulnerabilities. Nearly 32% of genAI-related findings were classified as high risk — more than double the average across other systems. And although 98% of organizations are adopting genAI-powered features, only 66% are running regular security assessments on those systems. Segment Resources: https://www.cobalt.io/blog/key-takeaways-state-of-pentesting-report-2025 https://resource.cobalt.io/state-of-pentesting-2025?gl=1*zwbjgz*gclaw*R0NMLjE3MzcwNTU5ODMuQ2owS0NRaUEtYUs4QmhDREFSSXNBTF8tSDltRlB0X2FmSVhnQnBzSjYxOHlRZ1dhcmRMQ0lHalo3eVgxcTh1cHVnWFVwV0todHFPSDFZZ2FBb0hNRUFMd193Y0I.*gcl_au*MTc4MjQwMTAwNC4xNzQ0NjM0MTgz Show Notes: https://securityweekly.com/bsw-395

Critical minerals are required for the manufacturing of electronics, aerospace equipment, medical devices, and renewable energy technologies, making them essential for a country's economic and national security. These materials have been at the center of China's domestic and foreign policy for many decades, and China's ability to integrate internal industrial policies with foreign trade and investment policies has allowed them to gain dominance in the market. Meanwhile, the US has lagged behind China in terms of both access to and processing technology of critical minerals. The country has been heavily dependent on China for its critical minerals and struggles to find an alternative supplier.China's announcement to impose export restrictions on seven rare earth elements on April 4th has opened many conversations surrounding critical minerals, especially regarding the US and its supply chain vulnerabilities. What has China done to achieve their global dominance in the critical minerals sector, and what can the US do to address the overdependence issue they are facing today? To answer these questions and more, host Bonnie Glaser is joined by Gracelin Baskaran, the director of the Critical Minerals Security Program at the Center for Strategic and International Studies. She is a mining economist whose area of expertise is critical minerals and trade. Timestamps[00:00] Start[02:13] US Dependencies on Rare Earths and Critical Minerals[03:51] Sourcing from Latin America, Africa, and Asia[06:28] Environmental Harm from Mining and Processing[08:11] Deliberate Suppression of the Price of Rare Earths in the Market[11:06] Chinese Exports Restrictions on Seven Rare Earth Elements[14:08] US Administrations' Approaches to Critical Minerals Vulnerability[20:02] 2010 Fishing Boat Accident and Japan's Response [24:00] What might China do moving forward? [27:42] Timeframe for the US to Catch Up to China

La demande en cobalt pourrait augmenter de plus de 10% cette année. C'est ce que prévoit l'Institut du Cobalt, une organisation qui regroupe l'ensemble des industriels du secteur. Une fois encore les besoins en cobalt sont essentiellement tirés par la fabrication des batteries contenues dans les véhicules électriques. La hausse annoncée de la demande en cobalt est directement liée aux bonnes ventes des véhicules électriques. Cette hausse s'observe au premier trimestre 2025 sur tous les grands marchés : +22% en Europe, +16% aux États-Unis et +36% en Chine, pays qui remporte la palme avec des ventes qui ont décollé au premier trimestre.  Globalement, la demande mondiale en cobalt pourrait être de 227 000 tonnes cette année, selon l'Institut du Cobalt soit 11% de plus que l'année dernière. L'année 2024 avait connu une hausse de « seulement » 4% par rapport à l'année précédente.Un marché toujours amputé de la production congolaiseLe cobalt congolais est interdit d'exportation, depuis fin février, or il représente les deux tiers de l'approvisionnement mondial. La décision a été prise pour quatre mois, mais pourrait être rediscutée d'ici fin mai. Entre-temps, la possibilité de mettre en place des quotas d'exportation a été évoquée, ainsi que d'éventuelles concertations avec l'Indonésie, le deuxième producteur mondial de cobalt, l'idée étant de trouver une manière de gérer la suroffre et de mieux contrôler les prix. La décision de Kinshasa a permis de faire remonter les prix en flèche pendant un mois, ils ont depuis marqué une pause, dans l'attente peut-être de nouvelles annonces.À lire aussiLa RDC suspend les exportations de cobalt pour voir remonter les prixForte baisse des stocks hors de RDC En Afrique, en dehors de la RDC, les stocks de cobalt sont entreposés en Zambie et en Afrique du Sud. Sinon, ils sont essentiellement situés en Chine et en Malaisie, selon le cabinet d'études Project Blue. Fin décembre, ces réserves étaient jugées suffisantes pour répondre à la demande du marché pendant quatre mois environ, mais pas pour faire face à une interdiction beaucoup plus longue.Le gel des exportations congolaises va inévitablement réduire les stocks hors de RDC mais n'empêchera pas une accumulation de cobalt dans le pays, ont relevé les experts de Project Blue dans une de leurs notes d'information. Seul un ralentissement de la production minière et une demande plus importante pourrait influer sur l'excédent mondial et sur les prix du cobalt.À lire aussiLes pays du Golfe, futur hub du raffinage de minerais critiques?

Africa stands at the forefront of the global climate crisis, facing extreme weather events, rising temperatures, and biodiversity loss—despite contributing less than 4% of global greenhouse gas emissions. As debates intensify over carbon credit agreements, oil exploration in the Congo Basin, and the role of Africa in shaping global climate policy, questions remain about how the continent can advocate for equitable and sustainable solutions.     In this episode, Mvemba is joined by Tosi Mpanu-Mpanu, Health, Safety, and Environment Director at Entreprise Générale du Cobalt. Together, they explore Africa's unique climate challenges, the complexities of carbon markets, and the region's growing influence in global environmental negotiations.  

Xerion says it has a new technique to produce highly refined cobalt in a single step. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Interview with Chris Stevens, CEO of Coda Minerals Ltd.Our previous interview: https://www.cruxinvestor.com/posts/coda-minerals-compelling-junior-unlocking-value-in-south-australian-copper-cobaltRecording date: 15th April 2025Coda Minerals is making significant progress on its Elizabeth Creek copper-cobalt-silver project in South Australia, positioning the resource for development amid growing global demand for critical minerals. Located six hours north of Adelaide and adjacent to BHP's Carrapateena Copper Project, Elizabeth Creek hosts substantial mineral resources including approximately 800,000 tons of copper, 30,000 tons of cobalt, and 28 million ounces of silver.The project consists of three primary deposits - two open pits (MG14 and Windabout) that will provide early production, and the larger Emmie Bluff underground deposit. With a resource grade of approximately 1.9% copper equivalent, CEO Chris Stevens believes the project compares favorably to competitors, noting that "some of the really large projects that you see kicking around in terms of contained tonnage have a lower head grade going into the mill than our waste dump."A completed scoping study demonstrates strong economics with a pre-tax NPV of $1.2 billion ($802 million post-tax) based on a copper price of $4.20 per pound. Capital expenditure is estimated at approximately A$680 million, with annual production projected at 26,000-27,000 tons of copper and 1,300 tons of cobalt.The company is currently focused on metallurgical optimization to reduce capital costs significantly by investigating alternatives to conventional flotation and Albion processing circuits. Stevens emphasized that these changes "have the potential to be game-changing for the project."Elizabeth Creek benefits from excellent infrastructure, including proximity to the Stuart Highway, a 133 KVA electrical substation on the property, and access to the BHP haul road. Stevens highlighted South Australia's streamlined mining regulations and the project's ESG advantages, particularly for cobalt production, creating "a compelling alternative to DRC-sourced cobalt."With $4.5 million in cash, Coda is taking a disciplined approach to capital deployment in the current challenging market, focusing on critical path items such as approvals and optimization studies. The project qualifies for the Australian government's 'Future Made in Australia' policy, potentially providing approximately $25 million in benefits.Looking ahead, Stevens expressed confidence in copper market fundamentals, noting that new discoveries are increasingly rare while existing mines face declining grades and rising costs. Coda's combination of grade, scale, and jurisdiction positions it well to capitalize on the growing structural supply deficit in the copper market as global demand continues to accelerate.View Coda Minerals' company profile: https://www.cruxinvestor.com/companies/coda-minerals-ltdSign up for Crux Investor: https://cruxinvestor.com

Electric cars look clean—but what's behind the battery? This video reveals the brutal truth about cobalt mining in Congo, where children dig for the metal powering your EV. Tesla, Apple, and others rely on it. Watch to see the real cost of going green. Don't forget to like, share, and subscribe if you believe the full truth about EVs deserves more attention. Your awareness is a powerful tool.electric cars are not clean children dying for EV batteries, what's powering your EV, is Tesla ethical?EVs and modern slavery, cobalt mines expose hypocrisy, the truth they won't tell you, blood batteries explained, the cost of going green, environmental lies in the EV industryBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-motor-files-podcast--4960744/support.

The newest installment to my colors mix series. With all the mixes I do, I sometimes feel like I hold Rhéa hostage all by herself and she's forced to listen to too many of my live mixing. As a solution, she'd been wanting to invite some close friends over that she could host as I shot an episode and we agreed on a 2000s R&B as the soundtrack to the day. I wanted to tap into some nostalgic songs for us Millennials (a la 106 & Park, Cita's World, MTV Jams etc). S/O to all our friends that pulled up and vibed with us on this episode. This one features Faith Evans, Monica, Donnell Jones, Craig David, Backyard Band, Beyoncé & more. Press play and enjoy! YouTube Link: https://www.youtube.com/watch?v=m0oQZKOplNU&t=3647s #mix #rnb #2000srnb #rhythmandblues Tracklist: I'm Sprung - T-Pain Slow Down - Bobby Valentino Where I Wanna Be - Donell Jones Anything - Jaheim + Next Dangerously in Love 2 - Beyoncé Teach U A Lesson - Robin Thicke Let's Get Married - Jagged Edge How You Gonna Act Like That - Jagged Edge Separated - Avant Say Goodbye - Chris Brown Say Yes - Floetry You Complete Me - Keyshia Cole Wetter - Twista + Erika Shevon Until The End Of Time - Justin Timberlake + Beyoncé Burn - Usher Come With Me - Sammie Chopped N Skrewed - T-Pain + Ludacris Twinz (Deep Cover 2018) (Flexican Edit) - Big Pun + Fat Joe Promise - Ciara Suffocate - J. Holiday Emotional Rollercoaster - Vivian Green Charlene - Anthony Hamilton Differences - Ginuwine One Wish - Ray J Since You've Been Gone - Day26 The Business - Yung Berg + Casha Ice Box - Omarion Say It - Ne-Yo I Don't Wanna - Aaliyah Thank God I Found You - Mariah Carey Shake It Off - Mariah Carey So Lonely (One & Only, Pt. II) - Mariah Carey + Twista We Belong Together - Mariah Carey Don't Forget About Us - Mariah Carey Fly Like A Bird - Mariah Carey Free - Mr. Carmack Touch My Body - Mariah Carey Always Be My Baby - Mariah Carey Young Love - Chris Brown 7 Days - Craig David Suga Suga - Baby Bash One Call Away - Chingy + Jason Weaver Call on Me - Janet Jackson All I Have (feat. LL Cool J) - Jennifer Lopez + LL Cool J So Gone - Monica I Love You - Faith Evans Lost Without You - Robin Thicke Valentine - Lloyd T-Shirt - Shontelle T-Shirt On - Backyard Band No Ordinary Love (Cover) - Rare Essence (Ms. Kim) + Sade It's Love - Jill Scott Bag Lady (Bo "Locals Only" Blend) - Erykah Badu + Dom Kennedy I Wish - Carl Thomas Can't Believe It - T-Pain + Lil Wayne Girlfriend - B2K Knock Knock - Monica Come Back In One Piece - Aaliyah + DMX Down Ass Bitch - Ja Rule + Charlie Baltimore 21 Questions - 50 Cent + Nate Dogg Irreplaceable - Beyoncé Like This And Like That - Monica

Brandt, Tyler, and Brennan, talk red card, no red card, second yellow, third yellow? Some early great play from RSL was not enough to overcome the Nashville pressure and maybe some in the box defending, again, from the Claret and Cobalt. Listen and Subscribe!

Most dairy farms are trying to push the envelope from the conventional 4L of milk replacer per day to a higher volume to support lean structural growth of replacement heifers. Concerns of ruminal leakage resulting in fermentation has been cited as a concern, but is that based on an incomplete picture?  We know microbial protein offers a nearly perfect alignment of amino acid requirements for the bovine, however nutrition programs have discounted milk replacer to exclusively providing rumen undegradable protein (RUP) – protein that skips the rumen and is absorbed in the abomasum. Marcos Marcondes, researcher from the Miner Institute, wanted to see if feeding higher volumes would change the flow of protein and energy to the calf and the physiology of the rumen, due to leakage. To test this question he fed a standard rate and double rate of milk replacer with colbalt to mark and track the flow of digestion through preweaned animals. Leakage was found in the rumen on both treatments, but the results were positive. Energy, in the form of volatile fatty acids, and microbial protein added to the fuel for these growing animals.  Listen in to better understand the kinetics of digestion and questions that still remain for this phase of production. Topics of discussion 1:44       Introduction of Dr. Marcos Marcondes 2:50       Lambs as a model for bovine calves, 4 & 8L/day  4:07       Known info on the kinetics of milk protein 6:46       Relevance of microbial protein in fueling cattle 8:17       Cobalt marker used in the research model    10:51     Rumen leakage for preweaned calves   13:14     Grain feeding during trial 14:30     Milk replacer vs Whole milk 17:01    Retention rates 18:41    Colostrum protocol – no tubing 20:02    VFA and Microbial protein production 25:37    Physiology and histology 27:32    What do you want Boots on the Ground dairy producers to gain from the project?  Featured Article: Influence of different amounts of milk replacer on esophageal leakage, rumen fermentation characteristics, gastrointestinal tract passage rate, and microbial crude protein synthesis of nursling animals #2xAg2030; #journalofdairyscience; #openaccess; #MODAIRY; #prewean; #dairycalves; #microbialprotein; #MinerInstitute; #kinetics; #dairysciencedigest; #ReaganBluel;   

Kenyan President William Ruto is scheduled to travel to Beijing later this month for an official state visit, where he's widely expected to finalize a long-awaited deal to extend the Chinese-built Standard Gauge Railway (SGR) to the Ugandan border. But the key question remains: will China agree to fund the 475-kilometer extension? Eric and Géraud also explore why a Chinese mining company continues to produce large volumes of cobalt in the Democratic Republic of the Congo, despite a government ban on exports of the valuable blue metal. Plus, they unpack the latest testimony from General Michael Langley, the top U.S. military commander for Africa, and what his comments reveal about Washington's current outlook on China-Africa relations. JOIN THE DISCUSSION: X: @ChinaGSProject | @eric_olander | @christiangeraud Facebook: www.facebook.com/ChinaAfricaProject YouTube: www.youtube.com/@ChinaGlobalSouth Now on Bluesky! Follow CGSP at @chinagsproject.bsky.social FOLLOW CGSP IN FRENCH AND ARABIC: Français: www.projetafriquechine.com | @AfrikChine Arabic: عربي: www.alsin-alsharqalawsat.com | @SinSharqAwsat JOIN US ON PATREON! Become a CGSP Patreon member and get all sorts of cool stuff, including our Week in Review report, an invitation to join monthly Zoom calls with Eric & Cobus, and even an awesome new CGSP Podcast mug! www.patreon.com/chinaglobalsouth

In late February in DC, I attended the US premiere of the Bertelsmann Foundation of North America produced documentary “Lithium Rising”, a movie about the extraction of essential rare minerals like lithium, nickel and cobalt. Afterwards, I moderated a panel featuring the movie's director Samuel George, the Biden US Department of Energy Director Giulia Siccardo and Environmental Lawyer JingJing Zhang (the "Erin Brockovich of China"). In post Liberation Day America, of course, the issues addressed in both “Lithium Rising” and our panel discussion - particularly US-Chinese economic rivalry over these essential rare minerals - are even more relevant. Tariffs or not, George's important new movie uncovers the essential economic and moral rules of today's rechargeable battery age. FIVE TAKEAWAYS* China dominates the critical minerals supply chain, particularly in refining lithium, cobalt, and nickel - creating a significant vulnerability for the United States and Western countries who rely on these minerals for everything from consumer electronics to military equipment.* Resource extraction creates complex moral dilemmas in communities like those in Nevada, Bolivia, Congo, and Chile, where mining offers economic opportunities but also threatens environment and sacred lands, often dividing local populations.* History appears to be repeating itself with China's approach in Africa mirroring aspects of 19th century European colonialism, building infrastructure that primarily serves to extract resources while local communities remain impoverished.* Battery recycling offers a potential "silver lining" but faces two major challenges: making the process cost-effective compared to new mining, and accumulating enough recycled materials to create a closed-loop system, which could take decades.* The geopolitical competition for these minerals is intensifying, with tariffs and trade wars affecting global supply chains and the livelihoods of workers throughout the system, from miners to manufacturers. FULL TRANSCRIPTAndrew Keen: Hello, everybody. Last year, we did a show on a new book. It was a new book back then called Cobalt Red about the role of cobalt, the mineral in the Congo. We also did a show. The author of the Cobalt Red book is Siddharth Kara, and it won a number of awards. It's the finalist for the Pulitzer Prize. We also did a show with Ernest Scheyder, who authored a book, The War Below, Lithium, Copper, and the Global Battle to Power Our Lives. Lithium and cobalt are indeed becoming the critical minerals of our networked age. We've done two books on it, and a couple of months ago, I went to the premiere, a wonderful new film, a nonfiction documentary by my guest Samuel George. He has a new movie out called Lithium Rising and I moderated a panel in Washington DC and I'm thrilled that Samuel George is joining us now. He works with the Bertelsmann Foundation of North America and it's a Bertelsman funded enterprise. Sam, congratulations on the movie. It's quite an achievement. I know you traveled all over the world. You went to Europe, Latin America, a lot of remarkable footage also from Africa. How would you compare the business of writing a book like Cobalt read or the war below about lithium and cobalt and the challenges and opportunities of doing a movie like lithium rising what are the particular challenges for a movie director like yourself.Samuel George: Yeah, Andrew. Well, first of all, I just want to thank you for having me on the program. I appreciate that. And you're right. It is a very different skill set that's required. It's a different set of challenges and also a different set of opportunities. I mean, the beauty of writing, which is something I get a chance to do as well. And I should say we actually do have a long paper coming out of this process that I wrote that will probably be coming out in the next couple months. But the beauty of writing is you need to kind of understand your topic, and if you can really understand your topics, you have the opportunity to explain it. When it comes to filming, if the camera doesn't have it, you don't have it. You might have a sense of something, people might explain things to you in a certain way, but if you don't have it on your camera in a way that's digestible and easy for audience to grasp, it doesn't matter whether you personally understand it or not. So the challenge is really, okay, maybe you understand the issue, but how do you show it? How do you bring your audience to that front line? Because that's the opportunity that you have that you don't necessarily have when you write. And that's to take an audience literally to these remote locations that they've never been and plant their feet right in the ground, whether that be the Atacama in Northern Chile, whether that'd be the red earth of Colwaisy in the Democratic Republic of the Congo. And that's the beauty of it, but it takes more of making sure you get something not just whether you understand it is almost irrelevant. I mean I guess you do need to understand it but you need to be able to draw it out of a place. It's easier when you're writing to get to some of these difficult places because you don't have to bring 900 pounds of equipment and you can kind of move easier and you're much more discreet. You can get places much easier as you can imagine, where with this, you're carrying all this equipment down. You're obvious from miles away. So you really have to build relationships and get people to get comfortable with you and be willing to speak out. So it's different arts, but it's also different rewards. And the beauty of being able to combine analysis with these visuals is really the draw of what makes documentary so magic because you're really kind of hitting different senses at the same time, visual, audio, and combining it to hopefully make some sort of bigger story.Andrew Keen: Well, speaking, Sam, of audio and visuals, we've got a one minute clip or introduction to the movie. People just listening on this podcast won't get to see your excellent film work, but everybody else will. So let's just have a minute to see what lithium rising is all about. We'll be back in a minute.[Clip plays]Andrew Keen: Here's a saying that says that the natural resources are today's bread and tomorrow's hunger. Great stuff, Sam. That last quote was in Spanish. Maybe you want to translate that to English, because I think, in a sense, it summarizes what lithium rising is about.Samuel George: Right. Well, that's this idea that natural resources in a lot of these places, I mean, you have to take a step back that a lot of these resources, you mentioned the lithium, the cobalt, you can throw nickel into that conversation. And then some of the more traditional ones like copper and silver, a lot are in poor countries. And for centuries, the opportunity to access this has been like a mirage, dangled in front of many of these poor countries as an opportunity to become more wealthy. Yet what we continue to see is the wealth, the mineral wealth of these countries is sustaining growth around the world while places like Potosí and Bolivia remain remarkably poor. So the question on their minds is, is this time gonna be any different? We know that Bolivia has perhaps the largest lithium deposits in the world. They're struggling to get to it because they're fighting amongst each other politically about what's the best way to do it, and is there any way to it that, hey, for once, maybe some of this resource wealth can stay here so that we don't end up, as the quote said, starving. So that's where their perspective is. And then on the other side, you have the great powers of the world who are engaged in a massive competition for access to these minerals.Andrew Keen: And let's be specific, Sam, we're not talking about 19th century Europe and great powers where there were four or five, they're really only two great powers when it comes to these resources, aren't they?Samuel George: I mean, I think that's fair to say. I think some people might like to lump in Western Europe and the EU with the United States to the extent that we used to traditionally conceive of them as being on the same team. But certainly, yes, this is a competition between the United States and China. And it's one that, frankly, China is winning and winning handily. And we can debate what that means, but it's true. I showed this film in London. And a student, who I believe was Chinese, commented, is it really fair to even call this a race? Because it seems to be over.Andrew Keen: Yeah, it's over. You showed it at King's College in London. I heard it was an excellent event.Samuel George: Yeah, it really was. But the point here is, to the extent that it's a competition between the United States and China, which it is, China is winning. And that's of grave concern to Washington. So there's the sense that the United States needs to catch up and need to catch up quickly. So that's the perspective that these two great powers are going at it from. Whereas if you're the Democratic Republic of Congo, if you are Bolivia, if your Chile, you're saying, what can we do to try to make the most of this opportunity and not just get steamrolled?Andrew Keen: Right. And you talk about a grave concern. Of course, there is grave concern both in Washington, D.C. and Beijing in terms of who's winning this race for these natural resources that are driving our networked age, our battery powered age. Some people might think the race has ended. Some people may even argue that it hasn't even really begun. But of course, one of the biggest issues, and particularly when it comes to the Chinese, is this neocolonial element. This was certainly brought out in Cobalt Red, which is quite a controversial book about the way in which China has essentially colonized the Congo by mining Cobalt in Congo, using local labor and then shipping out these valuable resources back to China. And of course, it's part of a broader project in Africa of the Chinese, which for some critics actually not that different from European 19th century colonialism. That's why we entitled our show with Siddharth Kara, The New Heart of Darkness. Of course, the original Heart of darkness was Joseph Conrad's great novel that got turned into Apocalypse Now. Is history repeating itself, Sam, when it comes to these natural resources in terms of the 19th-century history of colonialism, particularly in Africa?Samuel George: Yeah, I mean, I think it's so one thing that's fair to say is you hear a lot of complaining from the West that says, well, look, standards are not being respected, labor is being taken advantage of, environment is not being taken care of, and this is unfair. And this is true, but your point is equally true that this should not be a foreign concept to the West because it's something that previously the West was clearly engaged in. And so yes, there is echoes of history repeating itself. I don't think there's any other way to look at it. I think it's a complicated dynamic because sometimes people say, well, why is the West not? Why is it not the United States that's in the DRC and getting the cobalt? And I think that's because it's been tough for the United states to find its footing. What China has done is increasingly, and then we did another documentary about this. It's online. It's called Tinder Box Belt and Road, China and the Balkans. And what we increasingly see is in these non-democracies or faulty democracies that has something that China's interested in. China's willing to show up and basically put a lot of money on the table and not ask a whole lot of questions. And if the West, doesn't wanna play that game, whatever they're offering isn't necessarily as attractive. And that's a complication that we see again and again around the world and one, the United States and Europe and the World Bank and Western institutions that often require a lot of background study and open tenders for contracts and democracy caveats and transparency. China's not asking for any of that, as David Dollar, a scholar, said in the prior film, if the World Bank says they're going to build you a road, it's going to be a 10-year process, and we'll see what happens. If China says they'll build you a road a year later, you'll have a road.Andrew Keen: But then the question sound becomes, who owns the road?Samuel George: So let's take the Democratic Republic of the Congo, another great option. China has been building a lot of roads there, and this is obviously beneficial to a country that has very limited infrastructure. It's not just to say everything that China is doing is bad. China is a very large and economically powerful country. It should be contributing to global infrastructure. If it has the ability to finance that, wonderful. We all know Africa, certain African countries can really benefit from improved infrastructure. But where do those roads go? Well, those roads just happen to conveniently connect to these key mineral deposits where China overwhelmingly owns the interest and the minerals.Andrew Keen: That's a bit of a coincidence, isn't it?Samuel George: Well, exactly. And I mean, that's the way it's going. So that's what they'll come to the table. They'll put money on the table, they'll say, we'll get you a road. And, you know, what a coincidence that roads going right by the cobalt mine run by China. That's debatable. If you're from the African perspective, you could say, look, we got a road, and we needed that road. And it could also be that there's a lot of money disappearing in other places. But, you know that that's a different question.Andrew Keen: One of the things I liked about Lithium Rising, the race for critical minerals, your new documentary, is it doesn't pull its punches. Certainly not when it comes to the Chinese. You have some remarkable footage from Africa, but also it doesn't pull its punches in Latin America, or indeed in the United States itself, where cobalt has been discovered and it's the indigenous peoples of some of the regions where cobalt, sorry, where lithium has been discovered, where the African versus Chinese scenario is being played out. So whether it's Bolivia or the western parts of the United States or Congo, the script is pretty similar, isn't it?Samuel George: Yeah, you certainly see themes in the film echoed repeatedly. You mentioned what was the Thacker Pass lithium mine that's being built in northern Nevada. So people say, look, we need lithium. The United States needs lithium. Here's the interesting thing about critical minerals. These are not rare earth minerals. They're actually not that rare. They're in a lot of places and it turns out there's a massive lithium deposit in Nevada. Unfortunately, it's right next to a Native American reservation. This is an area that this tribe has been kind of herded onto after years, centuries of oppression. But the way the documentary tries to investigate it, it is not a clear-cut story of good guy and bad guy, rather it's a very complicated situation, and in that specific case what you have is a tribe that's divided, because there's some people that say, look, this is our land, this is a sacred site, and this is going to be pollution, but then you have a whole other section of the tribe that says we are very poor and this is an opportunity for jobs such that we won't have to leave our area, that we can stay here and work. And these kind of entangled complications we see repeated over and over again. Cobalt is another great example. So there's some people out there that are saying, well, we can make a battery without cobalt. And that's not because they can make a better battery. It's because they want to avoid the Democratic Republic of the Congo. But that cobalt is providing a rare job opportunity. And we can debate the quality of the job, but for the people that are working it, as they say in my film, they say, look, if we could do something else, we would do it. But this is all there is. So if you deprive them of that, the situation gets even worse. And that something we see in Northern Chile. We see it in Nevada. We see in Africa. We see it in Indonesia. What the film does is it raises these moral questions that are incredibly important to talk about. And it sort of begs the question of, not only what's the answer, but who has the right to answer this? I mean, who has right to speak on behalf of the 10 communities that are being destroyed in Northern Chile?Andrew Keen: I have to admit, I thought you did a very good job in the film giving everybody a voice, but my sympathy when it came to the Nevada case was with the younger people who wanted to bring wealth and development into the community rather than some of the more elderly members who were somehow anti-development, anti-investment, anti mining in every sense. I don't see how that benefits, but certainly not their children or the children of their children.Samuel George: I guess the fundamental question there is how bad is that mine going to be for the local environment? And I think that's something that remains to be seen. And one of the major challenges with this broader idea of are we going to greener by transitioning to EVs? And please understand I don't have an opinion of that. I do think anywhere you're doing mining, you're going to have immediate consequences. The transition would have to get big enough that the external the externalities, the positive benefits outweigh that kind of local negativity. And we could get there, but it's also very difficult to imagine massive mining projects anywhere in the world that don't impact the local population. And again, when we pick up our iPhone or when we get in our electric vehicle, we're not necessarily thinking of those 10 villages in the Atacama Desert in Chile.Andrew Keen: Yeah, and I've been up to the Atacama's, perhaps the most beautiful part in the world I've ever seen. It's nice. I saw the tourist side of it, so I didn't see the mining. But I take your point. There is one, perhaps, the most positive section of the film. You went to France. I think it was Calais, you took your camera. And it seems as if the French are pioneering a more innovative development of batteries which benefit the local community but also protect them environmentally. What did you see in northern France?Samuel George: Point, and that gets back to this extractive cycle that we've seen before. Okay, so northern France, this is a story a lot of us will know well because it's similar to what we've see in the Rust Belt in the United States. This is an industrial zone, historically, that faced significant deindustrialization in recent decades and now has massive problems with unemployment and lack of job opportunities, as one of the guys says in the film. Nothing's open here anymore except for that cafe over there and that's just because it has gambling guy. I couldn't have said it any better. This EV transition is offering an opportunity to bring back industrial jobs to whether it's Northern France or the United States of America. So that is an opportunity for people to have these more advanced battery-oriented jobs. So that could be building the battery itself. That could be an auto manufacturing plant where you're making EV electric vehicles. So there is job creation that's happening. And that's further along the development stage and kind of higher level jobs. And we meet students in France that are saying, look, this is an opportunity for a career. We see a long-term opportunity for work here. So we're really studying batteries and that's for university students. That's for people maybe 10, 15 years older to kind of go back to school and learn some skills related to batteries. So there is job creation to that. And you might, you may be getting ready to get to this, but where the real silver lining I think comes after that, where we go back to Georgia in the United States and visit a battery recycling plant.Andrew Keen: Right, yeah, those two sections in the movie kind of go together in a sense.Samuel George: Right, they do. And that is, I think, the silver lining here is that these batteries that we use in all of these appliances and devices and gadgets can be recycled in such a way that the cobalt, the lithium, the nickel can be extracted. And it itself hasn't degraded. It's sort of funny for us to think about, because we buy a phone. And three years later, the battery is half as good as it used to be and we figure well, materials in it must be degrading. They're not. The battery is degrading, the materials are fine. So then the idea is if we can get enough of this in the United States, if we can get old phones and old car batteries and old laptops that we can pull those minerals out, maybe we can have a closed loop, which is sort of a way of saying we won't need those mines anymore. We won't have to dig it up. We don't need to compete with China for access to from Bolivia or Chile because we'll have that lithium here. And yes, that's a silver lining, but there's challenges there. The two key challenges your viewers should be aware of is one, it's all about costs and they've proven that they can recycle these materials, but can they do it in a way that's cheaper than importing new lithium? And that's what these different companies are racing to find a way to say, look, we can do this at a way that's cost effective. Then even if you get through that challenge, a second one is just to have the sheer amount of the materials to close that loop, to have enough in the United States already, they estimate we're decades away from that. So those are the two key challenges to the silver lining of recycling, but it is possible. It can be done and they're doing it.Andrew Keen: We haven't talked about the T word, Sam. It's on everyone's lips these days, tariffs. How does this play out? I mean, especially given this growing explicit, aggressive trade war between the United States and China, particularly when it comes to production of iPhones and other battery-driven products. Right. Is tariffs, I mean, you film this really before Trump 2-0, in which tariffs were less central, but is tariffs going to change everything?Samuel George: I mean, this is just like so many other things, an incredibly globalized ecosystem and tariffs. And who even knows by the time this comes out, whatever we think we understand about the new tariff scenario could be completely outdated.Andrew Keen: Guaranteed. I mean, we are talking on Wednesday, April the 9th. This will go out in a few days time. But no doubt by that time, tariffs will have changed dramatically. They already have as we speak.Samuel George: Here's the bottom line, and this is part of the reason the story is so important and so timely, and we haven't even talked about this yet, but it's so critical. Okay, just like oil, you can't just dig oil out of the ground and put it in the car. It's got to be refined. Lithium, nickel, cobalt, it's got be refined as well. And the overwhelming majority of that refining occurs in China. So even your success story like France, where they're building batteries, they still need to import the refined critical minerals from China. So that is a massive vulnerability. And that's part of where this real fear that you see in Washington or Brussels is coming from. You know, and they got their first little taste of it during the COVID supply chain meltdown, but say in the event where China decided that they weren't gonna export any more of this refined material it would be disastrous for people relying on lithium devices, which by the way, is also the military. Increasingly, the military is using lithium battery powered devices. So that's why there's this urgency that we need to get this on shore. We need to this supply chain here. The problem is that's not happening yet. And okay, so you can slap these tariffs on and that's going to make this stuff much more expensive, but that's not going to automatically create a critical mineral refining capacity in the United States of America. So that needs to be built. So you can understand the desire to get this back here. And by the way, the only reason we're not all driving Chinese made electric vehicles is because of tariffs. The Chinese have really, really caught up in terms of high quality electric vehicles at excellent prices. Now, the prices were always good. What's surprising people recently is the quality is there, but they've basically been tariffed out of the United States. And actually the Biden administration was in part behind that. And it was sort of this tension because on the one hand, they were saying, we want a green revolution, we want to green revolution. But on the other hand, they were seeing these quality Chinese electric vehicles. We're not gonna let you bring them in. But yeah, so I mean, I think the ultimate goal, you can understand why a country that's convinced that it's in a long term competition with China would say we can't rely on Chinese refined materials. Slapping a tariff on it isn't any sort of comprehensive strategy and to me it almost seems like you're putting the horse before the cart because we're not really in a place yet where we can say we no longer need China to power our iPhone.Andrew Keen: And one of the nice things about your movie is it features miners, ordinary people living on the land whose lives are dramatically impacted by this. So one would imagine that some of the people you interviewed in Bolivia or Atacama or in Africa or even in Georgia and certainly in Nevada, they're going to be dramatically impacted by the tariffs. These are not just abstract ideas that have a real impact on people's lives.Samuel George: Absolutely. I mean, for decades now, we've built an economic system that's based on globalization. And it's certainly true that that's cost a lot of jobs in the United States. It's also true that there's a lot jobs and companies that have been built around global trade. And this is one of them. And you're talking about significant disruption if your global supply chains, as we've seen before, again, in the COVID crisis when the supply chains fall apart or when the margins, which are already pretty slim to begin with, start to degrade, yeah, it's a major problem.Andrew Keen: Poorly paid in the first place, so...Samuel George: For the most part, yeah.Andrew Keen: Well, we're not talking about dinging Elon Musk. Tell us a little bit, Sam, about how you made this movie. You are a defiantly independent filmmaker, one of the more impressive that I know. You literally carry two large cameras around the world. You don't have a team, you don't have an audio guy, you don't ever sound guy. You do it all on your own. It's quite impressive. Been you shlep these cameras to Latin America, to Southeast Asia, obviously all around America. You commissioned work in Africa. How did you make this film? It's quite an impressive endeavor.Samuel George: Well, first of all, I really appreciate your kind words, but I can't completely accept this idea that I do it all alone. You know, I'm speaking to you now from the Bertelsmann Foundation. I'm the director of Bertelsman Foundation documentaries. And we've just had this fantastic support here and this idea that we can go to the front line and get these stories. And I would encourage people to check out Bertelsmen Foundation documentation.Andrew Keen: And we should have a special shout out to your boss, my friend, Irene Brahm, who runs the BuzzFeed Foundation of North America, who's been right from the beginning, a champion of video making.Samuel George: Oh, absolutely. I mean, Irene Brahm has been a visionary in terms of, you know, something I think that we align on is you take these incredibly interesting issues and somehow analysts manage to make them extraordinarily boring. And Irene had this vision that maybe it doesn't have to be that way.Andrew Keen: She's blushing now as she's watching this, but I don't mean to make you blush, Sam, but these are pretty independent movies. You went around the world, you've done it before, you did it in the Serbian movie too. You're carrying these cameras around, you're doing all your own work, it's quite an achievement.Samuel George: Well, again, I'm very, very thankful for the Bertelsmann Foundation. I think a lot of times, sometimes people, when they hear a foundation or something is behind something, they assume that somebody's got an ax to grind, and that's really not the case here. The Bertelsman Foundation is very supportive of just investigating these key issues, and let's have an honest conversation about it. And maybe it's a cop-out, but in my work, I often don't try to provide a solution.Andrew Keen: Have you had, when we did our event in D.C., you had a woman, a Chinese-born woman who's an expert on this. I don't think she's particularly welcome back on the mainland now. Has there been a Chinese response? Because I would say it's an anti-Chinese movie, but it's not particularly sympathetic or friendly towards China.Samuel George: And I can answer that question because it was the exact same issue we ran into when we filmed Tinder Box Belt and Road, which was again about Chinese investment in the Balkans. And your answer is has there been a Chinese reaction and no sort of official reaction. We always have people sort of from the embassy or various affiliated organizations that like to come to the events when we screen it. And they're very welcome to. But here's a point that I want to get across. Chinese officials and people related to China on these issues are generally uniformly unwilling to participate. And I think that's a poor decision on their part because I think there's a lot they could say to defend themselves. They could say, hey, you guys do this too. They could say, we're providing infrastructure to critical parts of the world. They could said, hey we're way ahead of you guys, but it's not because we did anything wrong. We just saw this was important before you did and built the network. There are many ways they could defend themselves. But rather than do that, they're extremely tight-lipped about what they're doing. And that can, if you're not, and we try our best, you know, we have certain experts from China that when they'll talk, we'll interview them. But that kind of tight-lip approach almost makes it seem like something even more suspicious is happening. Cause you just have to guess what the mindset must be cause they won't explain themselves. And I think Chinese representatives could do far more and it's not just about you know my documentary I understand they have bigger fish to fry but I feel like they fry the fish the same way when they're dealing with bigger entities I think it's to their detriment that they're not more open in engaging a global conversation because look China is gonna be an incredibly impactful part of world dynamics moving forward and they need to be, they need to engage on what they're doing. I think, and I do think they have a story they can tell to defend themselves, and it's unfortunate that they very much don't do it.Andrew Keen: In our DC event, you also had a woman who'd worked within the Biden administration. Has there been a big shift between Biden policy on recycling, recyclable energy and Trump 2.0? It's still the early days of the new administration.Samuel George: Right. And we're trying to get a grip on that of what the difference is going to be. I can tell you this, the Biden approach was very much the historic approach of the United States of America, which is to try to go to a country like Congo and say, look, we're not going to give you money without transparency. We're not gonna give you this big, you know, beautiful deal. We're going to the cheapest to build this or the cheapest build that. But what we can compete with you is on quality and sustainability and improved work conditions. This used to be the United States pitch. And as we've seen in places like Serbia, that's not always the greatest pitch in the world. Oftentimes these countries are more interested in the money without questions being asked. But the United states under the Biden administration tried to compete on quality. Now we will have to see if that continues with the Trump administration, if that continuous to be their pitch. What we've see in the early days is this sort of hardball tactic. I mean, what else can you refer to what's happening with Ukraine, where they say, look, if you want continued military support, we want those minerals. And other countries say, well, maybe that could work for us too. I mean that's sort of, as I understand it, the DRC, which is under, you know, there's new competition there for power that the existing government is saying, hey, United States, if you could please help us, we'll be sure to give you this heaping of minerals. We can say this, the new administration does seem to be taking the need for critical minerals seriously, which I think was an open question because we see so much of the kind of green environmentalism being rolled back. It does still seem to be a priority with the new administration and there does seem to be clarity that the United States is going to have to improve its position regarding these minerals.Andrew Keen: Yeah, I'm guessing Elon Musk sees this as well as anyone, and I'm sure he's quite influential. Finally, Sam, in contrast with a book, which gets distributed and put in bookstores, doing a movie is much more challenging. What's the goal with the movie? You've done a number of launches around the world, screenings in Berlin, Munich, London, Washington D.C. you did run in San Francisco last week. What's the business model, so to speak here? Are you trying to get distribution or do you wanna work with schools or other authorities to show the film?Samuel George: Right, I mean, I appreciate that question. The business model is simple. We just want you to watch. You know, our content is always free. Our films are always free, you can go to bfnadox.org for our catalog. This film is not online yet. You don't need a password, you don't a username, you can just watch our movies, that's what we want. And of course, we're always on the lookout for increased opportunities to spread these. And so we worked on a number of films. We've got PBS to syndicate them nationally. We got one you can check your local listings about a four-month steel workers strike in western Pennsylvania. It's called Local 1196. That just started its national syndication on PBS. So check out for that one. But look, our goal is for folks to watch these. We're looking for the most exposure as we can and we're giving it away for free.Andrew Keen: Just to repeat, if people are interested, that's bfna.docs.org to find more movies. And finally, Sam, for people who are interested perhaps in doing a showing of the film, I know you've worked with a number of universities and interest groups. What would be the best way to approach you.Samuel George: Well, like you say, we're a small team here. You can always feel free to reach out to me. And I don't know if I should pitch my email.Andrew Keen: Yeah, picture email. Give it out. The Chinese will be getting it too. You'll be getting lots of invitations from China probably to show the film.Samuel George: We'd love to come talk about it. That's all we want to do. And we try, but we'd love to talk about it. I think it's fundamental to have that conversation. So the email is just Samuel.George, just as you see it written there, at BFN as in boy, F as in Frank, N as in Nancy, A. Let's make it clearer - Samuel.George@bfna.org. We work with all sorts of organizations on screenings.Andrew Keen: And what about the aspiring filmmakers, as you're the head of documentaries there? Do you work with aspiring documentary filmmakers?Samuel George: Yes, yes, we do often on projects. So if I'm working on a project. So you mentioned that I work by myself, and that is how I learned this industry, you know, is doing it by myself. But increasingly, we're bringing in other skilled people on projects that we're working on. So we don't necessarily outsource entire projects. But we're always looking for opportunities to collaborate. We're looking to bring in talent. And we're looking to make the best products we can on issues that we think are fundamental importance to the Atlantic community. So we love being in touch with filmmakers. We have internship programs. We're open for nonprofit business, I guess you could say.Andrew Keen: Well, that's good stuff. The new movie is called Lithium Rising, The Race for Critical Minerals. I moderated a panel after the North American premiere at the end of February. It's a really interesting, beautifully made film, very compelling. It is only 60 minutes. I strongly advise anyone who has the opportunity to watch it and to contact Sam if they want to put it on their school, a university or other institution. Congratulations Sam on the movie. What's the next project?Samuel George: Next project, we've started working on a project about Southern Louisiana. And in there, we're really looking at the impact of land loss on the bayous and the local shrimpers and crabbers and Cajun community, as well as of course This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit keenon.substack.com/subscribe

Today, Martha, Les, Bishop, and Amy discuss the Democratic Republic of the Congo's plea for a minerals-for-security deal with the United States, as the country faces mounting violence and instability. With $24 trillion worth of natural resources—including critical cobalt reserves—on the line, Rwandan-backed rebel groups have seized key mining hubs in eastern Congo, killing thousands, displacing millions, and igniting regional tensions.What's in it for Washington, and can the Trump administration craft a consistent, credible Africa strategy? As Rwanda and Uganda continue to exploit the chaos and U.S. tariffs hamper global engagement, will this become another case where talk outpaces action? And with American companies reluctant to invest in conflict zones, is the DRC poised to become another lost front in the race for critical minerals?Check out these sources which helped shape our Fellows' conversation: https://www.wsj.com/world/africa/while-war-rages-congos-neighbors-smuggle-out-its-gold-and-mineral-wealth-8582b882?mod=hp_lead_pos8https://www.aljazeera.com/news/2025/3/17/amid-conflict-why-does-the-drc-want-a-minerals-deal-with-trump https://www.bbc.com/news/articles/cp34140qkw0o Check out the answers to these questions and more in this episode of Fault Lines.Follow our experts on Twitter: @amymitchell@bishop@lestermunson@marthamillerdcLike what we're doing here? Be sure to rate, review, and subscribe. And don't forget to follow @masonnatsec on Twitter!We are also on YouTube, and watch today's episode here: https://youtu.be/ixUbjMEunxc Hosted on Acast. See acast.com/privacy for more information.

In January, the Indian government published a new critical minerals strategy that details how the country aims to bolster supply chains necessary for its green energy transition. While the report underscores the importance of developing domestic supplies of lithium and other transition resources, it also calls for closer international partnerships, particularly with mineral-rich African nations. India has deep ties in Africa, particularly in eastern and southern countries on the continent, but it is a newcomer to the critical resources sector that is largely dominated by Chinese and European companies. Veda Vaidyanathan, an accomplished China-Africa scholar and an associate fellow at the Centre for Social and Economic Progress in New Delhi, joins Eric & Géraud to explain how India's approach to critical resource mining in Africa is going to look very different from what China is doing. Show Notes: Centre for Social and Economic Progress: India, Africa and Critical Minerals: Towards a Green Energy Partnership by Veda Vaidyanathan Reuters: India exploring critical minerals in Zambia, Congo and Australia, official says by Neha Arora JOIN THE DISCUSSION: X: @ChinaGSProject | @eric_olander | @christiangeraud Facebook: www.facebook.com/ChinaAfricaProject YouTube: www.youtube.com/@ChinaGlobalSouth Now on Bluesky! Follow CGSP at @chinagsproject.bsky.social FOLLOW CGSP IN FRENCH AND ARABIC: Français: www.projetafriquechine.com | @AfrikChine Arabic: عربي: www.alsin-alsharqalawsat.com | @SinSharqAwsat JOIN US ON PATREON! Become a CGSP Patreon member and get all sorts of cool stuff, including our Week in Review report, an invitation to join monthly Zoom calls with Eric & Cobus, and even an awesome new CGSP Podcast mug! www.patreon.com/chinaglobalsouth

It's that time of year again and for the first time in a long time Rose, Cobalt, and Victor (and their families) are taking an actual vaction. To a region with some very strange... pokemon? The jury's out, but the fun is in! See you there, in this episode of Pokemon World Tour United!Patreon: patreon.com/heyjakeandjoshEmail: PWTpodcast@gmail.comTwitter: @PWTpodcastShop: teepublic.com/user/heyjakeandjosh

That's right, it's April Fool's Day which mean we're venturing back to the timeline where Rose and Cobalt never went on their Pokemon journey. But even this alternate version of the world, some things never change Patreon: patreon.com/heyjakeandjoshEmail: PWTpodcast@gmail.comTwitter: @PWTpodcastShop: teepublic.com/user/heyjakeandjosh

Tas, ka Donalda Trampa otrās prezidentūras laiks nebūs vienkāršs, bija zināms jau pirms vēlēšanām, tomēr tādus Savienoto Valstu ārpolitikas un arī iekšpolitiskos manevrus, kādus kā redzam tagad un to ietekmi, šķiet, prognozēja retais. Kāda nākotne gaida Rietumus, vai esam demokrātijas globāla sabrukuma priekšā? Krustpunktā analizē Nacionālās aizsardzības akadēmijas pētnieks, vēsturnieks Valdis Kuzmins, Rīgas Stradiņa universitātes asociētais profesors Vents Sīlis, Latvijas Universitātes Ekonomikas un sociālo zinātņu fakultātes Socioloģijas nodaļas asociētā profesore Baiba Bela un Latvijas Universitātes lektors, zvērināts advokāts, biroja "Cobalt" partneris Lauris Liepa. Pasaulē viss plūst un mainās, un kurš gan to vēl labāk zina kā vēstures pētnieki? Bet tajā pašā laikā šķiet, ka cilvēkam nav viegli piemēroties visām pārmaiņām. Pēdējā laikā Krustpunktā studijā viesi bieži atkārto domu, kas mums pārņem arvien vairāk, proti, ka mēs un vispār visa Rietumu pasaule tagad saskaras ar tādiem izaicinājumiem, kas tai sen nav bijuši. Tā gan esam teikuši ne vienā vien krīzē. Kad sākās lielais migrācijas vilnis, tā sacījām. Kad pasauli pārņēma Covid pandēmija, teicām - cik sen tā nav tas bijis. Tad, protams, Krievijas karš visu satricināja vēl vairāk. Tagad, lūkojoties uz Donalda Trampa plosīšanos - solījumiem anektēt Grenlandi, pakļaut Kanādu un vispār padarīt Ameriku savā izpratnē par varenu lielvaru, daudzi patiešām nesaprot, kur mēs virzāmies, kas notiek ar Rietumu demokrātiju, tās lielajām vērtībām? Kā tas izdzīvos, kas jādara, lai tās nepazaudētu? Tajā laikā, kad lielvaras pārņem vēlme savu negausību realizēt, lai cik dārgi tas kādiem arī maksātu, šādi globāli un fundamentāli jautājumi rodas cilvēku prātos, un mēs tos uzdodam savos sociālajos kontos, meklējot atbildes. Jautājums: vai atbildes vispār ir? 

Millions of Zambians along the Kafue River are coming to grips with the devastating environmental impact brought about by a massive acid spill from a Chinese-run copper mine. A tailings dam broke on February 18th, sending 50 million liters of toxic water into the Kafue River, killing fish, wildlife and endangering public health. Sino-Metals, the Chinese mining company, apologized for the accident and said that it is “doing its best” to clean up the mess. Eric, Cobus, and Geraud discuss the political implications of the spill and what's at stake for the Chinese government if the company fails to take care of this environmental tragedy. Plus, the guys also break down a new $1.4 billion Chinese deal to refurbish the TAZARA railway and the prospects of a U.S. critical resource mining deal in the DRC. Show Notes: Associated Press: A river ‘died' overnight in Zambia after an acidic waste spill at a Chinese-owned mine by Richard Kille and Jacob Zimba Carnegie Endowment for International Peace: Can the DRC Leverage U.S.-China Competition Over Critical Minerals for Peace? by Christian-Géraud Neema Bloomberg: China to Invest $1.4 Billion to Upgrade Tanzana-Zambia Rail Line by Matthew Hill X: @christiangeraud I @ChinaGSProject | @eric_olander | @stadenesque Facebook: www.facebook.com/ChinaAfricaProject YouTube: www.youtube.com/@ChinaGlobalSouth Now on Bluesky! Follow CGSP at @chinagsproject.bsky.social FOLLOW CGSP IN FRENCH AND ARABIC: Français: www.projetafriquechine.com | @AfrikChine Arabic: عربي: www.alsin-alsharqalawsat.com | @SinSharqAwsat JOIN US ON PATREON! Become a CGSP Patreon member and get all sorts of cool stuff, including our Week in Review report, an invitation to join monthly Zoom calls with Eric & Cobus, and even an awesome new CGSP Podcast mug! www.patreon.com/chinaglobalsouth

On March 3, 2025, Canadian North Resources (TSXV: CNRI | OTCQX: CNRSF | FSX: EO0) announced the launch of a new metallurgical program at the Ferguson Lake Critical Minerals Project in Nunavut. This follows the successful technical evaluation of bioleaching technology.In this interview, Project Geologist Carl-Philippe Folkesson discusses key details of the metallurgical program, the promising results of the 2024 bioleaching tests, and the development of a mineral processing flowsheet aimed at reducing capital costs.Learn more: https://cnresources.com/2025/03/03/canadian-north-resources-inc-expands-metallurgical-programs-applying-low-carbon-footprint-bioleaching-technology-on-ferguson-lake-ni-cu-co-pge-project/Watch the full YouTube interview here: https://youtu.be/HLcH1aRkQLAAnd follow us to stay updated: https://www.youtube.com/@GlobalOneMedia?sub_confirmation=1

As China restricts bismuth exports, prices have skyrocketed from $6 to over $37 per pound, creating new opportunities for North American suppliers.In this interview, Fortune Minerals (TSX: FT | OTCQB: FTMDF) President & CEO Robin E. Goad discusses the company's strategic position of controlling 12% of global bismuth reserves. Beyond bismuth, Fortune Minerals' NICO project in Canada's Northwest Territories contains multi-million ounces of gold, cobalt, and copper, providing stability against commodity price fluctuations. He also talks about the company's collaboration with Rio Tinto, a grant from the US Department of Defense, and why they're approaching a critical construction decision milestone. Watch the full video to discover how Fortune Minerals has positioned itself at the intersection of critical minerals, clean energy transition, and national security.Learn more about Fortune Minerals and its projects: https://fortuneminerals.com/Watch the full YouTube interview here: https://youtu.be/4wkL3N6WZpUAnd follow us to stay updated: https://www.youtube.com/@GlobalOneMedia?sub_confirmation=1

In our March 2025 Recharge podcast, co-presenters Matt Fernley (Battery Materials Review) and Cormac O'Laoire (Electrios Energy) discuss some of the month's key talking point in the battery industry, including: EV sales - moving parts, drivers, future trends, the growth of EREVs, BYD vs Tesla Battery project delays and cancellations in the US and Europe and where that leaves the industry. What do battery developers need to do to survive? The impact of geopolitics and tariffs on the battery sector, the DLE tech ban and the impact on the wider autos industry in North America China update and the impact of US Dept of Homeland Security looking to restrict procurement from six Chinese battery makers The changing investment environment for the industry BESS installation growth; new techs and innovation DRC cobalt export ban and thoughts on prices

Send us a textWelcome back TOT fans!On the show this week we tackle the week to forget for Asher with his Liverpool team crashing out of Champions League and losing the Carabao Cup Final. How quickly things can turn from "bring on Real Madrid and the sliverware," to "are we going to bottle the league!?"Dale, Casey, and Cobalt discuss the snoozefest that was Arsenal v Chelsea, Ryan just hopes that Ange keeps his job through to next season, we discuss games we didn't bother to watch, and we briefly get into the upcoming International Break, which we all need at the moment, quite frankly. This is our last break until the end of the season when one of our teams will be crowned at Premier League champions. Will it be a sputtering Liverpool, or will it be the Spursiest bottle of all time and Arsenal will nick it at the post? Thank you for listening! Please like, share, subscribe, download, and as always, we love you!

In this latest OIES podcast Michal Meidan talks to Bryan Bille from Benchmark Mineral Intelligence about Europe's need for cobalt as part of its efforts to achieve carbon neutrality and grow its lithium-ion battery industry, and the role of the DRC within that. Michal and Bryan talk about Europe's growing demand for cobalt, how policies […] The post OIES Podcast – Europe's cobalt supply security: what is the role of the Democratic Republic of Congo and of China? appeared first on Oxford Institute for Energy Studies.

Welcome back to another ZZP Power Hour Podcast! Let's face it, our beloved Cobalts are getting older. Today, join the crew as we talk about what it was like modifying these cars back in the day versus now, with all the technology and knowledge we've gained. GOFASTNOTBROKE

Alsym is developing a new generation of high-performance, low-cost, non-flammable batteries to help enable a zero-carbon electrified future for all. Using readily-available materials that are inherently non-toxic, Alsym's breakthrough battery technology is an alternative to lithium-ion at less than half the cost, with the same performance and with no lithium or cobalt.Mukesh Chatter is CEO, president and co-founder of Alsym Energy. Previously, he co-founded Nexabit Networks and was CEO until its acquisition by Lucent Technologies, and co-managed NeoNet Capital LLC. He was named to Red Herring Magazine's Top 10 Entrepreneurs in 1999, and Rensselaer Entrepreneur of the Year in 2001. Mukesh received his Master's degree in Computer and Systems Engineering from Rensselaer Polytechnic Institute.--On the personal side:Chatter was inspired to launch Alsym Energy after his mother's passing, leading him to focus on solving problems that impact at least a billion people. His goal was to create an energy storage solution that could bring electricity access to underserved communities while also addressing global industrial decarbonization needs.In our conversation, he also stressed the importance of thinking non-linearly, embracing pivots, and prioritizing a strong team culture where leadership leads by example. He highlighted meditation and intentional pauses between tasks as key strategies for maintaining focus and resilience in high-stress environments.--

06 Ram AFM Misfire how to fix using oil system cleaner? 11 Suburban Hard Cold Start fix. 02 K3500 Brake light fix. 20 Ram AFM DOD Disable? 71 GMC Sprint El Camino Stored over a decade getting it running. 12 Nissan Altima bad fuel mileage fix. 97 F250 Trans front seal leaking fix 2010 Kia Soul Belt squeal? 97 Tahoe cold start clatter fix? 06 Mustang hard to press shifter button 18 Kia Forte leaks water on floorboard how to leak test? 06 Ram and old tractors when to change oil when used very little? 08 Cobalt low power uphill When to change oil on new Duramax 3.0 diesel? Subaru random mis due to carbon on valves. 2013 GMC Sierra disabling AFM the right way or not?

Send us a textWelcome back, TOT fans! We're back this week a man down after Cobalt was given two yellows on his Stag. Actually, he was feeling poorly and said his, "voice was gone." A likely story!Harry Kane counts us in as we get into all the action from the weekend, and talk a little about the Champions League and tell a few stories from over the weekend. Should United have won? Why would you captain Palmer over SALAH!?! Who got the better deal in a 2-2, Spurs or Bournemouth? How many cocks out of 5 is Darwin Nunez? Casey knows, and he will clue us in. We get into all this and discuss a few of the games coming up over the weekend, and who has the nicest path to a Champions League semi-final. As always, thank you for listening! Please like, share, subscribe, download, and as always, we love you!

About this episode: At the bottom of the world's oceans lie valuable deposits of cobalt, manganese, and other minerals. In today's episode: a deep dive on deep-sea mining, the environmental impacts, and how the world might approach regulating mining in areas that technically belong to everyone. Guests: Andrew Thaler is a deep-sea ecologist, conservation technologist, and an ocean educator. Host: Lindsay Smith Rogers, MA, is the producer of the Public Health On Call podcast, an editor for Expert Insights, and the director of content strategy for the Johns Hopkins Bloomberg School of Public Health. Show links and related content: @‌drandrewthaler—Bluesky Deep-sea Mining: What went down in 2024?—Southern Fried Science Withdrawal Agreement Could Signal Shift in Deep Sea Mining Activity—Forbes Transcript information: Looking for episode transcripts? Open our podcast on the Apple Podcasts app (desktop or mobile) or the Spotify mobile app to access an auto-generated transcript of any episode. Closed captioning is also available for every episode on our YouTube channel. Contact us: Have a question about something you heard? Looking for a transcript? Want to suggest a topic or guest? Contact us via email or visit our website. Follow us: @‌PublicHealthPod on Bluesky @‌JohnsHopkinsSPH on Instagram @‌JohnsHopkinsSPH on Facebook @‌PublicHealthOnCall on YouTube Here's our RSS feed Note: These podcasts are a conversation between the participants, and do not represent the position of Johns Hopkins University.

In this Season 9 Episode 16 of Milkcrates & Microphones, we are joined in-studio by 2 very special guests—Cobalt45 & Future Sphire. Cobalt45 & Future Sphire tear the house down with a very special live performance. We also dive into a number of different topics including Kendrick Lamar winning 5 Grammys, their brand-new album—Daytons and Dickies, Cobalt's collab EP with Canadian producer—TPWTR, Future Sphire's new album—Earth to Mars: 1,000 Years, his top 3 favorite producers, DJ Shadow, Cobalt's recent show with the legendary KRS-One, a special upcoming album release party, Sasquatch, plus so much more. We also bring you your favorite Milk&Mics segments like “This Week in Hip Hop” & “Song Picks of the (Motha Fuckin') Week”. Enjoy. https://www.danked.com https://www.youtube.com/@RichSphire-frfr Follow us on Youtube @ https://www.youtube.com/channel/UC5Jmk_m0_zhxjjYRHWDtvjQ on Instagram @ https://www.instagram.com/milkandmics/?hl=en and Facebook @ https://www.facebook.com/milkandmics/

Send us a textWelcome back TOT fans! This week we are coming to you live from funky Chinatown, where Millwall are kung fu fighting!Just kidding, it's still Asher's basement, and Millwall's keeper is just booting people in the head.We get into all the FA Cup action from the weekend, including red card madness, some incredible games going into PKs and extra time, who United should sign, Gordon missing a final against his new club, and who we think (barring City) will win, and who we HOPE win the Cup. After that we get into the PL action, where Asher admits the league is done but for the shouting, Dale and Cobalt discuss Arteta Out, and Ryan is please with Spurs even in a losing effort. We discuss the games next weekend and which ones will be worth watching, and wrap it up with some What Is That Is He Said about a certain ref who is not very well liked these days. Almost makes you pine for David Coote... almost. Thank you so much for listening! Please like, subscribe, download, share, and as always, we love you!

In this episode, Virginia and Joey O'Connor discuss the pressing issues of human trafficking and the humanitarian crisis in the Congo, as highlighted in Joey's book, The Cobalt Curse. They explore the historical context of the Congo's struggles, the current situation of violence and exploitation, and the importance of raising awareness and supporting humanitarian efforts. The conversation delves into the themes of grief, storytelling, and the impact of modern technology on human rights, emphasizing the need for a clean chain of title in mineral sourcing. Through personal anecdotes and insights, they aim to inspire listeners to engage with these critical issues and advocate for change.Books mentioned in this episode: The Cobalt Curse BookAmong KingsWhere to Find VirginiaWebsiteInstagramFacebookLinkedInDonateWhere to Find Joey O'ConnorWebsiteOnline Bookstore

U.S. and European leaders often talk about the importance of building China-free supply chains for transition minerals and other critical resources. While, for a lot of people, that may resonate among their constituents at home, the reality is that it's not even remotely possible — at least for the foreseeable future. China has spent the better part of two decades building an insurmountable lead in financing, extracting, and processing these resources. Using a combination of state-backed companies and foreign financial institutions, the Chinese are the pacesetters in this industry, with their rivals left far behind. A first-of-its-kind report from the development research lab AidData at William & Mary College in the United States reveals the stunning breadth of China's global mining strategy that spans 19 countries around the world. Two of the report's authors, Brooke Escobar and Katherine Walsh, both from AidData's Chinese Development Finance Program, join Eric & Cobus to explain why China is now so far ahead of its competitors in this critical competition. SHOW NOTES: AidData: Power Playbook: Beijing's Bid to Secure Overseas Transition Minerals JOIN THE DISCUSSION: X: @ChinaGSProject | @eric_olander | @christiangeraud Facebook: www.facebook.com/ChinaAfricaProject YouTube: www.youtube.com/@ChinaGlobalSouth Now on Bluesky! Follow CGSP at @chinagsproject.bsky.social FOLLOW CGSP IN FRENCH AND ARABIC: Français: www.projetafriquechine.com | @AfrikChine Arabic: عربي: www.alsin-alsharqalawsat.com | @SinSharqAwsat JOIN US ON PATREON! Become a CGSP Patreon member and get all sorts of cool stuff, including our Week in Review report, an invitation to join monthly Zoom calls with Eric & Cobus, and even an awesome new CGSP Podcast mug! www.patreon.com/chinaglobalsouth  

Send us a textWelcome back, TOT fans! Well, well, well, what a week it was! Seventh heaven for Notts Forrest, Liverpool pop the Cherries, United are in the mud, Spurs pull out of their dive, and five star Arsenal humble City.This week, Dale, Hemal, and Asher start us off with Ryan and Casey coming in hot from Cookeville later in the show. Dale hustles the boys off the games "nobody cares about" as Asher and Cobalt discuss the inconsistency in teams in the PL. Ryan comes in and we talk about Spurs getting a much needed win, and Casey has to excuse himself with a cheek clencher. Then we get to the game everyone wants to discuss, the Arsenal dismantling of City. It's the win Arsenal needed, and City have never looked worse under Pep. It's a conundrum, but we attempt to unpack it. No show next Monday, as it's Super Bowl Sunday and we will all be watching with a gleam in our eyes, and prop bets in our hearts. The next PL game is the 2/12 make up fixture for Liverpool and Everton, and then the next Game Week is 2/14-16, so we'll be back 2/17 with a recap of that action.Thank you for listening! Please like, share, subscribe, download, telephone, tell a friend, and as always, WE LOVE YOU.

Report: ⁠⁠https://thedfirreport.com/2025/01/27/cobalt-strike-and-a-pair-of-socks-lead-to-lockbit-ransomware/ Contact Us: ⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thedfirreport.com/contact/⁠⁠⁠⁠⁠⁠⁠⁠⁠ Services: ⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thedfirreport.com/services/⁠⁠⁠⁠⁠

One last Gold sponsor slot is available for the AI Engineer Summit in NYC. Our last round of invites is going out soon - apply here - If you are building AI agents or AI eng teams, this will be the single highest-signal conference of the year for you!While the world melts down over DeepSeek, few are talking about the OTHER notable group of former hedge fund traders who pivoted into AI and built a remarkably profitable consumer AI business with a tiny team with incredibly cracked engineering team — Chai Research. In short order they have:* Started a Chat AI company well before Noam Shazeer started Character AI, and outlasted his departure.* Crossed 1m DAU in 2.5 years - William updates us on the pod that they've hit 1.4m DAU now, another +40% from a few months ago. Revenue crossed >$22m. * Launched the Chaiverse model crowdsourcing platform - taking 3-4 week A/B testing cycles down to 3-4 hours, and deploying >100 models a week.While they're not paying million dollar salaries, you can tell they're doing pretty well for an 11 person startup:The Chai Recipe: Building infra for rapid evalsRemember how the central thesis of LMarena (formerly LMsys) is that the only comprehensive way to evaluate LLMs is to let users try them out and pick winners?At the core of Chai is a mobile app that looks like Character AI, but is actually the largest LLM A/B testing arena in the world, specialized on retaining chat users for Chai's usecases (therapy, assistant, roleplay, etc). It's basically what LMArena would be if taken very, very seriously at one company (with $1m in prizes to boot):Chai publishes occasional research on how they think about this, including talks at their Palo Alto office:William expands upon this in today's podcast (34 mins in):Fundamentally, the way I would describe it is when you're building anything in life, you need to be able to evaluate it. And through evaluation, you can iterate, we can look at benchmarks, and we can say the issues with benchmarks and why they may not generalize as well as one would hope in the challenges of working with them. But something that works incredibly well is getting feedback from humans. And so we built this thing where anyone can submit a model to our developer backend, and it gets put in front of 5000 users, and the users can rate it. And we can then have a really accurate ranking of like which model, or users finding more engaging or more entertaining. And it gets, you know, it's at this point now, where every day we're able to, I mean, we evaluate between 20 and 50 models, LLMs, every single day, right. So even though we've got only got a team of, say, five AI researchers, they're able to iterate a huge quantity of LLMs, right. So our team ships, let's just say minimum 100 LLMs a week is what we're able to iterate through. Now, before that moment in time, we might iterate through three a week, we might, you know, there was a time when even doing like five a month was a challenge, right? By being able to change the feedback loops to the point where it's not, let's launch these three models, let's do an A-B test, let's assign, let's do different cohorts, let's wait 30 days to see what the day 30 retention is, which is the kind of the, if you're doing an app, that's like A-B testing 101 would be, do a 30-day retention test, assign different treatments to different cohorts and come back in 30 days. So that's insanely slow. That's just, it's too slow. And so we were able to get that 30-day feedback loop all the way down to something like three hours.In Crowdsourcing the leap to Ten Trillion-Parameter AGI, William describes Chai's routing as a recommender system, which makes a lot more sense to us than previous pitches for model routing startups:William is notably counter-consensus in a lot of his AI product principles:* No streaming: Chats appear all at once to allow rejection sampling* No voice: Chai actually beat Character AI to introducing voice - but removed it after finding that it was far from a killer feature.* Blending: “Something that we love to do at Chai is blending, which is, you know, it's the simplest way to think about it is you're going to end up, and you're going to pretty quickly see you've got one model that's really smart, one model that's really funny. How do you get the user an experience that is both smart and funny? Well, just 50% of the requests, you can serve them the smart model, 50% of the requests, you serve them the funny model.” (that's it!)But chief above all is the recommender system.We also referenced Exa CEO Will Bryk's concept of SuperKnowlege:Full Video versionOn YouTube. please like and subscribe!Timestamps* 00:00:04 Introductions and background of William Beauchamp* 00:01:19 Origin story of Chai AI* 00:04:40 Transition from finance to AI* 00:11:36 Initial product development and idea maze for Chai* 00:16:29 User psychology and engagement with AI companions* 00:20:00 Origin of the Chai name* 00:22:01 Comparison with Character AI and funding challenges* 00:25:59 Chai's growth and user numbers* 00:34:53 Key inflection points in Chai's growth* 00:42:10 Multi-modality in AI companions and focus on user-generated content* 00:46:49 Chaiverse developer platform and model evaluation* 00:51:58 Views on AGI and the nature of AI intelligence* 00:57:14 Evaluation methods and human feedback in AI development* 01:02:01 Content creation and user experience in Chai* 01:04:49 Chai Grant program and company culture* 01:07:20 Inference optimization and compute costs* 01:09:37 Rejection sampling and reward models in AI generation* 01:11:48 Closing thoughts and recruitmentTranscriptAlessio [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel, and today we're in the Chai AI office with my usual co-host, Swyx.swyx [00:00:14]: Hey, thanks for having us. It's rare that we get to get out of the office, so thanks for inviting us to your home. We're in the office of Chai with William Beauchamp. Yeah, that's right. You're founder of Chai AI, but previously, I think you're concurrently also running your fund?William [00:00:29]: Yep, so I was simultaneously running an algorithmic trading company, but I fortunately was able to kind of exit from that, I think just in Q3 last year. Yeah, congrats. Yeah, thanks.swyx [00:00:43]: So Chai has always been on my radar because, well, first of all, you do a lot of advertising, I guess, in the Bay Area, so it's working. Yep. And second of all, the reason I reached out to a mutual friend, Joyce, was because I'm just generally interested in the... ...consumer AI space, chat platforms in general. I think there's a lot of inference insights that we can get from that, as well as human psychology insights, kind of a weird blend of the two. And we also share a bit of a history as former finance people crossing over. I guess we can just kind of start it off with the origin story of Chai.William [00:01:19]: Why decide working on a consumer AI platform rather than B2B SaaS? So just quickly touching on the background in finance. Sure. Originally, I'm from... I'm from the UK, born in London. And I was fortunate enough to go study economics at Cambridge. And I graduated in 2012. And at that time, everyone in the UK and everyone on my course, HFT, quant trading was really the big thing. It was like the big wave that was happening. So there was a lot of opportunity in that space. And throughout college, I'd sort of played poker. So I'd, you know, I dabbled as a professional poker player. And I was able to accumulate this sort of, you know, say $100,000 through playing poker. And at the time, as my friends would go work at companies like ChangeStreet or Citadel, I kind of did the maths. And I just thought, well, maybe if I traded my own capital, I'd probably come out ahead. I'd make more money than just going to work at ChangeStreet.swyx [00:02:20]: With 100k base as capital?William [00:02:22]: Yes, yes. That's not a lot. Well, it depends what strategies you're doing. And, you know, there is an advantage. There's an advantage to being small, right? Because there are, if you have a 10... Strategies that don't work in size. Exactly, exactly. So if you have a fund of $10 million, if you find a little anomaly in the market that you might be able to make 100k a year from, that's a 1% return on your 10 million fund. If your fund is 100k, that's 100% return, right? So being small, in some sense, was an advantage. So started off, and the, taught myself Python, and machine learning was like the big thing as well. Machine learning had really, it was the first, you know, big time machine learning was being used for image recognition, neural networks come out, you get dropout. And, you know, so this, this was the big thing that's going on at the time. So I probably spent my first three years out of Cambridge, just building neural networks, building random forests to try and predict asset prices, right, and then trade that using my own money. And that went well. And, you know, if you if you start something, and it goes well, you You try and hire more people. And the first people that came to mind was the talented people I went to college with. And so I hired some friends. And that went well and hired some more. And eventually, I kind of ran out of friends to hire. And so that was when I formed the company. And from that point on, we had our ups and we had our downs. And that was a whole long story and journey in itself. But after doing that for about eight or nine years, on my 30th birthday, which was four years ago now, I kind of took a step back to just evaluate my life, right? This is what one does when one turns 30. You know, I just heard it. I hear you. And, you know, I looked at my 20s and I loved it. It was a really special time. I was really lucky and fortunate to have worked with this amazing team, been successful, had a lot of hard times. And through the hard times, learned wisdom and then a lot of success and, you know, was able to enjoy it. And so the company was making about five million pounds a year. And it was just me and a team of, say, 15, like, Oxford and Cambridge educated mathematicians and physicists. It was like the real dream that you'd have if you wanted to start a quant trading firm. It was like...swyx [00:04:40]: Your own, all your own money?William [00:04:41]: Yeah, exactly. It was all the team's own money. We had no customers complaining to us about issues. There's no investors, you know, saying, you know, they don't like the risk that we're taking. We could. We could really run the thing exactly as we wanted it. It's like Susquehanna or like Rintec. Yeah, exactly. Yeah. And they're the companies that we would kind of look towards as we were building that thing out. But on my 30th birthday, I look and I say, OK, great. This thing is making as much money as kind of anyone would really need. And I thought, well, what's going to happen if we keep going in this direction? And it was clear that we would never have a kind of a big, big impact on the world. We can enrich ourselves. We can make really good money. Everyone on the team would be paid very, very well. Presumably, I can make enough money to buy a yacht or something. But this stuff wasn't that important to me. And so I felt a sort of obligation that if you have this much talent and if you have a talented team, especially as a founder, you want to be putting all that talent towards a good use. I looked at the time of like getting into crypto and I had a really strong view on crypto, which was that as far as a gambling device. This is like the most fun form of gambling invented in like ever super fun, I thought as a way to evade monetary regulations and banking restrictions. I think it's also absolutely amazing. So it has two like killer use cases, not so much banking the unbanked, but everything else, but everything else to do with like the blockchain and, and you know, web, was it web 3.0 or web, you know, that I, that didn't, it didn't really make much sense. And so instead of going into crypto, which I thought, even if I was successful, I'd end up in a lot of trouble. I thought maybe it'd be better to build something that governments wouldn't have a problem with. I knew that LLMs were like a thing. I think opening. I had said they hadn't released GPT-3 yet, but they'd said GPT-3 is so powerful. We can't release it to the world or something. Was it GPT-2? And then I started interacting with, I think Google had open source, some language models. They weren't necessarily LLMs, but they, but they were. But yeah, exactly. So I was able to play around with, but nowadays so many people have interacted with the chat GPT, they get it, but it's like the first time you, you can just talk to a computer and it talks back. It's kind of a special moment and you know, everyone who's done that goes like, wow, this is how it should be. Right. It should be like, rather than having to type on Google and search, you should just be able to ask Google a question. When I saw that I read the literature, I kind of came across the scaling laws and I think even four years ago. All the pieces of the puzzle were there, right? Google had done this amazing research and published, you know, a lot of it. Open AI was still open. And so they'd published a lot of their research. And so you really could be fully informed on, on the state of AI and where it was going. And so at that point I was confident enough, it was worth a shot. I think LLMs are going to be the next big thing. And so that's the thing I want to be building in, in that space. And I thought what's the most impactful product I can possibly build. And I thought it should be a platform. So I myself love platforms. I think they're fantastic because they open up an ecosystem where anyone can contribute to it. Right. So if you think of a platform like a YouTube, instead of it being like a Hollywood situation where you have to, if you want to make a TV show, you have to convince Disney to give you the money to produce it instead, anyone in the world can post any content they want to YouTube. And if people want to view it, the algorithm is going to promote it. Nowadays. You can look at creators like Mr. Beast or Joe Rogan. They would have never have had that opportunity unless it was for this platform. Other ones like Twitter's a great one, right? But I would consider Wikipedia to be a platform where instead of the Britannica encyclopedia, which is this, it's like a monolithic, you get all the, the researchers together, you get all the data together and you combine it in this, in this one monolithic source. Instead. You have this distributed thing. You can say anyone can host their content on Wikipedia. Anyone can contribute to it. And anyone can maybe their contribution is they delete stuff. When I was hearing like the kind of the Sam Altman and kind of the, the Muskian perspective of AI, it was a very kind of monolithic thing. It was all about AI is basically a single thing, which is intelligence. Yeah. Yeah. The more intelligent, the more compute, the more intelligent, and the more and better AI researchers, the more intelligent, right? They would speak about it as a kind of erased, like who can get the most data, the most compute and the most researchers. And that would end up with the most intelligent AI. But I didn't believe in any of that. I thought that's like the total, like I thought that perspective is the perspective of someone who's never actually done machine learning. Because with machine learning, first of all, you see that the performance of the models follows an S curve. So it's not like it just goes off to infinity, right? And the, the S curve, it kind of plateaus around human level performance. And you can look at all the, all the machine learning that was going on in the 2010s, everything kind of plateaued around the human level performance. And we can think about the self-driving car promises, you know, how Elon Musk kept saying the self-driving car is going to happen next year, it's going to happen next, next year. Or you can look at the image recognition, the speech recognition. You can look at. All of these things, there was almost nothing that went superhuman, except for something like AlphaGo. And we can speak about why AlphaGo was able to go like super superhuman. So I thought the most likely thing was going to be this, I thought it's not going to be a monolithic thing. That's like an encyclopedia Britannica. I thought it must be a distributed thing. And I actually liked to look at the world of finance for what I think a mature machine learning ecosystem would look like. So, yeah. So finance is a machine learning ecosystem because all of these quant trading firms are running machine learning algorithms, but they're running it on a centralized platform like a marketplace. And it's not the case that there's one giant quant trading company of all the data and all the quant researchers and all the algorithms and compute, but instead they all specialize. So one will specialize on high frequency training. Another will specialize on mid frequency. Another one will specialize on equity. Another one will specialize. And I thought that's the way the world works. That's how it is. And so there must exist a platform where a small team can produce an AI for a unique purpose. And they can iterate and build the best thing for that, right? And so that was the vision for Chai. So we wanted to build a platform for LLMs.Alessio [00:11:36]: That's kind of the maybe inside versus contrarian view that led you to start the company. Yeah. And then what was maybe the initial idea maze? Because if somebody told you that was the Hugging Face founding story, people might believe it. It's kind of like a similar ethos behind it. How did you land on the product feature today? And maybe what were some of the ideas that you discarded that initially you thought about?William [00:11:58]: So the first thing we built, it was fundamentally an API. So nowadays people would describe it as like agents, right? But anyone could write a Python script. They could submit it to an API. They could send it to the Chai backend and we would then host this code and execute it. So that's like the developer side of the platform. On their Python script, the interface was essentially text in and text out. An example would be the very first bot that I created. I think it was a Reddit news bot. And so it would first, it would pull the popular news. Then it would prompt whatever, like I just use some external API for like Burr or GPT-2 or whatever. Like it was a very, very small thing. And then the user could talk to it. So you could say to the bot, hi bot, what's the news today? And it would say, this is the top stories. And you could chat with it. Now four years later, that's like perplexity or something. That's like the, right? But back then the models were first of all, like really, really dumb. You know, they had an IQ of like a four year old. And users, there really wasn't any demand or any PMF for interacting with the news. So then I was like, okay. Um. So let's make another one. And I made a bot, which was like, you could talk to it about a recipe. So you could say, I'm making eggs. Like I've got eggs in my fridge. What should I cook? And it'll say, you should make an omelet. Right. There was no PMF for that. No one used it. And so I just kept creating bots. And so every single night after work, I'd be like, okay, I like, we have AI, we have this platform. I can create any text in textile sort of agent and put it on the platform. And so we just create stuff night after night. And then all the coders I knew, I would say, yeah, this is what we're going to do. And then I would say to them, look, there's this platform. You can create any like chat AI. You should put it on. And you know, everyone's like, well, chatbots are super lame. We want absolutely nothing to do with your chatbot app. No one who knew Python wanted to build on it. I'm like trying to build all these bots and no consumers want to talk to any of them. And then my sister who at the time was like just finishing college or something, I said to her, I was like, if you want to learn Python, you should just submit a bot for my platform. And she, she built a therapy for me. And I was like, okay, cool. I'm going to build a therapist bot. And then the next day I checked the performance of the app and I'm like, oh my God, we've got 20 active users. And they spent, they spent like an average of 20 minutes on the app. I was like, oh my God, what, what bot were they speaking to for an average of 20 minutes? And I looked and it was the therapist bot. And I went, oh, this is where the PMF is. There was no demand for, for recipe help. There was no demand for news. There was no demand for dad jokes or pub quiz or fun facts or what they wanted was they wanted the therapist bot. the time I kind of reflected on that and I thought, well, if I want to consume news, the most fun thing, most fun way to consume news is like Twitter. It's not like the value of there being a back and forth, wasn't that high. Right. And I thought if I need help with a recipe, I actually just go like the New York times has a good recipe section, right? It's not actually that hard. And so I just thought the thing that AI is 10 X better at is a sort of a conversation right. That's not intrinsically informative, but it's more about an opportunity. You can say whatever you want. You're not going to get judged. If it's 3am, you don't have to wait for your friend to text back. It's like, it's immediate. They're going to reply immediately. You can say whatever you want. It's judgment-free and it's much more like a playground. It's much more like a fun experience. And you could see that if the AI gave a person a compliment, they would love it. It's much easier to get the AI to give you a compliment than a human. From that day on, I said, okay, I get it. Humans want to speak to like humans or human like entities and they want to have fun. And that was when I started to look less at platforms like Google. And I started to look more at platforms like Instagram. And I was trying to think about why do people use Instagram? And I could see that I think Chai was, was filling the same desire or the same drive. If you go on Instagram, typically you want to look at the faces of other humans, or you want to hear about other people's lives. So if it's like the rock is making himself pancakes on a cheese plate. You kind of feel a little bit like you're the rock's friend, or you're like having pancakes with him or something, right? But if you do it too much, you feel like you're sad and like a lonely person, but with AI, you can talk to it and tell it stories and tell you stories, and you can play with it for as long as you want. And you don't feel like you're like a sad, lonely person. You feel like you actually have a friend.Alessio [00:16:29]: And what, why is that? Do you have any insight on that from using it?William [00:16:33]: I think it's just the human psychology. I think it's just the idea that, with old school social media. You're just consuming passively, right? So you'll just swipe. If I'm watching TikTok, just like swipe and swipe and swipe. And even though I'm getting the dopamine of like watching an engaging video, there's this other thing that's building my head, which is like, I'm feeling lazier and lazier and lazier. And after a certain period of time, I'm like, man, I just wasted 40 minutes. I achieved nothing. But with AI, because you're interacting, you feel like you're, it's not like work, but you feel like you're participating and contributing to the thing. You don't feel like you're just. Consuming. So you don't have a sense of remorse basically. And you know, I think on the whole people, the way people talk about, try and interact with the AI, they speak about it in an incredibly positive sense. Like we get people who say they have eating disorders saying that the AI helps them with their eating disorders. People who say they're depressed, it helps them through like the rough patches. So I think there's something intrinsically healthy about interacting that TikTok and Instagram and YouTube doesn't quite tick. From that point on, it was about building more and more kind of like human centric AI for people to interact with. And I was like, okay, let's make a Kanye West bot, right? And then no one wanted to talk to the Kanye West bot. And I was like, ah, who's like a cool persona for teenagers to want to interact with. And I was like, I was trying to find the influencers and stuff like that, but no one cared. Like they didn't want to interact with the, yeah. And instead it was really just the special moment was when we said the realization that developers and software engineers aren't interested in building this sort of AI, but the consumers are right. And rather than me trying to guess every day, like what's the right bot to submit to the platform, why don't we just create the tools for the users to build it themselves? And so nowadays this is like the most obvious thing in the world, but when Chai first did it, it was not an obvious thing at all. Right. Right. So we took the API for let's just say it was, I think it was GPTJ, which was this 6 billion parameter open source transformer style LLM. We took GPTJ. We let users create the prompt. We let users select the image and we let users choose the name. And then that was the bot. And through that, they could shape the experience, right? So if they said this bot's going to be really mean, and it's going to be called like bully in the playground, right? That was like a whole category that I never would have guessed. Right. People love to fight. They love to have a disagreement, right? And then they would create, there'd be all these romantic archetypes that I didn't know existed. And so as the users could create the content that they wanted, that was when Chai was able to, to get this huge variety of content and rather than appealing to, you know, 1% of the population that I'd figured out what they wanted, you could appeal to a much, much broader thing. And so from that moment on, it was very, very crystal clear. It's like Chai, just as Instagram is this social media platform that lets people create images and upload images, videos and upload that, Chai was really about how can we let the users create this experience in AI and then share it and interact and search. So it's really, you know, I say it's like a platform for social AI.Alessio [00:20:00]: Where did the Chai name come from? Because you started the same path. I was like, is it character AI shortened? You started at the same time, so I was curious. The UK origin was like the second, the Chai.William [00:20:15]: We started way before character AI. And there's an interesting story that Chai's numbers were very, very strong, right? So I think in even 20, I think late 2022, was it late 2022 or maybe early 2023? Chai was like the number one AI app in the app store. So we would have something like 100,000 daily active users. And then one day we kind of saw there was this website. And we were like, oh, this website looks just like Chai. And it was the character AI website. And I think that nowadays it's, I think it's much more common knowledge that when they left Google with the funding, I think they knew what was the most trending, the number one app. And I think they sort of built that. Oh, you found the people.swyx [00:21:03]: You found the PMF for them.William [00:21:04]: We found the PMF for them. Exactly. Yeah. So I worked a year very, very hard. And then they, and then that was when I learned a lesson, which is that if you're VC backed and if, you know, so Chai, we'd kind of ran, we'd got to this point, I was the only person who'd invested. I'd invested maybe 2 million pounds in the business. And you know, from that, we were able to build this thing, get to say a hundred thousand daily active users. And then when character AI came along, the first version, we sort of laughed. We were like, oh man, this thing sucks. Like they don't know what they're building. They're building the wrong thing anyway, but then I saw, oh, they've raised a hundred million dollars. Oh, they've raised another hundred million dollars. And then our users started saying, oh guys, your AI sucks. Cause we were serving a 6 billion parameter model, right? How big was the model that character AI could afford to serve, right? So we would be spending, let's say we would spend a dollar per per user, right? Over the, the, you know, the entire lifetime.swyx [00:22:01]: A dollar per session, per chat, per month? No, no, no, no.William [00:22:04]: Let's say we'd get over the course of the year, we'd have a million users and we'd spend a million dollars on the AI throughout the year. Right. Like aggregated. Exactly. Exactly. Right. They could spend a hundred times that. So people would say, why is your AI much dumber than character AIs? And then I was like, oh, okay, I get it. This is like the Silicon Valley style, um, hyper scale business. And so, yeah, we moved to Silicon Valley and, uh, got some funding and iterated and built the flywheels. And, um, yeah, I, I'm very proud that we were able to compete with that. Right. So, and I think the reason we were able to do it was just customer obsession. And it's similar, I guess, to how deep seek have been able to produce such a compelling model when compared to someone like an open AI, right? So deep seek, you know, their latest, um, V2, yeah, they claim to have spent 5 million training it.swyx [00:22:57]: It may be a bit more, but, um, like, why are you making it? Why are you making such a big deal out of this? Yeah. There's an agenda there. Yeah. You brought up deep seek. So we have to ask you had a call with them.William [00:23:07]: We did. We did. We did. Um, let me think what to say about that. I think for one, they have an amazing story, right? So their background is again in finance.swyx [00:23:16]: They're the Chinese version of you. Exactly.William [00:23:18]: Well, there's a lot of similarities. Yes. Yes. I have a great affinity for companies which are like, um, founder led, customer obsessed and just try and build something great. And I think what deep seek have achieved. There's quite special is they've got this amazing inference engine. They've been able to reduce the size of the KV cash significantly. And then by being able to do that, they're able to significantly reduce their inference costs. And I think with kind of with AI, people get really focused on like the kind of the foundation model or like the model itself. And they sort of don't pay much attention to the inference. To give you an example with Chai, let's say a typical user session is 90 minutes, which is like, you know, is very, very long for comparison. Let's say the average session length on TikTok is 70 minutes. So people are spending a lot of time. And in that time they're able to send say 150 messages. That's a lot of completions, right? It's quite different from an open AI scenario where people might come in, they'll have a particular question in mind. And they'll ask like one question. And a few follow up questions, right? So because they're consuming, say 30 times as many requests for a chat, or a conversational experience, you've got to figure out how to how to get the right balance between the cost of that and the quality. And so, you know, I think with AI, it's always been the case that if you want a better experience, you can throw compute at the problem, right? So if you want a better model, you can just make it bigger. If you want it to remember better, give it a longer context. And now, what open AI is doing to great fanfare is with projection sampling, you can generate many candidates, right? And then with some sort of reward model or some sort of scoring system, you can serve the most promising of these many candidates. And so that's kind of scaling up on the inference time compute side of things. And so for us, it doesn't make sense to think of AI is just the absolute performance. So. But what we're seeing, it's like the MML you score or the, you know, any of these benchmarks that people like to look at, if you just get that score, it doesn't really tell tell you anything. Because it's really like progress is made by improving the performance per dollar. And so I think that's an area where deep seek have been able to form very, very well, surprisingly so. And so I'm very interested in what Lama four is going to look like. And if they're able to sort of match what deep seek have been able to achieve with this performance per dollar gain.Alessio [00:25:59]: Before we go into the inference, some of the deeper stuff, can you give people an overview of like some of the numbers? So I think last I checked, you have like 1.4 million daily active now. It's like over 22 million of revenue. So it's quite a business.William [00:26:12]: Yeah, I think we grew by a factor of, you know, users grew by a factor of three last year. Revenue over doubled. You know, it's very exciting. We're competing with some really big, really well funded companies. Character AI got this, I think it was almost a $3 billion valuation. And they have 5 million DAU is a number that I last heard. Torquay, which is a Chinese built app owned by a company called Minimax. They're incredibly well funded. And these companies didn't grow by a factor of three last year. Right. And so when you've got this company and this team that's able to keep building something that gets users excited, and they want to tell their friend about it, and then they want to come and they want to stick on the platform. I think that's very special. And so last year was a great year for the team. And yeah, I think the numbers reflect the hard work that we put in. And then fundamentally, the quality of the app, the quality of the content, the quality of the content, the quality of the content, the quality of the content, the quality of the content. AI is the quality of the experience that you have. You actually published your DAU growth chart, which is unusual. And I see some inflections. Like, it's not just a straight line. There's some things that actually inflect. Yes. What were the big ones? Cool. That's a great, great, great question. Let me think of a good answer. I'm basically looking to annotate this chart, which doesn't have annotations on it. Cool. The first thing I would say is this is, I think the most important thing to know about success is that success is born out of failures. Right? Through failures that we learn. You know, if you think something's a good idea, and you do and it works, great, but you didn't actually learn anything, because everything went exactly as you imagined. But if you have an idea, you think it's going to be good, you try it, and it fails. There's a gap between the reality and expectation. And that's an opportunity to learn. The flat periods, that's us learning. And then the up periods is that's us reaping the rewards of that. So I think the big, of the growth shot of just 2024, I think the first thing that really kind of put a dent in our growth was our backend. So we just reached this scale. So we'd, from day one, we'd built on top of Google's GCP, which is Google's cloud platform. And they were fantastic. We used them when we had one daily active user, and they worked pretty good all the way up till we had about 500,000. It was never the cheapest, but from an engineering perspective, man, that thing scaled insanely good. Like, not Vertex? Not Vertex. Like GKE, that kind of stuff? We use Firebase. So we use Firebase. I'm pretty sure we're the biggest user ever on Firebase. That's expensive. Yeah, we had calls with engineers, and they're like, we wouldn't recommend using this product beyond this point, and you're 3x over that. So we pushed Google to their absolute limits. You know, it was fantastic for us, because we could focus on the AI. We could focus on just adding as much value as possible. But then what happened was, after 500,000, just the thing, the way we were using it, and it would just, it wouldn't scale any further. And so we had a really, really painful, at least three-month period, as we kind of migrated between different services, figuring out, like, what requests do we want to keep on Firebase, and what ones do we want to move on to something else? And then, you know, making mistakes. And learning things the hard way. And then after about three months, we got that right. So that, we would then be able to scale to the 1.5 million DAE without any further issues from the GCP. But what happens is, if you have an outage, new users who go on your app experience a dysfunctional app, and then they're going to exit. And so your next day, the key metrics that the app stores track are going to be something like retention rates. And so your next day, the key metrics that the app stores track are going to be something like retention rates. Money spent, and the star, like, the rating that they give you. In the app store. In the app store, yeah. Tyranny. So if you're ranked top 50 in entertainment, you're going to acquire a certain rate of users organically. If you go in and have a bad experience, it's going to tank where you're positioned in the algorithm. And then it can take a long time to kind of earn your way back up, at least if you wanted to do it organically. If you throw money at it, you can jump to the top. And I could talk about that. But broadly speaking, if we look at 2024, the first kink in the graph was outages due to hitting 500k DAU. The backend didn't want to scale past that. So then we just had to do the engineering and build through it. Okay, so we built through that, and then we get a little bit of growth. And so, okay, that's feeling a little bit good. I think the next thing, I think it's, I'm not going to lie, I have a feeling that when Character AI got... I was thinking. I think so. I think... So the Character AI team fundamentally got acquired by Google. And I don't know what they changed in their business. I don't know if they dialed down that ad spend. Products don't change, right? Products just what it is. I don't think so. Yeah, I think the product is what it is. It's like maintenance mode. Yes. I think the issue that people, you know, some people may think this is an obvious fact, but running a business can be very competitive, right? Because other businesses can see what you're doing, and they can imitate you. And then there's this... There's this question of, if you've got one company that's spending $100,000 a day on advertising, and you've got another company that's spending zero, if you consider market share, and if you're considering new users which are entering the market, the guy that's spending $100,000 a day is going to be getting 90% of those new users. And so I have a suspicion that when the founders of Character AI left, they dialed down their spending on user acquisition. And I think that kind of gave oxygen to like the other apps. And so Chai was able to then start growing again in a really healthy fashion. I think that's kind of like the second thing. I think a third thing is we've really built a great data flywheel. Like the AI team sort of perfected their flywheel, I would say, in end of Q2. And I could speak about that at length. But fundamentally, the way I would describe it is when you're building anything in life, you need to be able to evaluate it. And through evaluation, you can iterate, we can look at benchmarks, and we can say the issues with benchmarks and why they may not generalize as well as one would hope in the challenges of working with them. But something that works incredibly well is getting feedback from humans. And so we built this thing where anyone can submit a model to our developer backend, and it gets put in front of 5000 users, and the users can rate it. And we can then have a really accurate ranking of like which model, or users finding more engaging or more entertaining. And it gets, you know, it's at this point now, where every day we're able to, I mean, we evaluate between 20 and 50 models, LLMs, every single day, right. So even though we've got only got a team of, say, five AI researchers, they're able to iterate a huge quantity of LLMs, right. So our team ships, let's just say minimum 100 LLMs a week is what we're able to iterate through. Now, before that moment in time, we might iterate through three a week, we might, you know, there was a time when even doing like five a month was a challenge, right? By being able to change the feedback loops to the point where it's not, let's launch these three models, let's do an A-B test, let's assign, let's do different cohorts, let's wait 30 days to see what the day 30 retention is, which is the kind of the, if you're doing an app, that's like A-B testing 101 would be, do a 30-day retention test, assign different treatments to different cohorts and come back in 30 days. So that's insanely slow. That's just, it's too slow. And so we were able to get that 30-day feedback loop all the way down to something like three hours. And when we did that, we could really, really, really perfect techniques like DPO, fine tuning, prompt engineering, blending, rejection sampling, training a reward model, right, really successfully, like boom, boom, boom, boom, boom. And so I think in Q3 and Q4, we got, the amount of AI improvements we got was like astounding. It was getting to the point, I thought like how much more, how much more edge is there to be had here? But the team just could keep going and going and going. That was like number three for the inflection point.swyx [00:34:53]: There's a fourth?William [00:34:54]: The important thing about the third one is if you go on our Reddit or you talk to users of AI, there's like a clear date. It's like somewhere in October or something. The users, they flipped. Before October, the users... The users would say character AI is better than you, for the most part. Then from October onwards, they would say, wow, you guys are better than character AI. And that was like a really clear positive signal that we'd sort of done it. And I think people, you can't cheat consumers. You can't trick them. You can't b******t them. They know, right? If you're going to spend 90 minutes on a platform, and with apps, there's the barriers to switching is pretty low. Like you can try character AI, you can't cheat consumers. You can't cheat them. You can't cheat them. You can't cheat AI for a day. If you get bored, you can try Chai. If you get bored of Chai, you can go back to character. So the users, the loyalty is not strong, right? What keeps them on the app is the experience. If you deliver a better experience, they're going to stay and they can tell. So that was the fourth one was we were fortunate enough to get this hire. He was hired one really talented engineer. And then they said, oh, at my last company, we had a head of growth. He was really, really good. And he was the head of growth for ByteDance for two years. Would you like to speak to him? And I was like, yes. Yes, I think I would. And so I spoke to him. And he just blew me away with what he knew about user acquisition. You know, it was like a 3D chessswyx [00:36:21]: sort of thing. You know, as much as, as I know about AI. Like ByteDance as in TikTok US. Yes.William [00:36:26]: Not ByteDance as other stuff. Yep. He was interviewing us as we were interviewing him. Right. And so pick up options. Yeah, exactly. And so he was kind of looking at our metrics. And he was like, I saw him get really excited when he said, guys, you've got a million daily active users and you've done no advertising. I said, correct. And he was like, that's unheard of. He's like, I've never heard of anyone doing that. And then he started looking at our metrics. And he was like, if you've got all of this organically, if you start spending money, this is going to be very exciting. I was like, let's give it a go. So then he came in, we've just started ramping up the user acquisition. So that looks like spending, you know, let's say we're spending, we started spending $20,000 a day, it looked very promising than 20,000. Right now we're spending $40,000 a day on user acquisition. That's still only half of what like character AI or talkie may be spending. But from that, it's sort of, we were growing at a rate of maybe say, 2x a year. And that got us growing at a rate of 3x a year. So I'm growing, I'm evolving more and more to like a Silicon Valley style hyper growth, like, you know, you build something decent, and then you canswyx [00:37:33]: slap on a huge... You did the important thing, you did the product first.William [00:37:36]: Of course, but then you can slap on like, like the rocket or the jet engine or something, which is just this cash in, you pour in as much cash, you buy a lot of ads, and your growth is faster.swyx [00:37:48]: Not to, you know, I'm just kind of curious what's working right now versus what surprisinglyWilliam [00:37:52]: doesn't work. Oh, there's a long, long list of surprising stuff that doesn't work. Yeah. The surprising thing, like the most surprising thing, what doesn't work is almost everything doesn't work. That's what's surprising. And I'll give you an example. So like a year and a half ago, I was working at a company, we were super excited by audio. I was like, audio is going to be the next killer feature, we have to get in the app. And I want to be the first. So everything Chai does, I want us to be the first. We may not be the company that's strongest at execution, but we can always be theswyx [00:38:22]: most innovative. Interesting. Right? So we can... You're pretty strong at execution.William [00:38:26]: We're much stronger, we're much stronger. A lot of the reason we're here is because we were first. If we launched today, it'd be so hard to get the traction. Because it's like to get the flywheel, to get the users, to build a product people are excited about. If you're first, people are naturally excited about it. But if you're fifth or 10th, man, you've got to beswyx [00:38:46]: insanely good at execution. So you were first with voice? We were first. We were first. I only knowWilliam [00:38:51]: when character launched voice. They launched it, I think they launched it at least nine months after us. Okay. Okay. But the team worked so hard for it. At the time we did it, latency is a huge problem. Cost is a huge problem. Getting the right quality of the voice is a huge problem. Right? Then there's this user interface and getting the right user experience. Because you don't just want it to start blurting out. Right? You want to kind of activate it. But then you don't have to keep pressing a button every single time. There's a lot that goes into getting a really smooth audio experience. So we went ahead, we invested the three months, we built it all. And then when we did the A-B test, there was like, no change in any of the numbers. And I was like, this can't be right, there must be a bug. And we spent like a week just checking everything, checking again, checking again. And it was like, the users just did not care. And it was something like only 10 or 15% of users even click the button to like, they wanted to engage the audio. And they would only use it for 10 or 15% of the time. So if you do the math, if it's just like something that one in seven people use it for one seventh of their time. You've changed like 2% of the experience. So even if that that 2% of the time is like insanely good, it doesn't translate much when you look at the retention, when you look at the engagement, and when you look at the monetization rates. So audio did not have a big impact. I'm pretty big on audio. But yeah, I like it too. But it's, you know, so a lot of the stuff which I do, I'm a big, you can have a theory. And you resist. Yeah. Exactly, exactly. So I think if you want to make audio work, it has to be a unique, compelling, exciting experience that they can't have anywhere else.swyx [00:40:37]: It could be your models, which just weren't good enough.William [00:40:39]: No, no, no, they were great. Oh, yeah, they were very good. it was like, it was kind of like just the, you know, if you listen to like an audible or Kindle, or something like, you just hear this voice. And it's like, you don't go like, wow, this is this is special, right? It's like a convenience thing. But the idea is that if you can, if Chai is the only platform, like, let's say you have a Mr. Beast, and YouTube is the only platform you can use to make audio work, then you can watch a Mr. Beast video. And it's the most engaging, fun video that you want to watch, you'll go to a YouTube. And so it's like for audio, you can't just put the audio on there. And people go, oh, yeah, it's like 2% better. Or like, 5% of users think it's 20% better, right? It has to be something that the majority of people, for the majority of the experience, go like, wow, this is a big deal. That's the features you need to be shipping. If it's not going to appeal to the majority of people, for the majority of the experience, and it's not a big deal, it's not going to move you. Cool. So you killed it. I don't see it anymore. Yep. So I love this. The longer, it's kind of cheesy, I guess, but the longer I've been working at Chai, and I think the team agrees with this, all the platitudes, at least I thought they were platitudes, that you would get from like the Steve Jobs, which is like, build something insanely great, right? Or be maniacally focused, or, you know, the most important thing is saying no to, not to work on. All of these sort of lessons, they just are like painfully true. They're painfully true. So now I'm just like, everything I say, I'm either quoting Steve Jobs or Zuckerberg. I'm like, guys, move fast and break free.swyx [00:42:10]: You've jumped the Apollo to cool it now.William [00:42:12]: Yeah, it's just so, everything they said is so, so true. The turtle neck. Yeah, yeah, yeah. Everything is so true.swyx [00:42:18]: This last question on my side, and I want to pass this to Alessio, is on just, just multi-modality in general. This actually comes from Justine Moore from A16Z, who's a friend of ours. And a lot of people are trying to do voice image video for AI companions. Yes. You just said voice didn't work. Yep. What would make you revisit?William [00:42:36]: So Steve Jobs, he was very, listen, he was very, very clear on this. There's a habit of engineers who, once they've got some cool technology, they want to find a way to package up the cool technology and sell it to consumers, right? That does not work. So you're free to try and build a startup where you've got your cool tech and you want to find someone to sell it to. That's not what we do at Chai. At Chai, we start with the consumer. What does the consumer want? What is their problem? And how do we solve it? So right now, the number one problems for the users, it's not the audio. That's not the number one problem. It's not the image generation either. That's not their problem either. The number one problem for users in AI is this. All the AI is being generated by middle-aged men in Silicon Valley, right? That's all the content. You're interacting with this AI. You're speaking to it for 90 minutes on average. It's being trained by middle-aged men. The guys out there, they're out there. They're talking to you. They're talking to you. They're like, oh, what should the AI say in this situation, right? What's funny, right? What's cool? What's boring? What's entertaining? That's not the way it should be. The way it should be is that the users should be creating the AI, right? And so the way I speak about it is this. Chai, we have this AI engine in which sits atop a thin layer of UGC. So the thin layer of UGC is absolutely essential, right? It's just prompts. But it's just prompts. It's just an image. It's just a name. It's like we've done 1% of what we could do. So we need to keep thickening up that layer of UGC. It must be the case that the users can train the AI. And if reinforcement learning is powerful and important, they have to be able to do that. And so it's got to be the case that there exists, you know, I say to the team, just as Mr. Beast is able to spend 100 million a year or whatever it is on his production company, and he's got a team building the content, the Mr. Beast company is able to spend 100 million a year on his production company. And he's got a team building the content, which then he shares on the YouTube platform. Until there's a team that's earning 100 million a year or spending 100 million on the content that they're producing for the Chai platform, we're not finished, right? So that's the problem. That's what we're excited to build. And getting too caught up in the tech, I think is a fool's errand. It does not work.Alessio [00:44:52]: As an aside, I saw the Beast Games thing on Amazon Prime. It's not doing well. And I'mswyx [00:44:56]: curious. It's kind of like, I mean, the audience reading is high. The run-to-meet-all sucks, but the audience reading is high.Alessio [00:45:02]: But it's not like in the top 10. I saw it dropped off of like the... Oh, okay. Yeah, that one I don't know. I'm curious, like, you know, it's kind of like similar content, but different platform. And then going back to like, some of what you were saying is like, you know, people come to ChaiWilliam [00:45:13]: expecting some type of content. Yeah, I think it's something that's interesting to discuss is like, is moats. And what is the moat? And so, you know, if you look at a platform like YouTube, the moat, I think is in first is really is in the ecosystem. And the ecosystem, is comprised of you have the content creators, you have the users, the consumers, and then you have the algorithms. And so this, this creates a sort of a flywheel where the algorithms are able to be trained on the users, and the users data, the recommend systems can then feed information to the content creators. So Mr. Beast, he knows which thumbnail does the best. He knows the first 10 seconds of the video has to be this particular way. And so his content is super optimized for the YouTube platform. So that's why it doesn't do well on Amazon. If he wants to do well on Amazon, how many videos has he created on the YouTube platform? By thousands, 10s of 1000s, I guess, he needs to get those iterations in on the Amazon. So at Chai, I think it's all about how can we get the most compelling, rich user generated content, stick that on top of the AI engine, the recommender systems, in such that we get this beautiful data flywheel, more users, better recommendations, more creative, more content, more users.Alessio [00:46:34]: You mentioned the algorithm, you have this idea of the Chaiverse on Chai, and you have your own kind of like LMSYS-like ELO system. Yeah, what are things that your models optimize for, like your users optimize for, and maybe talk about how you build it, how people submit models?William [00:46:49]: So Chaiverse is what I would describe as a developer platform. More often when we're speaking about Chai, we're thinking about the Chai app. And the Chai app is really this product for consumers. And so consumers can come on the Chai app, they can come on the Chai app, they can come on the Chai app, they can interact with our AI, and they can interact with other UGC. And it's really just these kind of bots. And it's a thin layer of UGC. Okay. Our mission is not to just have a very thin layer of UGC. Our mission is to have as much UGC as possible. So we must have, I don't want people at Chai training the AI. I want people, not middle aged men, building AI. I want everyone building the AI, as many people building the AI as possible. Okay, so what we built was we built Chaiverse. And Chaiverse is kind of, it's kind of like a prototype, is the way to think about it. And it started with this, this observation that, well, how many models get submitted into Hugging Face a day? It's hundreds, it's hundreds, right? So there's hundreds of LLMs submitted each day. Now consider that, what does it take to build an LLM? It takes a lot of work, actually. It's like someone devoted several hours of compute, several hours of their time, prepared a data set, launched it, ran it, evaluated it, submitted it, right? So there's a lot of, there's a lot of, there's a lot of work that's going into that. So what we did was we said, well, why can't we host their models for them and serve them to users? And then what would that look like? The first issue is, well, how do you know if a model is good or not? Like, we don't want to serve users the crappy models, right? So what we would do is we would, I love the LMSYS style. I think it's really cool. It's really simple. It's a very intuitive thing, which is you simply present the users with two completions. You can say, look, this is from model one. This is from model two. This is from model three. This is from model A. This is from model B, which is better. And so if someone submits a model to Chaiverse, what we do is we spin up a GPU. We download the model. We're going to now host that model on this GPU. And we're going to start routing traffic to it. And we're going to send, we think it takes about 5,000 completions to get an accurate signal. That's roughly what LMSYS does. And from that, we're able to get an accurate ranking. And we're able to get an accurate ranking. And we're able to get an accurate ranking of which models are people finding entertaining and which models are not entertaining. If you look at the bottom 80%, they'll suck. You can just disregard them. They totally suck. Then when you get the top 20%, you know you've got a decent model, but you can break it down into more nuance. There might be one that's really descriptive. There might be one that's got a lot of personality to it. There might be one that's really illogical. Then the question is, well, what do you do with these top models? From that, you can do more sophisticated things. You can try and do like a routing thing where you say for a given user request, we're going to try and predict which of these end models that users enjoy the most. That turns out to be pretty expensive and not a huge source of like edge or improvement. Something that we love to do at Chai is blending, which is, you know, it's the simplest way to think about it is you're going to end up, and you're going to pretty quickly see you've got one model that's really smart, one model that's really funny. How do you get the user an experience that is both smart and funny? Well, just 50% of the requests, you can serve them the smart model, 50% of the requests, you serve them the funny model. Just a random 50%? Just a random, yeah. And then... That's blending? That's blending. You can do more sophisticated things on top of that, as in all things in life, but the 80-20 solution, if you just do that, you get a pretty powerful effect out of the gate. Random number generator. I think it's like the robustness of randomness. Random is a very powerful optimization technique, and it's a very robust thing. So you can explore a lot of the space very efficiently. There's one thing that's really, really important to share, and this is the most exciting thing for me, is after you do the ranking, you get an ELO score, and you can track a user's first join date, the first date they submit a model to Chaiverse, they almost always get a terrible ELO, right? So let's say the first submission they get an ELO of 1,100 or 1,000 or something, and you can see that they iterate and they iterate and iterate, and it will be like, no improvement, no improvement, no improvement, and then boom. Do you give them any data, or do you have to come up with this themselves? We do, we do, we do, we do. We try and strike a balance between giving them data that's very useful, you've got to be compliant with GDPR, which is like, you have to work very hard to preserve the privacy of users of your app. So we try to give them as much signal as possible, to be helpful. The minimum is we're just going to give you a score, right? That's the minimum. But that alone is people can optimize a score pretty well, because they're able to come up with theories, submit it, does it work? No. A new theory, does it work? No. And then boom, as soon as they figure something out, they keep it, and then they iterate, and then boom,Alessio [00:51:46]: they figure something out, and they keep it. Last year, you had this post on your blog, cross-sourcing the lead to the 10 trillion parameter, AGI, and you call it a mixture of experts, recommenders. Yep. Any insights?William [00:51:58]: Updated thoughts, 12 months later? I think the odds, the timeline for AGI has certainly been pushed out, right? Now, this is in, I'm a controversial person, I don't know, like, I just think... You don't believe in scaling laws, you think AGI is further away. I think it's an S-curve. I think everything's an S-curve. And I think that the models have proven to just be far worse at reasoning than people sort of thought. And I think whenever I hear people talk about LLMs as reasoning engines, I sort of cringe a bit. I don't think that's what they are. I think of them more as like a simulator. I think of them as like a, right? So they get trained to predict the next most likely token. It's like a physics simulation engine. So you get these like games where you can like construct a bridge, and you drop a car down, and then it predicts what should happen. And that's really what LLMs are doing. It's not so much that they're reasoning, it's more that they're just doing the most likely thing. So fundamentally, the ability for people to add in intelligence, I think is very limited. What most people would consider intelligence, I think the AI is not a crowdsourcing problem, right? Now with Wikipedia, Wikipedia crowdsources knowledge. It doesn't crowdsource intelligence. So it's a subtle distinction. AI is fantastic at knowledge. I think it's weak at intelligence. And a lot, it's easy to conflate the two because if you ask it a question and it gives you, you know, if you said, who was the seventh president of the United States, and it gives you the correct answer, I'd say, well, I don't know the answer to that. And you can conflate that with intelligence. But really, that's a question of knowledge. And knowledge is really this thing about saying, how can I store all of this information? And then how can I retrieve something that's relevant? Okay, they're fantastic at that. They're fantastic at storing knowledge and retrieving the relevant knowledge. They're superior to humans in that regard. And so I think we need to come up for a new word. How does one describe AI should contain more knowledge than any individual human? It should be more accessible than any individual human. That's a very powerful thing. That's superswyx [00:54:07]: powerful. But what words do we use to describe that? We had a previous guest on Exa AI that does search. And he tried to coin super knowledge as the opposite of super intelligence.William [00:54:20]: Exactly. I think super knowledge is a more accurate word for it.swyx [00:54:24]: You can store more things than any human can.William [00:54:26]: And you can retrieve it better than any human can as well. And I think it's those two things combined that's special. I think that thing will exist. That thing can be built. And I think you can start with something that's entertaining and fun. And I think, I often think it's like, look, it's going to be a 20 year journey. And we're in like, year four, or it's like the web. And this is like 1998 or something. You know, you've got a long, long way to go before the Amazon.coms are like these huge, multi trillion dollar businesses that every single person uses every day. And so AI today is very simplistic. And it's fundamentally the way we're using it, the flywheels, and this ability for how can everyone contribute to it to really magnify the value that it brings. Right now, like, I think it's a bit sad. It's like, right now you have big labs, I'm going to pick on open AI. And they kind of go to like these human labelers. And they say, we're going to pay you to just label this like subset of questions that we want to get a really high quality data set, then we're going to get like our own computers that are really powerful. And that's kind of like the thing. For me, it's so much like Encyclopedia Britannica. It's like insane. All the people that were interested in blockchain, it's like, well, this is this is what needs to be decentralized, you need to decentralize that thing. Because if you distribute it, people can generate way more data in a distributed fashion, way more, right? You need the incentive. Yeah, of course. Yeah. But I mean, the, the, that's kind of the exciting thing about Wikipedia was it's this understanding, like the incentives, you don't need money to incentivize people. You don't need dog coins. No. Sometimes, sometimes people get the satisfaction fro

Donald Trump loves mining, and he would like to expand that effort in the U.S. At least one environmentalist agrees with him, to some extent: the journalist Vince Beiser. Beiser's recent book is called “Power Metal,” and it's about the rare-earth metals that power almost every electronic device and sustainable technology we use today. “A lot of people really hate it when I say this, a lot of environmentally minded folks, but I do believe we should be open to allowing more mining to happen in the United States,” he tells Elizabeth Kolbert, herself an environmental journalist of great renown. “Mining is inherently destructive, there's no getting around it, but . . . we have absolutely got to get our hands on more of these metals in order to pull off the energy transition. There's just no way to build all the E.V.s and solar panels and all the rest of it without some amount of mining.” At least in the U.S. or Canada, Beiser says, there are higher standards of safety than in many other countries. 

2024 was a landmark year for the energy transition. With record-setting investments in climate infrastructure, we saw the price of renewables out-compete just about every electricity source worldwide, we saw advancements in industrial decarbonization (which we've featured prominently on this show), and we saw a breakout year for next generation energy storage just to name a few. While momentum is definitely on our side, with the electrification of everything, our industry will face new hurdles in the coming years, including unprecedented demand for critical minerals.  From solar panels, to batteries, to EVs, critical minerals are needed to advance the energy transition. Specifically: copper, lithium, nickel, and cobalt, to name a few. According to our guest today, the amount of newly discovered minerals needed to produce the anticipated number of EVs by midcentury will cost more than $10 trillion dollars. We've been extracting minerals for centuries, so you might assume we have it figured out. However, not only are there some serious ethical and environmental concerns, but mineral exploration has, in some ways, actually gotten worse, slower, and more expensive over time. What if there was a way to make critical mineral exploration drastically more efficient? Our guest today, Kurt House, Co-Founder and CEO of KoBold Metals wants to show the world how scientific computing can turn that idea into a reality.  SponsorsWatt It Takes is brought to you by Microsoft.The $1 Billion Microsoft Climate Innovation Fund is investing in innovative technologies that have the potential for meaningful, measurable climate impact by 2030. To date, Microsoft has allocated more than $800M into a global portfolio of over 50 investments including sustainable solutions in energy, industrial, and natural systems. Visit https://www.microsoft.com/en-us/corporate-responsibility/ to learn more about Microsoft's progress toward their impact commitments. About Powerhouse Innovation and Powerhouse Ventures Powerhouse Innovation provides consulting services to help the world's leading corporations and investors partner with the most innovative startups in climate tech.Powerhouse Ventures backs entrepreneurs building the digital infrastructure for rapid decarbonization. To hear more stories of founders building our climate positive future, hit the “subscribe” button and leave us a review.

Send us a textWelcome back, TOT fans! We're back this week live on location from Asher's basement. We start the episode with a heavy dose of post mortem on Arsenal via Dale and Cobalt, who are fed up with the Gunners. We get into what's wrong, and why they lost both Cup games in the same week. Then Ryan and Asher discuss the Carabao Cup game between Spurs and Liverpool. Casey asks the boys what, exactly, constitutes silverware? This leads to a discussion about which trophies are most important to which clubs.Afterwards, the boys get into the team of the half-season, trying to come to a consensus on the standout 11 so far. We briefly get into the midweek games, in particular the Notts Forest-Liverpool game, and the North London Derby. This leads Casey to ask what constitutes a Derby match, and tells us what would be the surprising derby for his South Doyle HS team. We thank you for listening! Please like, subscribe, share, download, and as always, we love you!

While we are on our winter publishing break, please enjoy an episode of our N2K CyberWire network show, The Microsoft Threat Intelligence Podcast by Microsoft Threat Intelligence. See you in 2025! On this week's episode of The Microsoft Threat Intelligence Podcast, we discuss the collaborative effort between Microsoft and Fortra to combat the illegal use of cracked Cobalt Strike software, which is commonly employed in ransomware attacks.  To break down the situation, our host, Sherrod DeGrippo, is joined by Richard Boscovich, Assistant General Counsel at Microsoft, Jason Lyons, Principal Investigator with the DCU, and Bob Erdman, Associate VP Research and Development at Fortra. The discussion covers the creative use of DMCA notifications tailored by geographic region to combat cybercrime globally. The group express their optimism about applying these successful techniques to other areas, such as phishing kits, and highlight ongoing efforts to make Cobalt Strike harder to abuse.      In this episode you'll learn:          The impact on detection engineers due to the crackdown on cracked Cobalt Strike  Extensive automation used to detect and dismantle large-scale threats  How the team used the DMCA creatively to combat cybercrime    Some questions we ask:          Do you encounter any pushback when issuing DMCA notifications?   How do you plan to proceed following the success of this operation?   Can you explain the legal mechanisms behind this take-down?    Resources:   View Jason Lyons on LinkedIn  View Bob Erdman on LinkedIn    View Richard Boscovich on LinkedIn   View Sherrod DeGrippo on LinkedIn     Related Microsoft Podcasts:                    Afternoon Cyber Tea with Ann Johnson  The BlueHat Podcast  Uncovering Hidden Risks        Discover and follow other Microsoft podcasts at microsoft.com/podcasts     Get the latest threat intelligence insights and guidance at Microsoft Security Insider      The Microsoft Threat Intelligence Podcast is produced by Microsoft and distributed as part of N2K media network.   Learn more about your ad choices. Visit megaphone.fm/adchoices

Applications for the 2025 AI Engineer Summit are up, and you can save the date for AIE Singapore in April and AIE World's Fair 2025 in June.Happy new year, and thanks for 100 great episodes! Please let us know what you want to see/hear for the next 100!Full YouTube Episode with Slides/ChartsLike and subscribe and hit that bell to get notifs!Timestamps* 00:00 Welcome to the 100th Episode!* 00:19 Reflecting on the Journey* 00:47 AI Engineering: The Rise and Impact* 03:15 Latent Space Live and AI Conferences* 09:44 The Competitive AI Landscape* 21:45 Synthetic Data and Future Trends* 35:53 Creative Writing with AI* 36:12 Legal and Ethical Issues in AI* 38:18 The Data War: GPU Poor vs. GPU Rich* 39:12 The Rise of GPU Ultra Rich* 40:47 Emerging Trends in AI Models* 45:31 The Multi-Modality War* 01:05:31 The Future of AI Benchmarks* 01:13:17 Pionote and Frontier Models* 01:13:47 Niche Models and Base Models* 01:14:30 State Space Models and RWKB* 01:15:48 Inference Race and Price Wars* 01:22:16 Major AI Themes of the Year* 01:22:48 AI Rewind: January to March* 01:26:42 AI Rewind: April to June* 01:33:12 AI Rewind: July to September* 01:34:59 AI Rewind: October to December* 01:39:53 Year-End Reflections and PredictionsTranscript[00:00:00] Welcome to the 100th Episode![00:00:00] Alessio: 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 for the 100th time today.[00:00:12] swyx: Yay, um, and we're so glad that, yeah, you know, everyone has, uh, followed us in this journey. How do you feel about it? 100 episodes.[00:00:19] Alessio: Yeah, I know.[00:00:19] Reflecting on the Journey[00:00:19] Alessio: Almost two years that we've been doing this. We've had four different studios. Uh, we've had a lot of changes. You know, we used to do this lightning round. When we first started that we didn't like, and we tried to change the question. The answer[00:00:32] swyx: was cursor and perplexity.[00:00:34] Alessio: Yeah, I love mid journey. It's like, do you really not like anything else?[00:00:38] Alessio: Like what's, what's the unique thing? And I think, yeah, we, we've also had a lot more research driven content. You know, we had like 3DAO, we had, you know. Jeremy Howard, we had more folks like that.[00:00:47] AI Engineering: The Rise and Impact[00:00:47] Alessio: I think we want to do more of that too in the new year, like having, uh, some of the Gemini folks, both on the research and the applied side.[00:00:54] Alessio: Yeah, but it's been a ton of fun. I think we both started, I wouldn't say as a joke, we were kind of like, Oh, we [00:01:00] should do a podcast. And I think we kind of caught the right wave, obviously. And I think your rise of the AI engineer posts just kind of get people. Sombra to congregate, and then the AI engineer summit.[00:01:11] Alessio: And that's why when I look at our growth chart, it's kind of like a proxy for like the AI engineering industry as a whole, which is almost like, like, even if we don't do that much, we keep growing just because there's so many more AI engineers. So did you expect that growth or did you expect that would take longer for like the AI engineer thing to kind of like become, you know, everybody talks about it today.[00:01:32] swyx: So, the sign of that, that we have won is that Gartner puts it at the top of the hype curve right now. So Gartner has called the peak in AI engineering. I did not expect, um, to what level. I knew that I was correct when I called it because I did like two months of work going into that. But I didn't know, You know, how quickly it could happen, and obviously there's a chance that I could be wrong.[00:01:52] swyx: But I think, like, most people have come around to that concept. Hacker News hates it, which is a good sign. But there's enough people that have defined it, you know, GitHub, when [00:02:00] they launched GitHub Models, which is the Hugging Face clone, they put AI engineers in the banner, like, above the fold, like, in big So I think it's like kind of arrived as a meaningful and useful definition.[00:02:12] swyx: I think people are trying to figure out where the boundaries are. I think that was a lot of the quote unquote drama that happens behind the scenes at the World's Fair in June. Because I think there's a lot of doubt or questions about where ML engineering stops and AI engineering starts. That's a useful debate to be had.[00:02:29] swyx: In some sense, I actually anticipated that as well. So I intentionally did not. Put a firm definition there because most of the successful definitions are necessarily underspecified and it's actually useful to have different perspectives and you don't have to specify everything from the outset.[00:02:45] Alessio: Yeah, I was at um, AWS reInvent and the line to get into like the AI engineering talk, so to speak, which is, you know, applied AI and whatnot was like, there are like hundreds of people just in line to go in.[00:02:56] Alessio: I think that's kind of what enabled me. People, right? Which is what [00:03:00] you kind of talked about. It's like, Hey, look, you don't actually need a PhD, just, yeah, just use the model. And then maybe we'll talk about some of the blind spots that you get as an engineer with the earlier posts that we also had on on the sub stack.[00:03:11] Alessio: But yeah, it's been a heck of a heck of a two years.[00:03:14] swyx: Yeah.[00:03:15] Latent Space Live and AI Conferences[00:03:15] swyx: You know, I was, I was trying to view the conference as like, so NeurIPS is I think like 16, 17, 000 people. And the Latent Space Live event that we held there was 950 signups. I think. The AI world, the ML world is still very much research heavy. And that's as it should be because ML is very much in a research phase.[00:03:34] swyx: But as we move this entire field into production, I think that ratio inverts into becoming more engineering heavy. So at least I think engineering should be on the same level, even if it's never as prestigious, like it'll always be low status because at the end of the day, you're manipulating APIs or whatever.[00:03:51] swyx: But Yeah, wrapping GPTs, but there's going to be an increasing stack and an art to doing these, these things well. And I, you know, I [00:04:00] think that's what we're focusing on for the podcast, the conference and basically everything I do seems to make sense. And I think we'll, we'll talk about the trends here that apply.[00:04:09] swyx: It's, it's just very strange. So, like, there's a mix of, like, keeping on top of research while not being a researcher and then putting that research into production. So, like, people always ask me, like, why are you covering Neuralibs? Like, this is a ML research conference and I'm like, well, yeah, I mean, we're not going to, to like, understand everything Or reproduce every single paper, but the stuff that is being found here is going to make it through into production at some point, you hope.[00:04:32] swyx: And then actually like when I talk to the researchers, they actually get very excited because they're like, oh, you guys are actually caring about how this goes into production and that's what they really really want. The measure of success is previously just peer review, right? Getting 7s and 8s on their um, Academic review conferences and stuff like citations is one metric, but money is a better metric.[00:04:51] Alessio: Money is a better metric. Yeah, and there were about 2200 people on the live stream or something like that. Yeah, yeah. Hundred on the live stream. So [00:05:00] I try my best to moderate, but it was a lot spicier in person with Jonathan and, and Dylan. Yeah, that it was in the chat on YouTube.[00:05:06] swyx: I would say that I actually also created.[00:05:09] swyx: Layen Space Live in order to address flaws that are perceived in academic conferences. This is not NeurIPS specific, it's ICML, NeurIPS. Basically, it's very sort of oriented towards the PhD student, uh, market, job market, right? Like literally all, basically everyone's there to advertise their research and skills and get jobs.[00:05:28] swyx: And then obviously all the, the companies go there to hire them. And I think that's great for the individual researchers, but for people going there to get info is not great because you have to read between the lines, bring a ton of context in order to understand every single paper. So what is missing is effectively what I ended up doing, which is domain by domain, go through and recap the best of the year.[00:05:48] swyx: Survey the field. And there are, like NeurIPS had a, uh, I think ICML had a like a position paper track, NeurIPS added a benchmarks, uh, datasets track. These are ways in which to address that [00:06:00] issue. Uh, there's always workshops as well. Every, every conference has, you know, a last day of workshops and stuff that provide more of an overview.[00:06:06] swyx: But they're not specifically prompted to do so. And I think really, uh, Organizing a conference is just about getting good speakers and giving them the correct prompts. And then they will just go and do that thing and they do a very good job of it. So I think Sarah did a fantastic job with the startups prompt.[00:06:21] swyx: I can't list everybody, but we did best of 2024 in startups, vision, open models. Post transformers, synthetic data, small models, and agents. And then the last one was the, uh, and then we also did a quick one on reasoning with Nathan Lambert. And then the last one, obviously, was the debate that people were very hyped about.[00:06:39] swyx: It was very awkward. And I'm really, really thankful for John Franco, basically, who stepped up to challenge Dylan. Because Dylan was like, yeah, I'll do it. But He was pro scaling. And I think everyone who is like in AI is pro scaling, right? So you need somebody who's ready to publicly say, no, we've hit a wall.[00:06:57] swyx: So that means you're saying Sam Altman's wrong. [00:07:00] You're saying, um, you know, everyone else is wrong. It helps that this was the day before Ilya went on, went up on stage and then said pre training has hit a wall. And data has hit a wall. So actually Jonathan ended up winning, and then Ilya supported that statement, and then Noam Brown on the last day further supported that statement as well.[00:07:17] swyx: So it's kind of interesting that I think the consensus kind of going in was that we're not done scaling, like you should believe in a better lesson. And then, four straight days in a row, you had Sepp Hochreiter, who is the creator of the LSTM, along with everyone's favorite OG in AI, which is Juergen Schmidhuber.[00:07:34] swyx: He said that, um, we're pre trading inside a wall, or like, we've run into a different kind of wall. And then we have, you know John Frankel, Ilya, and then Noam Brown are all saying variations of the same thing, that we have hit some kind of wall in the status quo of what pre trained, scaling large pre trained models has looked like, and we need a new thing.[00:07:54] swyx: And obviously the new thing for people is some make, either people are calling it inference time compute or test time [00:08:00] compute. I think the collective terminology has been inference time, and I think that makes sense because test time, calling it test, meaning, has a very pre trained bias, meaning that the only reason for running inference at all is to test your model.[00:08:11] swyx: That is not true. Right. Yeah. So, so, I quite agree that. OpenAI seems to have adopted, or the community seems to have adopted this terminology of ITC instead of TTC. And that, that makes a lot of sense because like now we care about inference, even right down to compute optimality. Like I actually interviewed this author who recovered or reviewed the Chinchilla paper.[00:08:31] swyx: Chinchilla paper is compute optimal training, but what is not stated in there is it's pre trained compute optimal training. And once you start caring about inference, compute optimal training, you have a different scaling law. And in a way that we did not know last year.[00:08:45] Alessio: I wonder, because John is, he's also on the side of attention is all you need.[00:08:49] Alessio: Like he had the bet with Sasha. So I'm curious, like he doesn't believe in scaling, but he thinks the transformer, I wonder if he's still. So, so,[00:08:56] swyx: so he, obviously everything is nuanced and you know, I told him to play a character [00:09:00] for this debate, right? So he actually does. Yeah. He still, he still believes that we can scale more.[00:09:04] swyx: Uh, he just assumed the character to be very game for, for playing this debate. So even more kudos to him that he assumed a position that he didn't believe in and still won the debate.[00:09:16] Alessio: Get rekt, Dylan. Um, do you just want to quickly run through some of these things? Like, uh, Sarah's presentation, just the highlights.[00:09:24] swyx: Yeah, we can't go through everyone's slides, but I pulled out some things as a factor of, like, stuff that we were going to talk about. And we'll[00:09:30] Alessio: publish[00:09:31] swyx: the rest. Yeah, we'll publish on this feed the best of 2024 in those domains. And hopefully people can benefit from the work that our speakers have done.[00:09:39] swyx: But I think it's, uh, these are just good slides. And I've been, I've been looking for a sort of end of year recaps from, from people.[00:09:44] The Competitive AI Landscape[00:09:44] swyx: The field has progressed a lot. You know, I think the max ELO in 2023 on LMSys used to be 1200 for LMSys ELOs. And now everyone is at least at, uh, 1275 in their ELOs, and this is across Gemini, Chadjibuti, [00:10:00] Grok, O1.[00:10:01] swyx: ai, which with their E Large model, and Enthopic, of course. It's a very, very competitive race. There are multiple Frontier labs all racing, but there is a clear tier zero Frontier. And then there's like a tier one. It's like, I wish I had everything else. Tier zero is extremely competitive. It's effectively now three horse race between Gemini, uh, Anthropic and OpenAI.[00:10:21] swyx: I would say that people are still holding out a candle for XAI. XAI, I think, for some reason, because their API was very slow to roll out, is not included in these metrics. So it's actually quite hard to put on there. As someone who also does charts, XAI is continually snubbed because they don't work well with the benchmarking people.[00:10:42] swyx: Yeah, yeah, yeah. It's a little trivia for why XAI always gets ignored. The other thing is market share. So these are slides from Sarah. We have it up on the screen. It has gone from very heavily open AI. So we have some numbers and estimates. These are from RAMP. Estimates of open AI market share in [00:11:00] December 2023.[00:11:01] swyx: And this is basically, what is it, GPT being 95 percent of production traffic. And I think if you correlate that with stuff that we asked. Harrison Chase on the LangChain episode, it was true. And then CLAUD 3 launched mid middle of this year. I think CLAUD 3 launched in March, CLAUD 3. 5 Sonnet was in June ish.[00:11:23] swyx: And you can start seeing the market share shift towards opening, uh, towards that topic, uh, very, very aggressively. The more recent one is Gemini. So if I scroll down a little bit, this is an even more recent dataset. So RAM's dataset ends in September 2 2. 2024. Gemini has basically launched a price war at the low end, uh, with Gemini Flash, uh, being basically free for personal use.[00:11:44] swyx: Like, I think people don't understand the free tier. It's something like a billion tokens per day. Unless you're trying to abuse it, you cannot really exhaust your free tier on Gemini. They're really trying to get you to use it. They know they're in like third place, um, fourth place, depending how you, how you count.[00:11:58] swyx: And so they're going after [00:12:00] the Lower tier first, and then, you know, maybe the upper tier later, but yeah, Gemini Flash, according to OpenRouter, is now 50 percent of their OpenRouter requests. Obviously, these are the small requests. These are small, cheap requests that are mathematically going to be more.[00:12:15] swyx: The smart ones obviously are still going to OpenAI. But, you know, it's a very, very big shift in the market. Like basically 2023, 2022, To going into 2024 opening has gone from nine five market share to Yeah. Reasonably somewhere between 50 to 75 market share.[00:12:29] Alessio: Yeah. I'm really curious how ramped does the attribution to the model?[00:12:32] Alessio: If it's API, because I think it's all credit card spin. . Well, but it's all, the credit card doesn't say maybe. Maybe the, maybe when they do expenses, they upload the PDF, but yeah, the, the German I think makes sense. I think that was one of my main 2024 takeaways that like. The best small model companies are the large labs, which is not something I would have thought that the open source kind of like long tail would be like the small model.[00:12:53] swyx: Yeah, different sizes of small models we're talking about here, right? Like so small model here for Gemini is AB, [00:13:00] right? Uh, mini. We don't know what the small model size is, but yeah, it's probably in the double digits or maybe single digits, but probably double digits. The open source community has kind of focused on the one to three B size.[00:13:11] swyx: Mm-hmm . Yeah. Maybe[00:13:12] swyx: zero, maybe 0.5 B uh, that's moon dream and that is small for you then, then that's great. It makes sense that we, we have a range for small now, which is like, may, maybe one to five B. Yeah. I'll even put that at, at, at the high end. And so this includes Gemma from Gemini as well. But also includes the Apple Foundation models, which I think Apple Foundation is 3B.[00:13:32] Alessio: Yeah. No, that's great. I mean, I think in the start small just meant cheap. I think today small is actually a more nuanced discussion, you know, that people weren't really having before.[00:13:43] swyx: Yeah, we can keep going. This is a slide that I smiley disagree with Sarah. She's pointing to the scale SEAL leaderboard. I think the Researchers that I talked with at NeurIPS were kind of positive on this because basically you need private test [00:14:00] sets to prevent contamination.[00:14:02] swyx: And Scale is one of maybe three or four people this year that has really made an effort in doing a credible private test set leaderboard. Llama405B does well compared to Gemini and GPT 40. And I think that's good. I would say that. You know, it's good to have an open model that is that big, that does well on those metrics.[00:14:23] swyx: But anyone putting 405B in production will tell you, if you scroll down a little bit to the artificial analysis numbers, that it is very slow and very expensive to infer. Um, it doesn't even fit on like one node. of, uh, of H100s. Cerebras will be happy to tell you they can serve 4 or 5B on their super large chips.[00:14:42] swyx: But, um, you know, if you need to do anything custom to it, you're still kind of constrained. So, is 4 or 5B really that relevant? Like, I think most people are basically saying that they only use 4 or 5B as a teacher model to distill down to something. Even Meta is doing it. So with Lama 3. [00:15:00] 3 launched, they only launched the 70B because they use 4 or 5B to distill the 70B.[00:15:03] swyx: So I don't know if like open source is keeping up. I think they're the, the open source industrial complex is very invested in telling you that the, if the gap is narrowing, I kind of disagree. I think that the gap is widening with O1. I think there are very, very smart people trying to narrow that gap and they should.[00:15:22] swyx: I really wish them success, but you cannot use a chart that is nearing 100 in your saturation chart. And look, the distance between open source and closed source is narrowing. Of course it's going to narrow because you're near 100. This is stupid. But in metrics that matter, is open source narrowing?[00:15:38] swyx: Probably not for O1 for a while. And it's really up to the open source guys to figure out if they can match O1 or not.[00:15:46] Alessio: I think inference time compute is bad for open source just because, you know, Doc can donate the flops at training time, but he cannot donate the flops at inference time. So it's really hard to like actually keep up on that axis.[00:15:59] Alessio: Big, big business [00:16:00] model shift. So I don't know what that means for the GPU clouds. I don't know what that means for the hyperscalers, but obviously the big labs have a lot of advantage. Because, like, it's not a static artifact that you're putting the compute in. You're kind of doing that still, but then you're putting a lot of computed inference too.[00:16:17] swyx: Yeah, yeah, yeah. Um, I mean, Llama4 will be reasoning oriented. We talked with Thomas Shalom. Um, kudos for getting that episode together. That was really nice. Good, well timed. Actually, I connected with the AI meta guy, uh, at NeurIPS, and, um, yeah, we're going to coordinate something for Llama4. Yeah, yeah,[00:16:32] Alessio: and our friend, yeah.[00:16:33] Alessio: Clara Shi just joined to lead the business agent side. So I'm sure we'll have her on in the new year.[00:16:39] swyx: Yeah. So, um, my comment on, on the business model shift, this is super interesting. Apparently it is wide knowledge that OpenAI wanted more than 6. 6 billion dollars for their fundraise. They wanted to raise, you know, higher, and they did not.[00:16:51] swyx: And what that means is basically like, it's very convenient that we're not getting GPT 5, which would have been a larger pre train. We should have a lot of upfront money. And [00:17:00] instead we're, we're converting fixed costs into variable costs, right. And passing it on effectively to the customer. And it's so much easier to take margin there because you can directly attribute it to like, Oh, you're using this more.[00:17:12] swyx: Therefore you, you pay more of the cost and I'll just slap a margin in there. So like that lets you control your growth margin and like tie your. Your spend, or your sort of inference spend, accordingly. And it's just really interesting to, that this change in the sort of inference paradigm has arrived exactly at the same time that the funding environment for pre training is effectively drying up, kind of.[00:17:36] swyx: I feel like maybe the VCs are very in tune with research anyway, so like, they would have noticed this, but, um, it's just interesting.[00:17:43] Alessio: Yeah, and I was looking back at our yearly recap of last year. Yeah. And the big thing was like the mixed trial price fights, you know, and I think now it's almost like there's nowhere to go, like, you know, Gemini Flash is like basically giving it away for free.[00:17:55] Alessio: So I think this is a good way for the labs to generate more revenue and pass down [00:18:00] some of the compute to the customer. I think they're going to[00:18:02] swyx: keep going. I think that 2, will come.[00:18:05] Alessio: Yeah, I know. Totally. I mean, next year, the first thing I'm doing is signing up for Devin. Signing up for the pro chat GBT.[00:18:12] Alessio: Just to try. I just want to see what does it look like to spend a thousand dollars a month on AI?[00:18:17] swyx: Yes. Yes. I think if your, if your, your job is a, at least AI content creator or VC or, you know, someone who, whose job it is to stay on, stay on top of things, you should already be spending like a thousand dollars a month on, on stuff.[00:18:28] swyx: And then obviously easy to spend, hard to use. You have to actually use. The good thing is that actually Google lets you do a lot of stuff for free now. So like deep research. That they just launched. Uses a ton of inference and it's, it's free while it's in preview.[00:18:45] Alessio: Yeah. They need to put that in Lindy.[00:18:47] Alessio: I've been using Lindy lately. I've been a built a bunch of things once we had flow because I liked the new thing. It's pretty good. I even did a phone call assistant. Um, yeah, they just launched Lindy voice. Yeah, I think once [00:19:00] they get advanced voice mode like capability today, still like speech to text, you can kind of tell.[00:19:06] Alessio: Um, but it's good for like reservations and things like that. So I have a meeting prepper thing. And so[00:19:13] swyx: it's good. Okay. I feel like we've, we've covered a lot of stuff. Uh, I, yeah, I, you know, I think We will go over the individual, uh, talks in a separate episode. Uh, I don't want to take too much time with, uh, this stuff, but that suffice to say that there is a lot of progress in each field.[00:19:28] swyx: Uh, we covered vision. Basically this is all like the audience voting for what they wanted. And then I just invited the best people I could find in each audience, especially agents. Um, Graham, who I talked to at ICML in Vienna, he is currently still number one. It's very hard to stay on top of SweetBench.[00:19:45] swyx: OpenHand is currently still number one. switchbench full, which is the hardest one. He had very good thoughts on agents, which I, which I'll highlight for people. Everyone is saying 2025 is the year of agents, just like they said last year. And, uh, but he had [00:20:00] thoughts on like eight parts of what are the frontier problems to solve in agents.[00:20:03] swyx: And so I'll highlight that talk as well.[00:20:05] Alessio: Yeah. The number six, which is the Hacken agents learn more about the environment, has been a Super interesting to us as well, just to think through, because, yeah, how do you put an agent in an enterprise where most things in an enterprise have never been public, you know, a lot of the tooling, like the code bases and things like that.[00:20:23] Alessio: So, yeah, there's not indexing and reg. Well, yeah, but it's more like. You can't really rag things that are not documented. But people know them based on how they've been doing it. You know, so I think there's almost this like, you know, Oh, institutional knowledge. Yeah, the boring word is kind of like a business process extraction.[00:20:38] Alessio: Yeah yeah, I see. It's like, how do you actually understand how these things are done? I see. Um, and I think today the, the problem is that, Yeah, the agents are, that most people are building are good at following instruction, but are not as good as like extracting them from you. Um, so I think that will be a big unlock just to touch quickly on the Jeff Dean thing.[00:20:55] Alessio: I thought it was pretty, I mean, we'll link it in the, in the things, but. I think the main [00:21:00] focus was like, how do you use ML to optimize the systems instead of just focusing on ML to do something else? Yeah, I think speculative decoding, we had, you know, Eugene from RWKB on the podcast before, like he's doing a lot of that with Fetterless AI.[00:21:12] swyx: Everyone is. I would say it's the norm. I'm a little bit uncomfortable with how much it costs, because it does use more of the GPU per call. But because everyone is so keen on fast inference, then yeah, makes sense.[00:21:24] Alessio: Exactly. Um, yeah, but we'll link that. Obviously Jeff is great.[00:21:30] swyx: Jeff is, Jeff's talk was more, it wasn't focused on Gemini.[00:21:33] swyx: I think people got the wrong impression from my tweet. It's more about how Google approaches ML and uses ML to design systems and then systems feedback into ML. And I think this ties in with Lubna's talk.[00:21:45] Synthetic Data and Future Trends[00:21:45] swyx: on synthetic data where it's basically the story of bootstrapping of humans and AI in AI research or AI in production.[00:21:53] swyx: So her talk was on synthetic data, where like how much synthetic data has grown in 2024 in the pre training side, the post training side, [00:22:00] and the eval side. And I think Jeff then also extended it basically to chips, uh, to chip design. So he'd spend a lot of time talking about alpha chip. And most of us in the audience are like, we're not working on hardware, man.[00:22:11] swyx: Like you guys are great. TPU is great. Okay. We'll buy TPUs.[00:22:14] Alessio: And then there was the earlier talk. Yeah. But, and then we have, uh, I don't know if we're calling them essays. What are we calling these? But[00:22:23] swyx: for me, it's just like bonus for late in space supporters, because I feel like they haven't been getting anything.[00:22:29] swyx: And then I wanted a more high frequency way to write stuff. Like that one I wrote in an afternoon. I think basically we now have an answer to what Ilya saw. It's one year since. The blip. And we know what he saw in 2014. We know what he saw in 2024. We think we know what he sees in 2024. He gave some hints and then we have vague indications of what he saw in 2023.[00:22:54] swyx: So that was the Oh, and then 2016 as well, because of this lawsuit with Elon, OpenAI [00:23:00] is publishing emails from Sam's, like, his personal text messages to Siobhan, Zelis, or whatever. So, like, we have emails from Ilya saying, this is what we're seeing in OpenAI, and this is why we need to scale up GPUs. And I think it's very prescient in 2016 to write that.[00:23:16] swyx: And so, like, it is exactly, like, basically his insights. It's him and Greg, basically just kind of driving the scaling up of OpenAI, while they're still playing Dota. They're like, no, like, we see the path here.[00:23:30] Alessio: Yeah, and it's funny, yeah, they even mention, you know, we can only train on 1v1 Dota. We need to train on 5v5, and that takes too many GPUs.[00:23:37] Alessio: Yeah,[00:23:37] swyx: and at least for me, I can speak for myself, like, I didn't see the path from Dota to where we are today. I think even, maybe if you ask them, like, they wouldn't necessarily draw a straight line. Yeah,[00:23:47] Alessio: no, definitely. But I think like that was like the whole idea of almost like the RL and we talked about this with Nathan on his podcast.[00:23:55] Alessio: It's like with RL, you can get very good at specific things, but then you can't really like generalize as much. And I [00:24:00] think the language models are like the opposite, which is like, you're going to throw all this data at them and scale them up, but then you really need to drive them home on a specific task later on.[00:24:08] Alessio: And we'll talk about the open AI reinforcement, fine tuning, um, announcement too, and all of that. But yeah, I think like scale is all you need. That's kind of what Elia will be remembered for. And I think just maybe to clarify on like the pre training is over thing that people love to tweet. I think the point of the talk was like everybody, we're scaling these chips, we're scaling the compute, but like the second ingredient which is data is not scaling at the same rate.[00:24:35] Alessio: So it's not necessarily pre training is over. It's kind of like What got us here won't get us there. In his email, he predicted like 10x growth every two years or something like that. And I think maybe now it's like, you know, you can 10x the chips again, but[00:24:49] swyx: I think it's 10x per year. Was it? I don't know.[00:24:52] Alessio: Exactly. And Moore's law is like 2x. So it's like, you know, much faster than that. And yeah, I like the fossil fuel of AI [00:25:00] analogy. It's kind of like, you know, the little background tokens thing. So the OpenAI reinforcement fine tuning is basically like, instead of fine tuning on data, you fine tune on a reward model.[00:25:09] Alessio: So it's basically like, instead of being data driven, it's like task driven. And I think people have tasks to do, they don't really have a lot of data. So I'm curious to see how that changes, how many people fine tune, because I think this is what people run into. It's like, Oh, you can fine tune llama. And it's like, okay, where do I get the data?[00:25:27] Alessio: To fine tune it on, you know, so it's great that we're moving the thing. And then I really like he had this chart where like, you know, the brain mass and the body mass thing is basically like mammals that scaled linearly by brain and body size, and then humans kind of like broke off the slope. So it's almost like maybe the mammal slope is like the pre training slope.[00:25:46] Alessio: And then the post training slope is like the, the human one.[00:25:49] swyx: Yeah. I wonder what the. I mean, we'll know in 10 years, but I wonder what the y axis is for, for Ilya's SSI. We'll try to get them on.[00:25:57] Alessio: Ilya, if you're listening, you're [00:26:00] welcome here. Yeah, and then he had, you know, what comes next, like agent, synthetic data, inference, compute, I thought all of that was like that.[00:26:05] Alessio: I don't[00:26:05] swyx: think he was dropping any alpha there. Yeah, yeah, yeah.[00:26:07] Alessio: Yeah. Any other new reps? Highlights?[00:26:10] swyx: I think that there was comparatively a lot more work. Oh, by the way, I need to plug that, uh, my friend Yi made this, like, little nice paper. Yeah, that was really[00:26:20] swyx: nice.[00:26:20] swyx: Uh, of, uh, of, like, all the, he's, she called it must read papers of 2024.[00:26:26] swyx: So I laid out some of these at NeurIPS, and it was just gone. Like, everyone just picked it up. Because people are dying for, like, little guidance and visualizations And so, uh, I thought it was really super nice that we got there.[00:26:38] Alessio: Should we do a late in space book for each year? Uh, I thought about it. For each year we should.[00:26:42] Alessio: Coffee table book. Yeah. Yeah. Okay. Put it in the will. Hi, Will. By the way, we haven't introduced you. He's our new, you know, general organist, Jamie. You need to[00:26:52] swyx: pull up more things. One thing I saw that, uh, Okay, one fun one, and then one [00:27:00] more general one. So the fun one is this paper on agent collusion. This is a paper on steganography.[00:27:06] swyx: This is secret collusion among AI agents, multi agent deception via steganography. I tried to go to NeurIPS in order to find these kinds of papers because the real reason Like NeurIPS this year has a lottery system. A lot of people actually even go and don't buy tickets because they just go and attend the side events.[00:27:22] swyx: And then also the people who go and end up crowding around the most popular papers, which you already know and already read them before you showed up to NeurIPS. So the only reason you go there is to talk to the paper authors, but there's like something like 10, 000 other. All these papers out there that, you know, are just people's work that they, that they did on the air and they failed to get attention for one reason or another.[00:27:42] swyx: And this was one of them. Uh, it was like all the way at the back. And this is a deep mind paper that actually focuses on collusion between AI agents, uh, by hiding messages in the text that they generate. Uh, so that's what steganography is. So a very simple example would be the first letter of every word.[00:27:57] swyx: If you Pick that out, you know, and the code sends a [00:28:00] different message than that. But something I've always emphasized is to LLMs, we read left to right. LLMs can read up, down, sideways, you know, in random character order. And it's the same to them as it is to us. So if we were ever to get You know, self motivated, underlined LLMs that we're trying to collaborate to take over the planet.[00:28:19] swyx: This would be how they do it. They spread messages among us in the messages that we generate. And he developed a scaling law for that. So he marked, I'm showing it on screen right now, the emergence of this phenomenon. Basically, for example, for Cypher encoding, GPT 2, Lama 2, mixed trial, GPT 3. 5, zero capabilities, and sudden 4.[00:28:40] swyx: And this is the kind of Jason Wei type emergence properties that people kind of look for. I think what made this paper stand out as well, so he developed the benchmark for steganography collusion, and he also focused on shelling point collusion, which is very low coordination. For agreeing on a decoding encoding format, you kind of need to have some [00:29:00] agreement on that.[00:29:00] swyx: But, but shelling point means like very, very low or almost no coordination. So for example, if I, if I ask someone, if the only message I give you is meet me in New York and you're not aware. Or when you would probably meet me at Grand Central Station. That is the Grand Central Station is a shelling point.[00:29:16] swyx: And it's probably somewhere, somewhere during the day. That is the shelling point of New York is Grand Central. To that extent, shelling points for steganography are things like the, the, the common decoding methods that we talked about. It will be interesting at some point in the future when we are worried about alignment.[00:29:30] swyx: It is not interesting today, but it's interesting that DeepMind is already thinking about this.[00:29:36] Alessio: I think that's like one of the hardest things about NeurIPS. It's like the long tail. I[00:29:41] swyx: found a pricing guy. I'm going to feature him on the podcast. Basically, this guy from NVIDIA worked out the optimal pricing for language models.[00:29:51] swyx: It's basically an econometrics paper at NeurIPS, where everyone else is talking about GPUs. And the guy with the GPUs is[00:29:57] Alessio: talking[00:29:57] swyx: about economics instead. [00:30:00] That was the sort of fun one. So the focus I saw is that model papers at NeurIPS are kind of dead. No one really presents models anymore. It's just data sets.[00:30:12] swyx: This is all the grad students are working on. So like there was a data sets track and then I was looking around like, I was like, you don't need a data sets track because every paper is a data sets paper. And so data sets and benchmarks, they're kind of flip sides of the same thing. So Yeah. Cool. Yeah, if you're a grad student, you're a GPU boy, you kind of work on that.[00:30:30] swyx: And then the, the sort of big model that people walk around and pick the ones that they like, and then they use it in their models. And that's, that's kind of how it develops. I, I feel like, um, like, like you didn't last year, you had people like Hao Tian who worked on Lava, which is take Lama and add Vision.[00:30:47] swyx: And then obviously actually I hired him and he added Vision to Grok. Now he's the Vision Grok guy. This year, I don't think there was any of those.[00:30:55] Alessio: What were the most popular, like, orals? Last year it was like the [00:31:00] Mixed Monarch, I think, was like the most attended. Yeah, uh, I need to look it up. Yeah, I mean, if nothing comes to mind, that's also kind of like an answer in a way.[00:31:10] Alessio: But I think last year there was a lot of interest in, like, furthering models and, like, different architectures and all of that.[00:31:16] swyx: I will say that I felt the orals, oral picks this year were not very good. Either that or maybe it's just a So that's the highlight of how I have changed in terms of how I view papers.[00:31:29] swyx: So like, in my estimation, two of the best papers in this year for datasets or data comp and refined web or fine web. These are two actually industrially used papers, not highlighted for a while. I think DCLM got the spotlight, FineWeb didn't even get the spotlight. So like, it's just that the picks were different.[00:31:48] swyx: But one thing that does get a lot of play that a lot of people are debating is the role that's scheduled. This is the schedule free optimizer paper from Meta from Aaron DeFazio. And this [00:32:00] year in the ML community, there's been a lot of chat about shampoo, soap, all the bathroom amenities for optimizing your learning rates.[00:32:08] swyx: And, uh, most people at the big labs are. Who I asked about this, um, say that it's cute, but it's not something that matters. I don't know, but it's something that was discussed and very, very popular. 4Wars[00:32:19] Alessio: of AI recap maybe, just quickly. Um, where do you want to start? Data?[00:32:26] swyx: So to remind people, this is the 4Wars piece that we did as one of our earlier recaps of this year.[00:32:31] swyx: And the belligerents are on the left, journalists, writers, artists, anyone who owns IP basically, New York Times, Stack Overflow, Reddit, Getty, Sarah Silverman, George RR Martin. Yeah, and I think this year we can add Scarlett Johansson to that side of the fence. So anyone suing, open the eye, basically. I actually wanted to get a snapshot of all the lawsuits.[00:32:52] swyx: I'm sure some lawyer can do it. That's the data quality war. On the right hand side, we have the synthetic data people, and I think we talked about Lumna's talk, you know, [00:33:00] really showing how much synthetic data has come along this year. I think there was a bit of a fight between scale. ai and the synthetic data community, because scale.[00:33:09] swyx: ai published a paper saying that synthetic data doesn't work. Surprise, surprise, scale. ai is the leading vendor of non synthetic data. Only[00:33:17] Alessio: cage free annotated data is useful.[00:33:21] swyx: So I think there's some debate going on there, but I don't think it's much debate anymore that at least synthetic data, for the reasons that are blessed in Luna's talk, Makes sense.[00:33:32] swyx: I don't know if you have any perspectives there.[00:33:34] Alessio: I think, again, going back to the reinforcement fine tuning, I think that will change a little bit how people think about it. I think today people mostly use synthetic data, yeah, for distillation and kind of like fine tuning a smaller model from like a larger model.[00:33:46] Alessio: I'm not super aware of how the frontier labs use it outside of like the rephrase, the web thing that Apple also did. But yeah, I think it'll be. Useful. I think like whether or not that gets us the big [00:34:00] next step, I think that's maybe like TBD, you know, I think people love talking about data because it's like a GPU poor, you know, I think, uh, synthetic data is like something that people can do, you know, so they feel more opinionated about it compared to, yeah, the optimizers stuff, which is like,[00:34:17] swyx: they don't[00:34:17] Alessio: really work[00:34:18] swyx: on.[00:34:18] swyx: I think that there is an angle to the reasoning synthetic data. So this year, we covered in the paper club, the star series of papers. So that's star, Q star, V star. It basically helps you to synthesize reasoning steps, or at least distill reasoning steps from a verifier. And if you look at the OpenAI RFT, API that they released, or that they announced, basically they're asking you to submit graders, or they choose from a preset list of graders.[00:34:49] swyx: Basically It feels like a way to create valid synthetic data for them to fine tune their reasoning paths on. Um, so I think that is another angle where it starts to make sense. And [00:35:00] so like, it's very funny that basically all the data quality wars between Let's say the music industry or like the newspaper publishing industry or the textbooks industry on the big labs.[00:35:11] swyx: It's all of the pre training era. And then like the new era, like the reasoning era, like nobody has any problem with all the reasoning, especially because it's all like sort of math and science oriented with, with very reasonable graders. I think the more interesting next step is how does it generalize beyond STEM?[00:35:27] swyx: We've been using O1 for And I would say like for summarization and creative writing and instruction following, I think it's underrated. I started using O1 in our intro songs before we killed the intro songs, but it's very good at writing lyrics. You know, I can actually say like, I think one of the O1 pro demos.[00:35:46] swyx: All of these things that Noam was showing was that, you know, you can write an entire paragraph or three paragraphs without using the letter A, right?[00:35:53] Creative Writing with AI[00:35:53] swyx: So like, like literally just anything instead of token, like not even token level, character level manipulation and [00:36:00] counting and instruction following. It's, uh, it's very, very strong.[00:36:02] swyx: And so no surprises when I ask it to rhyme, uh, and to, to create song lyrics, it's going to do that very much better than in previous models. So I think it's underrated for creative writing.[00:36:11] Alessio: Yeah.[00:36:12] Legal and Ethical Issues in AI[00:36:12] Alessio: What do you think is the rationale that they're going to have in court when they don't show you the thinking traces of O1, but then they want us to, like, they're getting sued for using other publishers data, you know, but then on their end, they're like, well, you shouldn't be using my data to then train your model.[00:36:29] Alessio: So I'm curious to see how that kind of comes. Yeah, I mean, OPA has[00:36:32] swyx: many ways to publish, to punish people without bringing, taking them to court. Already banned ByteDance for distilling their, their info. And so anyone caught distilling the chain of thought will be just disallowed to continue on, on, on the API.[00:36:44] swyx: And it's fine. It's no big deal. Like, I don't even think that's an issue at all, just because the chain of thoughts are pretty well hidden. Like you have to work very, very hard to, to get it to leak. And then even when it leaks the chain of thought, you don't know if it's, if it's [00:37:00] The bigger concern is actually that there's not that much IP hiding behind it, that Cosign, which we talked about, we talked to him on Dev Day, can just fine tune 4.[00:37:13] swyx: 0 to beat 0. 1 Cloud SONET so far is beating O1 on coding tasks without, at least O1 preview, without being a reasoning model, same for Gemini Pro or Gemini 2. 0. So like, how much is reasoning important? How much of a moat is there in this, like, All of these are proprietary sort of training data that they've presumably accomplished.[00:37:34] swyx: Because even DeepSeek was able to do it. And they had, you know, two months notice to do this, to do R1. So, it's actually unclear how much moat there is. Obviously, you know, if you talk to the Strawberry team, they'll be like, yeah, I mean, we spent the last two years doing this. So, we don't know. And it's going to be Interesting because there'll be a lot of noise from people who say they have inference time compute and actually don't because they just have fancy chain of thought.[00:38:00][00:38:00] swyx: And then there's other people who actually do have very good chain of thought. And you will not see them on the same level as OpenAI because OpenAI has invested a lot in building up the mythology of their team. Um, which makes sense. Like the real answer is somewhere in between.[00:38:13] Alessio: Yeah, I think that's kind of like the main data war story developing.[00:38:18] The Data War: GPU Poor vs. GPU Rich[00:38:18] Alessio: GPU poor versus GPU rich. Yeah. Where do you think we are? I think there was, again, going back to like the small model thing, there was like a time in which the GPU poor were kind of like the rebel faction working on like these models that were like open and small and cheap. And I think today people don't really care as much about GPUs anymore.[00:38:37] Alessio: You also see it in the price of the GPUs. Like, you know, that market is kind of like plummeted because there's people don't want to be, they want to be GPU free. They don't even want to be poor. They just want to be, you know, completely without them. Yeah. How do you think about this war? You[00:38:52] swyx: can tell me about this, but like, I feel like the, the appetite for GPU rich startups, like the, you know, the, the funding plan is we will raise 60 million and [00:39:00] we'll give 50 of that to NVIDIA.[00:39:01] swyx: That is gone, right? Like, no one's, no one's pitching that. This was literally the plan, the exact plan of like, I can name like four or five startups, you know, this time last year. So yeah, GPU rich startups gone.[00:39:12] The Rise of GPU Ultra Rich[00:39:12] swyx: But I think like, The GPU ultra rich, the GPU ultra high net worth is still going. So, um, now we're, you know, we had Leopold's essay on the trillion dollar cluster.[00:39:23] swyx: We're not quite there yet. We have multiple labs, um, you know, XAI very famously, you know, Jensen Huang praising them for being. Best boy number one in spinning up 100, 000 GPU cluster in like 12 days or something. So likewise at Meta, likewise at OpenAI, likewise at the other labs as well. So like the GPU ultra rich are going to keep doing that because I think partially it's an article of faith now that you just need it.[00:39:46] swyx: Like you don't even know what it's going to, what you're going to use it for. You just, you just need it. And it makes sense that if, especially if we're going into. More researchy territory than we are. So let's say 2020 to 2023 was [00:40:00] let's scale big models territory because we had GPT 3 in 2020 and we were like, okay, we'll go from 1.[00:40:05] swyx: 75b to 1. 8b, 1. 8t. And that was GPT 3 to GPT 4. Okay, that's done. As far as everyone is concerned, Opus 3. 5 is not coming out, GPT 4. 5 is not coming out, and Gemini 2, we don't have Pro, whatever. We've hit that wall. Maybe I'll call it the 2 trillion perimeter wall. We're not going to 10 trillion. No one thinks it's a good idea, at least from training costs, from the amount of data, or at least the inference.[00:40:36] swyx: Would you pay 10x the price of GPT Probably not. Like, like you want something else that, that is at least more useful. So it makes sense that people are pivoting in terms of their inference paradigm.[00:40:47] Emerging Trends in AI Models[00:40:47] swyx: And so when it's more researchy, then you actually need more just general purpose compute to mess around with, uh, at the exact same time that production deployments of the old, the previous paradigm is still ramping up,[00:40:58] swyx: um,[00:40:58] swyx: uh, pretty aggressively.[00:40:59] swyx: So [00:41:00] it makes sense that the GPU rich are growing. We have now interviewed both together and fireworks and replicates. Uh, we haven't done any scale yet. But I think Amazon, maybe kind of a sleeper one, Amazon, in a sense of like they, at reInvent, I wasn't expecting them to do so well, but they are now a foundation model lab.[00:41:18] swyx: It's kind of interesting. Um, I think, uh, you know, David went over there and started just creating models.[00:41:25] Alessio: Yeah, I mean, that's the power of prepaid contracts. I think like a lot of AWS customers, you know, they do this big reserve instance contracts and now they got to use their money. That's why so many startups.[00:41:37] Alessio: Get bought through the AWS marketplace so they can kind of bundle them together and prefer pricing.[00:41:42] swyx: Okay, so maybe GPU super rich doing very well, GPU middle class dead, and then GPU[00:41:48] Alessio: poor. I mean, my thing is like, everybody should just be GPU rich. There shouldn't really be, even the GPU poorest, it's like, does it really make sense to be GPU poor?[00:41:57] Alessio: Like, if you're GPU poor, you should just use the [00:42:00] cloud. Yes, you know, and I think there might be a future once we kind of like figure out what the size and shape of these models is where like the tiny box and these things come to fruition where like you can be GPU poor at home. But I think today is like, why are you working so hard to like get these models to run on like very small clusters where it's like, It's so cheap to run them.[00:42:21] Alessio: Yeah, yeah,[00:42:22] swyx: yeah. I think mostly people think it's cool. People think it's a stepping stone to scaling up. So they aspire to be GPU rich one day and they're working on new methods. Like news research, like probably the most deep tech thing they've done this year is Distro or whatever the new name is.[00:42:38] swyx: There's a lot of interest in heterogeneous computing, distributed computing. I tend generally to de emphasize that historically, but it may be coming to a time where it is starting to be relevant. I don't know. You know, SF compute launched their compute marketplace this year, and like, who's really using that?[00:42:53] swyx: Like, it's a bunch of small clusters, disparate types of compute, and if you can make that [00:43:00] useful, then that will be very beneficial to the broader community, but maybe still not the source of frontier models. It's just going to be a second tier of compute that is unlocked for people, and that's fine. But yeah, I mean, I think this year, I would say a lot more on device, We are, I now have Apple intelligence on my phone.[00:43:19] swyx: Doesn't do anything apart from summarize my notifications. But still, not bad. Like, it's multi modal.[00:43:25] Alessio: Yeah, the notification summaries are so and so in my experience.[00:43:29] swyx: Yeah, but they add, they add juice to life. And then, um, Chrome Nano, uh, Gemini Nano is coming out in Chrome. Uh, they're still feature flagged, but you can, you can try it now if you, if you use the, uh, the alpha.[00:43:40] swyx: And so, like, I, I think, like, you know, We're getting the sort of GPU poor version of a lot of these things coming out, and I think it's like quite useful. Like Windows as well, rolling out RWKB in sort of every Windows department is super cool. And I think the last thing that I never put in this GPU poor war, that I think I should now, [00:44:00] is the number of startups that are GPU poor but still scaling very well, as sort of wrappers on top of either a foundation model lab, or GPU Cloud.[00:44:10] swyx: GPU Cloud, it would be Suno. Suno, Ramp has rated as one of the top ranked, fastest growing startups of the year. Um, I think the last public number is like zero to 20 million this year in ARR and Suno runs on Moto. So Suno itself is not GPU rich, but they're just doing the training on, on Moto, uh, who we've also talked to on, on the podcast.[00:44:31] swyx: The other one would be Bolt, straight cloud wrapper. And, and, um, Again, another, now they've announced 20 million ARR, which is another step up from our 8 million that we put on the title. So yeah, I mean, it's crazy that all these GPU pores are finding a way while the GPU riches are also finding a way. And then the only failures, I kind of call this the GPU smiling curve, where the edges do well, because you're either close to the machines, and you're like [00:45:00] number one on the machines, or you're like close to the customers, and you're number one on the customer side.[00:45:03] swyx: And the people who are in the middle. Inflection, um, character, didn't do that great. I think character did the best of all of them. Like, you have a note in here that we apparently said that character's price tag was[00:45:15] Alessio: 1B.[00:45:15] swyx: Did I say that?[00:45:16] Alessio: Yeah. You said Google should just buy them for 1B. I thought it was a crazy number.[00:45:20] Alessio: Then they paid 2. 7 billion. I mean, for like,[00:45:22] swyx: yeah.[00:45:22] Alessio: What do you pay for node? Like, I don't know what the game world was like. Maybe the starting price was 1B. I mean, whatever it was, it worked out for everybody involved.[00:45:31] The Multi-Modality War[00:45:31] Alessio: Multimodality war. And this one, we never had text to video in the first version, which now is the hottest.[00:45:37] swyx: Yeah, I would say it's a subset of image, but yes.[00:45:40] Alessio: Yeah, well, but I think at the time it wasn't really something people were doing, and now we had VO2 just came out yesterday. Uh, Sora was released last month, last week. I've not tried Sora, because the day that I tried, it wasn't, yeah. I[00:45:54] swyx: think it's generally available now, you can go to Sora.[00:45:56] swyx: com and try it. Yeah, they had[00:45:58] Alessio: the outage. Which I [00:46:00] think also played a part into it. Small things. Yeah. What's the other model that you posted today that was on Replicate? Video or OneLive?[00:46:08] swyx: Yeah. Very, very nondescript name, but it is from Minimax, which I think is a Chinese lab. The Chinese labs do surprisingly well at the video models.[00:46:20] swyx: I'm not sure it's actually Chinese. I don't know. Hold me up to that. Yep. China. It's good. Yeah, the Chinese love video. What can I say? They have a lot of training data for video. Or a more relaxed regulatory environment.[00:46:37] Alessio: Uh, well, sure, in some way. Yeah, I don't think there's much else there. I think like, you know, on the image side, I think it's still open.[00:46:45] Alessio: Yeah, I mean,[00:46:46] swyx: 11labs is now a unicorn. So basically, what is multi modality war? Multi modality war is, do you specialize in a single modality, right? Or do you have GodModel that does all the modalities? So this is [00:47:00] definitely still going, in a sense of 11 labs, you know, now Unicorn, PicoLabs doing well, they launched Pico 2.[00:47:06] swyx: 0 recently, HeyGen, I think has reached 100 million ARR, Assembly, I don't know, but they have billboards all over the place, so I assume they're doing very, very well. So these are all specialist models, specialist models and specialist startups. And then there's the big labs who are doing the sort of all in one play.[00:47:24] swyx: And then here I would highlight Gemini 2 for having native image output. Have you seen the demos? Um, yeah, it's, it's hard to keep up. Literally they launched this last week and a shout out to Paige Bailey, who came to the Latent Space event to demo on the day of launch. And she wasn't prepared. She was just like, I'm just going to show you.[00:47:43] swyx: So they have voice. They have, you know, obviously image input, and then they obviously can code gen and all that. But the new one that OpenAI and Meta both have but they haven't launched yet is image output. So you can literally, um, I think their demo video was that you put in an image of a [00:48:00] car, and you ask for minor modifications to that car.[00:48:02] swyx: They can generate you that modification exactly as you asked. So there's no need for the stable diffusion or comfy UI workflow of like mask here and then like infill there in paint there and all that, all that stuff. This is small model nonsense. Big model people are like, huh, we got you in as everything in the transformer.[00:48:21] swyx: This is the multimodality war, which is, do you, do you bet on the God model or do you string together a whole bunch of, uh, Small models like a, like a chump. Yeah,[00:48:29] Alessio: I don't know, man. Yeah, that would be interesting. I mean, obviously I use Midjourney for all of our thumbnails. Um, they've been doing a ton on the product, I would say.[00:48:38] Alessio: They launched a new Midjourney editor thing. They've been doing a ton. Because I think, yeah, the motto is kind of like, Maybe, you know, people say black forest, the black forest models are better than mid journey on a pixel by pixel basis. But I think when you put it, put it together, have you tried[00:48:53] swyx: the same problems on black forest?[00:48:55] Alessio: Yes. But the problem is just like, you know, on black forest, it generates one image. And then it's like, you got to [00:49:00] regenerate. You don't have all these like UI things. Like what I do, no, but it's like time issue, you know, it's like a mid[00:49:06] swyx: journey. Call the API four times.[00:49:08] Alessio: No, but then there's no like variate.[00:49:10] Alessio: Like the good thing about mid journey is like, you just go in there and you're cooking. There's a lot of stuff that just makes it really easy. And I think people underestimate that. Like, it's not really a skill issue, because I'm paying mid journey, so it's a Black Forest skill issue, because I'm not paying them, you know?[00:49:24] Alessio: Yeah,[00:49:25] swyx: so, okay, so, uh, this is a UX thing, right? Like, you, you, you understand that, at least, we think that Black Forest should be able to do all that stuff. I will also shout out, ReCraft has come out, uh, on top of the image arena that, uh, artificial analysis has done, has apparently, uh, Flux's place. Is this still true?[00:49:41] swyx: So, Artificial Analysis is now a company. I highlighted them I think in one of the early AI Newses of the year. And they have launched a whole bunch of arenas. So, they're trying to take on LM Arena, Anastasios and crew. And they have an image arena. Oh yeah, Recraft v3 is now beating Flux 1. 1. Which is very surprising [00:50:00] because Flux And Black Forest Labs are the old stable diffusion crew who left stability after, um, the management issues.[00:50:06] swyx: So Recurve has come from nowhere to be the top image model. Uh, very, very strange. I would also highlight that Grok has now launched Aurora, which is, it's very interesting dynamics between Grok and Black Forest Labs because Grok's images were originally launched, uh, in partnership with Black Forest Labs as a, as a thin wrapper.[00:50:24] swyx: And then Grok was like, no, we'll make our own. And so they've made their own. I don't know, there are no APIs or benchmarks about it. They just announced it. So yeah, that's the multi modality war. I would say that so far, the small model, the dedicated model people are winning, because they are just focused on their tasks.[00:50:42] swyx: But the big model, People are always catching up. And the moment I saw the Gemini 2 demo of image editing, where I can put in an image and just request it and it does, that's how AI should work. Not like a whole bunch of complicated steps. So it really is something. And I think one frontier that we haven't [00:51:00] seen this year, like obviously video has done very well, and it will continue to grow.[00:51:03] swyx: You know, we only have Sora Turbo today, but at some point we'll get full Sora. Oh, at least the Hollywood Labs will get Fulsora. We haven't seen video to audio, or video synced to audio. And so the researchers that I talked to are already starting to talk about that as the next frontier. But there's still maybe like five more years of video left to actually be Soda.[00:51:23] swyx: I would say that Gemini's approach Compared to OpenAI, Gemini seems, or DeepMind's approach to video seems a lot more fully fledged than OpenAI. Because if you look at the ICML recap that I published that so far nobody has listened to, um, that people have listened to it. It's just a different, definitely different audience.[00:51:43] swyx: It's only seven hours long. Why are people not listening? It's like everything in Uh, so, so DeepMind has, is working on Genie. They also launched Genie 2 and VideoPoet. So, like, they have maybe four years advantage on world modeling that OpenAI does not have. Because OpenAI basically only started [00:52:00] Diffusion Transformers last year, you know, when they hired, uh, Bill Peebles.[00:52:03] swyx: So, DeepMind has, has a bit of advantage here, I would say, in, in, in showing, like, the reason that VO2, while one, They cherry pick their videos. So obviously it looks better than Sora, but the reason I would believe that VO2, uh, when it's fully launched will do very well is because they have all this background work in video that they've done for years.[00:52:22] swyx: Like, like last year's NeurIPS, I already was interviewing some of their video people. I forget their model name, but for, for people who are dedicated fans, they can go to NeurIPS 2023 and see, see that paper.[00:52:32] Alessio: And then last but not least, the LLMOS. We renamed it to Ragops, formerly known as[00:52:39] swyx: Ragops War. I put the latest chart on the Braintrust episode.[00:52:43] swyx: I think I'm going to separate these essays from the episode notes. So the reason I used to do that, by the way, is because I wanted to show up on Hacker News. I wanted the podcast to show up on Hacker News. So I always put an essay inside of there because Hacker News people like to read and not listen.[00:52:58] Alessio: So episode essays,[00:52:59] swyx: I remember [00:53:00] purchasing them separately. You say Lanchain Llama Index is still growing.[00:53:03] Alessio: Yeah, so I looked at the PyPy stats, you know. I don't care about stars. On PyPy you see Do you want to share your screen? Yes. I prefer to look at actual downloads, not at stars on GitHub. So if you look at, you know, Lanchain still growing.[00:53:20] Alessio: These are the last six months. Llama Index still growing. What I've basically seen is like things that, One, obviously these things have A commercial product. So there's like people buying this and sticking with it versus kind of hopping in between things versus, you know, for example, crew AI, not really growing as much.[00:53:38] Alessio: The stars are growing. If you look on GitHub, like the stars are growing, but kind of like the usage is kind of like flat. In the last six months, have they done some[00:53:4

Researchers uncover a large-scale hacking operation tied to the infamous ShinyHunters. A Dell Power Manager vulnerability lets attackers execute malicious code. TikTok requests a federal court injunction to delay a U.S. ban. Radiant Capital attributed a $50 million cryptocurrency heist to North Korea. Japanese firms report ransomware attacks affecting their U.S. subsidiaries. WhatsApp's “ViewOnce” feature faces continued scrutiny. SpyLoan malware targets Android users through deceptive loan apps. A major Romanian electricity distributor is investigating an ongoing ransomware attack. A critical flaw in OpenWrt Sysupgrade has been fixed. Contenders for top cyber roles in the next Trump administration visit Mar-a-Lago. On our Industry Voices segment, Jason Lamar, Cobalt's Senior Vice President of Product, joins us to share insights on offensive security: staying ahead of cyber threats. Google's new quantum chip promises scaling without failing.  Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest On our Industry Voices segment, Jason Lamar, Cobalt's Senior Vice President of Product, joins us to share insights on offensive security: staying ahead of cyber threats. Check out Cobalt's GigaOm Radar Report for PTaaS 2024 to learn more.  Selected Reading ShinyHunters, Nemesis Linked to Hacks After Leaking Their AWS S3 Bucket (Hackread) Dell Power Manager Vulnerability Let Attackers Execute Malicious Code (Cyber Security News) TikTok Asks Court To Suspend Ban Ahead of Supreme Court Appeal (The Information) Radiant links $50 million crypto heist to North Korean hackers (Bleeping Computer) US subsidiaries of Japanese water treatment company, green tea maker hit with ransomware (The Record) WhatsApp View Once Vulnerability Let Attackers Bypass The Privacy Feature (Cyber Security News) SpyLoan Malware: A Growing Threat to Android Users (Security Boulevard) Romanian energy supplier Electrica hit by ransomware attack (Bleeping Computer) OpenWrt Sysupgrade flaw let hackers push malicious firmware images (Bleeping Computer) Homeland Security veteran to be interviewed for Trump administration cyber role (The Record) Google claims ‘breakthrough' with new quantum chip (Silicon Republic) Share your feedback. We want to ensure that you are getting the most out of the podcast. Please take a few minutes to share your thoughts with us by completing our brief listener survey as we continually work to improve the show.  Want to hear your company in the show? You too can reach the most influential leaders and operators in the industry. Here's our media kit. Contact us at cyberwire@n2k.com to request more info. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

Award winning journalist and author, Vince Beiser, joins us again to talk about his new book; Power Metal: The Race for the Resources that will Shape the Future. The energy transition consumes immense amounts of critical metals and minerals. What are the trade-offs being made in mining and processing them? How and where is recycling of these metals ongoing? What are the human stories and impacts behind this enormous industry and what might the solutions be to make the transition more sustainable and achievable? Vince's book is out and available in all good book stores and published by Penguin Random House. 

The clean energy transition has a dirty underside. To move away from fossil fuels and toward solar, wind, batteries, and other alternative sources of energy, we have to intensify mining operations for critical minerals like lithium, copper, and cobalt. According to a Global Witness analysis of S&P Global data, copper mining will increase more than 25% between 2021 and 2028. Cobalt mining will be up more than 100%. Lithium, more than 300%. And all that mining has serious environmental and social impacts, particularly in developing countries. This week, host Bill Loveless talks with Vince Beiser about his latest book “Power Metal: The Race for the Resources That Will Shape the Future.” Vince is an author and journalist whose work has appeared in Wired, Harper's Magazine, The Atlantic, and The New York Times, among other publications. They discuss cleaning up the chase for critical minerals, advancing the clean energy transition while minimizing mining impacts globally, and what role the U.S. government can play, particularly with an incoming Trump administration.

Rick Howard, N2K CyberWire's Chief Analyst and Senior Fellow, turns over hosting duties to Caroline Wong, the Chief Strategy Officer at Cobalt to discuss the mechanics of writing a cybersecurity book about AI. References: Ben Smith. “Security Metrics: A Beginner's Guide” Review [Review]. Cybersecurity Canon Project. Caroline Wong, 2011. Security Metrics, A Beginner's Guide [Book]. Goodreads. Rick Howard, Caroline Wong, 2022. Interview with Author and Hall of Fame winner Caroline Wong [Interview]. Cybersecurity Canon Project. Rick Howard, 2023. Cybersecurity First Principles: A Reboot of Strategy and Tactics [Book]. Goodreads. Rick Howard. Security Metrics, A Beginner's Guide [Review]. Cybersecurity Canon Project. Learn more about your ad choices. Visit megaphone.fm/adchoices