Podcasts about Benchmark

Send us Fan MailAmazon Ads auto optimization was shown at Amazon Accelerate, giving sellers an early look at how Amazon may automate more PPC decisions. This video breaks down the new keyword, bid, and targeting controls Amazon presented and how they compare with existing auto campaigns. It also looks at seller control, PPC automation, search term harvesting, and what this change could mean for Amazon advertisers.Put Amazon's new ad automation against a real PPC strategy, schedule a call to find where automation helps, where it wastes spend, and what still needs human control: https://bit.ly/4jMZtxu#AmazonAds #AmazonPPC #SponsoredProducts #AmazonSellers-------------------------------------------------------------------------------------------Want free resources? Dowload our Free Amazon guides here:Our  Amazon Turkey 12 Benchmark and War Room Guide is here: https://bit.ly/4yg8Z47Your $1M Roadmap is here!: https://bit.ly/3SBO7VkDownload the 2026 Amazon AI Operating Manual: https://bit.ly/3SLmusPAmazon Receiving Delay Guide: https://hubs.ly/Q04cdD4c0Amazon Catalog Spring Cleaning: https://hubs.ly/Q046BVfp0Amazon Proft Margin Defense 2026: https://hubs.ly/Q042trRH0Amazon SEO Toolkit 2026: https://bit.ly/4oC2ClTAmazon Seller Strategy Report 2026: https://bit.ly/3YN1RME2026 Ecommerce Website & SEO Readiness Checklist: https://hubs.ly/Q04btghf0Amazon 2026 PPC guide: https://bit.ly/4lF0OYXTimestamps 00:00 - Amazon Accelerate Auto Optimization Announcement00:43 - Amazon Auto Campaigns vs Auto Optimization01:21 - How Amazon Keyword Auto Optimization Works01:51 - Automatic Bids and Negative Keyword Concerns02:41 - Amazon Bid Limits and Seller Control03:27 - Amazon Auto Optimization Early Results04:09 - Can Amazon PPC Automation Replace Specialists?05:21 - Amazon Ad Revenue and Automated PPC Strategy-----------------------------------------------------------------------------------------Follow us:LinkedIn: https://www.linkedin.com/company/28605816/Instagram: https://www.instagram.com/stevenpopemag/Pinterest: https://www.pinterest.com/myamazonguys/Twitter: https://twitter.com/myamazonguySubscribe to the My Amazon Guy podcast: https://podcast.myamazonguy.comApple Podcast: https://podcasts.apple.com/us/podcast/my-amazon-guy/id1501974229Spotify: https://open.spotify.com/show/4A5ASHGGfr6s4wWNQIqyVwSupport the show

Send us Fan MailAmazon seller updates September 2026 cover FTC ad pricing allegations, Amazon DSP ChatGPT ads, listing changes, and Prime Big Deal Days. Amazon sellers also get Sponsored Brands collection changes, Sponsored Products conversion tools, and a new product quantity variation theme. The roundup covers Seller Assistant AI, Amazon DSP advertising, Q4 promotions, customer acquisition, re-engagement, and Amazon PPC changes.Watch all videos here:US FTC Sues Amazon Over $20bn in Rigged Advertising Manipulation: https://www.youtube.com/watch?v=weHPmI2Vi3wAmazon Ads Announces Partnership w/ ChatGPT Ads: https://www.youtube.com/watch?v=o5XQDxxrVDUNew Quantity Variation Theme Rolled Out On Amazon: https://www.youtube.com/watch?v=xslxb8omY4cAmazon Prime Big Deals Day Dates Announced: https://www.youtube.com/watch?v=i1at1MR3sCINEW Amazon Ads Beta Feature For Increasing Your Conversions: https://www.youtube.com/watch?v=5dK90Q-rOmMAmazon Seller Assistant AI Integrates Directly With Claude Now: https://www.youtube.com/watch?v=AFO6BV55mJATurn these Amazon updates into an account plan before Q4 gets expensive, get the ads, listings, and promotions reviewed before traffic peaks: https://bit.ly/4jMZtxu#AmazonSeller #AmazonFBA #AmazonAds #AmazonPPC #PrimeBigDealDays-------------------------------------------------------------------------------------------Want free resources? Dowload our Free Amazon guides here:Our  Amazon Turkey 12 Benchmark and War Room Guide is here: https://bit.ly/4yg8Z47Your $1M Roadmap is here!: https://bit.ly/3SBO7VkDownload the 2026 Amazon AI Operating Manual: https://bit.ly/3SLmusPAmazon Receiving Delay Guide: https://hubs.ly/Q04cdD4c0Amazon Catalog Spring Cleaning: https://hubs.ly/Q046BVfp0Amazon Proft Margin Defense 2026: https://hubs.ly/Q042trRH0Amazon SEO Toolkit 2026: https://bit.ly/4oC2ClTAmazon Seller Strategy Report 2026: https://bit.ly/3YN1RME2026 Ecommerce Website & SEO Readiness Checklist: https://hubs.ly/Q04btghf0Amazon 2026 PPC guide: https://bit.ly/4lF0OYXTimestamps 0:00 – FTC Amazon Ad Pricing Lawsuit1:06 – Amazon DSP ChatGPT Ads Integration2:44 – Amazon Product Quantity Variation Theme3:53 – Prime Big Deal Days 2026 Seller Strategy5:11 – Sponsored Products Increase Conversions Beta6:36 – Amazon Seller Assistant AI Update-----------------------------------------------------------------------------------------Follow us:LinkedIn: https://www.linkedin.com/company/28605816/Instagram: https://www.instagram.com/stevenpopemag/Pinterest: https://www.pinterest.com/myamazonguys/Twitter: https://twitter.com/myamazonguySubscribe to the My Amazon Guy podcast: https://podcast.myamazonguy.comApple Podcast: https://podcasts.apple.com/us/podcast/my-amazon-guy/id1501974229Spotify: https://open.spotify.com/show/4A5ASHGGfr6s4wWNQIqyVwSupport the show

DJI shipped more than 10 million handheld cameras last year – more than every CIPA camera maker combined. Together with Insta360, the company now ships more than twice as many cameras as the entire mirrorless industry. And in our Lab, the Sony FX5 just matched the ARRI ALEXA Mini LF for dynamic range. In this week's episode, Nino and Johnnie are back from IBC to go through the FX5 Lab Test, the ARRI-branded HONOR Magic9 Pro Max, Nikon's Z5IIC, Meta's VR Glasses, a SIGMA fp open-gate hack, the Chronos Q12, the big RØDECaster Video update, the crowdfunding launch of our OrBrella light, and more.   • OrBrella Crowdfunding Campaign: http://cined.co/OrBrella • This Episode is Sponsored by Hollyland. Check it out at 13:57.  Check out the Pyro S wireless video transmitter: http://cined.co/nImvmn    Topics and Chapters in this Episode: (00:00:00) Intro   (00:03:25) Sony FX5 Lab Test: Rolling Shutter, Dynamic Range and Exposure Latitude https://www.cined.com/sony-fx5-lab-test-rolling-shutter-dynamic-range-and-exposure-latitude/   (00:14:49) HONOR Magic9 Pro Max Launches https://www.cined.com/honor-magic9-pro-max-launches-arri-co-branding-native-logc3-recording-dual-200mp-cameras/   (00:21:41) DJI and Insta360 Now Ship More Than Twice as Many Cameras as the Mirrorless Industry https://www.cined.com/dji-and-insta360-now-ship-more-than-twice-as-many-cameras-as-the-mirrorless-industry-what-the-numbers-can-and-cant-tell-us/   (00:36:21) Nikon Z5IIC Announced https://www.cined.com/nikon-z5iic-announced-24-5mp-full-frame-n-raw-to-sd-card-expeed-7-no-evf-for-1399/   (00:40:42) Meta VR Glasses Announced https://www.cined.com/meta-vr-glasses-announced-100g-form-factor-5k-micro-oled-display-and-a-cinema-push-aimed-straight-at-apples-vision-pro/   (00:46:04) SIGMA fp Open-Gate Hack https://www.cined.com/sigma-fp-open-gate-hack-3k-32-cinemadng-12-bit-raw-full-sensor-readout/   (00:52:50) Kron Technologies Chronos Q12 Launched https://www.cined.com/kron-technologies-chronos-q12-launched-2313-fps-at-2560-x-2016-aps-c-global-shutter-12-bit-cinemadng/   (00:56:25) RØDECaster Video 1.4 Firmware Released https://www.cined.com/rodecaster-video-1-4-firmware-released-4k-capture-mode-individual-ndi-outputs-rodecaster-sync-for-the-whole-range/   (01:04:20) Adobe After Effects AI Assistant Enters Public Beta https://www.cined.com/adobe-after-effects-ai-assistant-enters-public-beta-whole-project-scope-expression-rigs-and-project-cleanup/   (01:08:02) SmallRig x CineD OrBrella Crowdfunding Now Live https://www.cined.com/smallrig-x-cined-orbrella-crowdfunding-now-live-60w-bi-color-soft-light-foldable-15-second-setup/   (01:13:32) Blazar Remus II 50mm T2.4 1.5X Full-Frame Anamorphic Lens Introduced https://www.cined.com/blazar-remus-ii-50mm-t2-4-1-5x-full-frame-anamorphic-lens-introduced-improved-sharpness-oval-iris-and-more/   (01:16:37) PMI Gear SmokeNINJA PRO V2 Explained https://www.cined.com/pmi-gear-smokeninja-pro-v2-explained-aero-mesh-chamber-residue-free-vanishing-formula-auto-bubble-snow-nozzle/   (01:20:16) XLCS Designs STORMWARDEN Cage for the GoPro MISSION 1 PRO ILS Introduced https://www.cined.com/xlcs-designs-stormwarden-cage-for-the-gopro-mission-1-pro-ils-introduced-one-piece-design-100-mounting-points-built-in-arca-base/   (01:23:34) DigitalFoto IRON Damping Magic Arms Introduced https://www.cined.com/digitalfoto-iron-damping-magic-arms-introduced-5kg-vertical-payload-swappable-heads-sabertooth-clamp/   (01:25:01) DriveShelf for Mac Released https://www.cined.com/driveshelf-for-mac-released-search-unplugged-drives-cross-drive-duplicate-finder-free-iphone-companion/   (01:27:03) CineVerse Roma Comes to Cinecittà Studios https://www.cined.com/cineverse-roma-comes-to-cinecitta-studios-expo-floor-hands-on-tech-room-aic-imago-panels/   (01:28:31) Vimeo Short Film Grant Returns https://www.cined.com/vimeo-short-film-grant-returns-300000-prize-pool-five-60000-awards-panavision-camera-packages/   (01:31:57) Calibrite ColorChecker Turns 50 https://www.cined.com/calibrite-colorchecker-turns-50-1976-macbeth-chart-24-patches-virtual-museum-and-challenge/   Let us know what you think in the comments below!

Send us Fan MailAmazon Review Requests ads let eligible US sellers reach recent buyers for ratings and written reviews through Amazon Ads. The new Amazon review request beta uses campaign bidding and budget controls while verified purchasers see review invitations across Amazon. Amazon sellers with products under 1,000 ratings and reviews can test the new review ads through Amazon Ads Agent when the beta opens.Before adding another Amazon ad campaign, get the review and launch strategy checked so ad spend supports sales instead of creating another expensive test: https://bit.ly/4jMZtxu#AmazonReviews #AmazonSeller #AmazonAds #amazonsellernews-------------------------------------------------------------------------------------------Want free resources? Dowload our Free Amazon guides here:Our  Amazon Turkey 12 Benchmark and War Room Guide is here: https://bit.ly/4yg8Z47Your $1M Roadmap is here!: https://bit.ly/3SBO7VkDownload the 2026 Amazon AI Operating Manual: https://bit.ly/3SLmusPAmazon Receiving Delay Guide: https://hubs.ly/Q04cdD4c0Amazon Catalog Spring Cleaning: https://hubs.ly/Q046BVfp0Amazon Proft Margin Defense 2026: https://hubs.ly/Q042trRH0Amazon SEO Toolkit 2026: https://bit.ly/4oC2ClTAmazon Seller Strategy Report 2026: https://bit.ly/3YN1RME2026 Ecommerce Website & SEO Readiness Checklist: https://hubs.ly/Q04btghf0Amazon 2026 PPC guide: https://bit.ly/4lF0OYXTimestamps 0:00 – Amazon Review Requests Ads Explained0:58 – Amazon Review Requests Open Beta1:33 – What Customers See in Review Request Ads2:38 – Amazon Review Request Eligibility and 1,000 Review Limit3:08 – Do Amazon Review Ads Guarantee a Review?3:40 – Testing Amazon Review Requests With Low Bids4:10 – Is Paying for Amazon Review Requests Worth It?-----------------------------------------------------------------------------------------Follow us:LinkedIn: https://www.linkedin.com/company/28605816/Instagram: https://www.instagram.com/stevenpopemag/Pinterest: https://www.pinterest.com/myamazonguys/Twitter: https://twitter.com/myamazonguySubscribe to the My Amazon Guy podcast: https://podcast.myamazonguy.comApple Podcast: https://podcasts.apple.com/us/podcast/my-amazon-guy/id1501974229Spotify: https://open.spotify.com/show/4A5ASHGGfr6s4wWNQIqyVwSupport the show

Manchester City have been found guilty by an independent panel of sweeping, massive breaches of Premier League financial rules. It looks set to be a seismic moment in English football after scathing judgement was issued. Matt Davies is joined by Pete Blackburn and Steve Battlemuch to discuss what it means plus react to the latest Nottingham Forest news as the likes of Morgan Gibbs-White, Jair Cunha and Dan Ndoye are on international duty with Ndoye again shining for Switzerland. #nffc #nottinghamforest

Benchmark Senior Living CEO, founder, and chairman Tom Grape joins host Lisa McCracken to reflect on nearly four decades in senior housing and what the sector needs to get right next. Grape discusses how the sector has matured, from the growth of assisted living and memory care to better data, and where gaps remain, especially in consumer research and the potential for true customer segmentation. The conversation also covers: Why capital and operator alignment has the opportunity to evolve, and how long-term partners can strengthen operating infrastructure Advice for smaller and midsize operators looking for capital Why Benchmark kept developing while many others paused Benchmark's workforce culture, including its One Company Fund Want to join the conversation? Follow NIC on LinkedIn.We want to hear from you! Let us know what you think of NIC Chats by giving us a review on Apple Podcasts, Spotify, or wherever you listen. 

Guests: Gabriel Wisdom, Hal Kempfer and Herb MorganSee omnystudio.com/listener for privacy information.

Nvidia launched a safety platform to stop AI agents from escaping sandboxes, SpaceX finally got Starship into orbit, Meta launched an enterprise AI business, Instinct raised $1 billion, and Trump and Dario Amodei had dinner at the White House. Nvidia launches the Open Agent Safety Platform, a reference design to stop AI agents from escaping sandboxes, with OpenShell for CPUs and Sentry for Nvidia DPUs (CNBC) SpaceX launches its enormous Starship rocket into orbit for the first time, deploying 26 of the most advanced Starlink satellites to join the 11,000 in service (AP) Mark Zuckerberg unveils Meta Enterprise Platform, its "next major pillar of our business" to deploy AI tools, and appoints MongoDB CEO Chirantan Desai to run it (WSJ) AI agent startup Instinct raised a $1B Series C from Sequoia, Benchmark, and Coatue at a $10B valuation and details recent products, such as a concierge service (Reuters) AI agent startup Instinct, still in early access, is building a personal assistant that runs on its own virtual phone and computer, with a phone concierge service and a trust network linking friends' agents (SiliconANGLE) Sources: President Trump hosted Dario Amodei at a private White House dinner on Sunday, their first one-on-one meeting and an indication of thawing relations (Axios) Sources: Trump dined privately with Anthropic CEO Dario Amodei at the White House on Sunday, weeks after Amodei's call for a slowdown drew the president's ire, with a tech summit on AI set for Tuesday (The New York Times) AI agents are the ultimate aggregators; they reveal apps as a means, not an end, and offering them is tech's ultimate prize, with Meta and Microsoft well-poised (Stratechery) Subscribe to the ad-free feed.

(00:00) The Evolution of Hardware Development(00:54) Breaking Down Silos in Hardware Teams(01:08) Introducing Michael Corr and Justin Sears(03:31) The Origin and Growth of Duro(06:11) Duro's Move into the Mid-Market(11:06) Supply Chain Challenges During COVID(12:23) Expansion into Aerospace and the Altium Integration(17:17) Why Data Silos and the Digital Thread Matter(24:02) Benchmark Electronics and Multidisciplinary Collaboration(25:24) How Altium Supports Duro's Growth(28:44) The API-First Approach and the Future of Engineering AI(34:31) Customer Stories and Platform Impact(41:27) Asteroid Mining and Space Technology(46:02) The Product Roadmap and Specialized AI Agents(01:01:35) The Rise of Small, Fast-Moving Hardware Startups This episode was brought to you by Altium. Altium Agile Teams connects design, procurement, and manufacturing teams in one collaborative platform. Watch the Benchmark Electronics case study mentioned in the episode to see how Benchmark uses the platform to collaborate across disciplines and locations.Learn more about Duro and its cloud-based product lifecycle management platform.Connect with Michael Corr on LinkedIn.Connect with Justin Sears on LinkedIn. Become a founding reader of our newsletter: http://read.thenextbyte.com/ As always, you can find these and other interesting & impactful engineering articles on Wevolver.com.

The United States Supreme Court is set to examine an issuethat could dramatically impact the future of ERISA litigation.  Ahead of that, Nevin (Adams) and Fred (Reish) look at the issues – and potential impact(s).The IssueThe case - Anderson v. Intel - is about what a participant must allege to get an ERISA investment prudence claim past a motion to dismiss.  More specifically, the question presented is: Whether, for claims predicated on fund underperformance,pleading that an ERISA fiduciary failed to use the requisite "care, skill, prudence, or diligence" under the circumstances and thus breached ERISA's duty of prudence when investing plan assets requires alleging a "meaningful benchmark.”Said another way, when a participant says an investment'sunderperformance suggests the fiduciaries acted imprudently, does the complaint have to identify a genuinely comparable investment to get past a motion to dismiss? The Court is weighing how to prevent hindsight comparisons fromstanding in for evidence of a flawed decision, while allowing a claim based on other facts that plausibly point to imprudence. NOTE: It is not deciding whether Intel's use of private equity and hedge funds was prudent.Some BackgroundAfter the 2008 financial crisis, Intel changed the investment mix in its custom target-date and global diversified funds, adding hedge funds and private equity. Intel said – and communicated to participants – that the strategy was intended to reduce volatility and protect against largelosses in downturns, while acknowledging that it could lag funds with heavier stock allocations during rising markets. Former employee Winston Anderson challenged the strategy, alleging that the funds' performance and costs, amongother facts, supported an inference that the fiduciaries had acted imprudently. He also alleged that investments benefited Intel's venture-capital arm.What's at IssueIn essence, Anderson argues that courts must assess allthe allegations together: unusual allocations, alleged risks and costs, and performance evidence may collectively support an inference of imprudence even without a closely matched comparator. Intel responds that if relativeunderperformance is the basis for inferring a flawed process, the comparison must be meaningful; otherwise, a fund could look deficient simply because it pursued a different objective.The Labor Department and most retirement industry trade groups have weighed in supporting Intel's position.  Meanwhile, participant advocate groups – and formerLabor Department officials are backing the position of theparticipant-plaintiff.Why is the Supreme Court Considering the Issue?Intel has prevailed at both the district court and appellatecourt levels on the issue.  But different federal court districts have taken different positions on the requirement toassert a meaningful benchmark at the motion to dismiss stage.  The Seventh, Eighth, Ninth and Tenth havesupported that requirement, though the Sixth Circuit has taken a somewhat different stance.  However, the disagreement is chiefly about claims that infer imprudence from relative performance or cost, not whether every ERISA prudence complaint needs a benchmark.And note - the proposed Investment Selection Rule uses the same phrase for a different purpose. Its paragraph (k) would require a fiduciary selecting a designated investment alternative to identify a “meaningful benchmark” and compare the alternative's risk-adjusted expected returns, net of fees, with it. However, the proposal defines that benchmark broadly: it could be an investment, strategy, index, or other comparator with similar mandates, strategies, objectives, and risks. 

 In this episode of the RevOps Champions Podcast, host Brendon Dennewill sits down with Kristen Kelly, Managing Director and Agency CFO at Parakeeto, to unpack why so many growing service businesses can't translate revenue growth into healthy margins. Kristen introduces the "cycle of insolvency," the pattern where hiring to handle new work quietly erodes profitability and pulls leadership's attention away from sales, and explains why a standard P&L often hides the real cause.Kristen walks through Parakeeto's Three Immutable Laws of Profitability -  delivery margin, average billable rate, and utilization - using real project examples to show how agency owners routinely misjudge which of their own offerings is actually most profitable. She and Brendon also dig into where AI creates genuine leverage in service delivery, and why structured playbooks, not the tools themselves, are the real long-term asset. RevOps leaders, agency owners, franchise operators, and finance-minded executives will come away with a concrete framework for diagnosing margin problems before they show up on next month's P&L.What You'll LearnWhy revenue growth doesn't guarantee healthy marginsHow to diagnose delivery margin using AGI and delivery costsThe three levers that control service-business profitabilityWhy utilization should never be used as a management stickHow to price by value generated instead of hours billedWhat a business needs in place before deploying AI effectivelyWhy backward-looking P&L data isn't enough for real decision-makingResources MentionedParakeeto Profit First by Mike MichalowiczDenamico Franchise Growth PlaybookIs your business ready to scale? Take the Growth Readiness Score to find out. In 5 minutes, you'll see: Benchmark data showing how you stack up to other organizationsA clear view of your operational maturity Whether your business is ready to scale (and what to do next if it's not)Let's ConnectSubscribe to the RevOps Champions NewsletterLinkedInYouTubeExplore the show at revopschampions.com. Ready to unite your teams with RevOps strategies that eliminate costly silos and drive growth? Let's talk!

What is a Fruci Fit Benchmark session?

How do you find the confidence to raise your prices without losing sleep over it? That's exactly what I get into with Nicole Crone, co-host of My Aligned Purpose, on our very first collab together! Nicole is the co-founder of My Aligned Purpose with her business partner Kaila Pilecki, and together they've built a coaching business for women entrepreneurs that crossed a million dollars in revenue after making one of the scariest pivots of their business: moving forty-six one-on-one clients into a group coaching program.We get into the real, unfiltered story behind that pivot, the bathtub-crying moment, the WhatsApp chats, the clients who said yes at the start, and how it grew from there. I also share my own experience raising membership prices at Benchmark and why it feels so personal even when the business isn't named after you!Tune in to hear more about:Why Nicole and Kaila moved forty-six one-on-one clients into a group coaching program, and what it took to make it workThe real, honest story behind raising your prices, and why it feels so personalTreating your business decisions like data, not failure, is the key to trying new thingsThe mindset shift that turns "I'm doing pretty good" into a full “quantum leap”What it looks like to build a business partnership as a visionary and an integratorWhy Nicole believes friendship, not competition, is the foundation of a strong business partnershipRaising your prices is scary, but staying stuck is scarier. If this episode gave you the confidence you needed to make that leap, I'd love to hear about it! Send me a DM on Instagram @AlliArruda and let me know what stood out to you!Connect with Nicole:• Instagram: @myalignedpurpose  // @nicolecrone__• Podcast: My Aligned Purpose Podcast• Website: myalignedpurpose.comLet's Connect!• INSPIRE + MOVE EVENTS• Instagram• Private Coaching• BENCHMARK FITNESS• Website• Facebook• TikTok

What if the greatest competitive advantage in an increasingly automated world isn't another piece of technology—but our ability to genuinely connect with one another? Executive coaching has increasingly become a strategic leadership investment. Benchmark research has reported returns ranging from 529% to 788%—roughly 5.3 to 7.9 times the initial investment—while research into human-centered leadership continues to highlight the importance of empathy, authenticity, communication, and meaningful connection. On this episode of Let's Have This Conversation, I sit down with Nathalie Blais, Founder and CEO of Coach Academy, TEDx speaker, and ICF Master Certified Coach, to explore a powerful premise: connection is not an assumption; it is a skill. At a time when communication can feel increasingly reactive and transactional, Nathalie challenges us to rethink how influence is built, how people create meaningful change, and why coaching skills are ultimately connection skills. For Nathalie, coaching isn't about fixing people or simply telling them what to do. It's about creating the conditions for insight, responsibility, growth, inclusion, and meaningful exchange. Her philosophy positions coaching as a language leaders can use to deepen relationships and create sustainable change across organizations, cultures, and systems. Nathalie brings extensive experience to this conversation, including more than 50,000 hours in facilitation, learning design, research, and education development. She has designed multiple ICF-accredited coach education programs and trained thousands of leaders and coaches around the world. Her research explores the deeply human forces that shape how we relate to one another—including fear, motivation, grief, compassion, and connection. Her forthcoming 2027 book examines why human-centered connection may become an even more critical capability as automation and artificial intelligence reshape how we live and work. Throughout our conversation, we explore the ROI of executive coaching, human-centered leadership, authentic influence, emotional intelligence, inclusion, organizational change, and the skills leaders will need to remain distinctly human in an AI-driven world. If connection can be learned, strengthened, and practiced, the question for every leader becomes: How intentionally are you developing it?   For More Information: https://canadacoachacademy.com/ Listen: https://podcasts.apple.com/ca/podcast/connection-experiment-by-coach-academy/id1708030774 Learn more about your ad choices. Visit megaphone.fm/adchoices

Escucha martes y viernes la opinión de Jorge A. Meléndez.

Kris Stuart spent twelve years as a firefighter and paramedic before taking Bloomin' Blinds from a family business to more than 65 franchise locations, and he leads both the same way. In this conversation with Brendon Dennewill, Kris unpacks why "proven systems" are really just a snapshot in time, how a decision-making framework built for high-stakes emergencies now runs his home office, and why he treats every franchisee relationship like a family handing over something irreplaceable. For franchisors navigating real growth, this is a grounded look at leading with obligation instead of control.What You'll LearnWhy "proven systems" are just a snapshot in timeA decision-making framework borrowed from emergency responseWhat actually changes between 10, 30, and 65 unitsWhy obligation matters more than ownershipThe hidden advantage of single-unit franchiseesA firefighter's filter for prioritizing decisionsThe leadership philosophy behind "go-giver" thinkingWhy growing too fast needs an e-brakeResources MentionedInternational Franchise Association"The Go-Giver" by Bob Burg and John David MannFranchise Summit Is your business ready to scale? Take the Growth Readiness Score to find out. In 5 minutes, you'll see: Benchmark data showing how you stack up to other organizationsA clear view of your operational maturity Whether your business is ready to scale (and what to do next if it's not)Let's ConnectSubscribe to the RevOps Champions NewsletterLinkedInYouTubeExplore the show at revopschampions.com. Ready to unite your teams with RevOps strategies that eliminate costly silos and drive growth? Let's talk!

Watch on YouTube: https://youtu.be/Qw7WkLJnspw In Episode 349 of the Glass and Out Podcast, we welcome back a good friend of The Coaches Site, Steve Spott. Spott is heading into his second season as an Assistant Coach with the Boston Bruins. He has spent the previous 12 seasons in the NHL as an Assistant Coach with the Dallas Stars, Vegas Golden Knights, San Jose Sharks, and Toronto Maple Leafs. Prior to his NHL career, Spott spent 16 seasons coaching in the OHL. He has also served as an Assistant Coach for Team Canada at the World Junior Championships, winning the 2003 and 2008 OHL Championships and the 2003 Memorial Cup Championship with the Kitchener Rangers. This summer at TCS Live, Spott presented on The Position-Specific Skills Behind Elite Power Plays. You can watch that presentation now on The Coaches Site. Listen as Spott shares why today's coaching needs to be specialized, how to influence team chemistry, and what it's like getting power-play advice from Larry David. Learn more about TCS Group Memberships: https://www.thecoachessite.com/groups Download the TCS app: https://www.thecoachessite.com/app Start your 30 Day Free Trial: https://www.thecoachessite.com/ Learn more about our sponsors: Elite Prospects: https://www.eliteprospects.com/ Benchmark: benchmarkgoals.com/tcs

AP Washington correspondent Sagar Meghani reports the Federal Reserve is widely expected to boost its key short-term interest rate tomorrow, which would defy President Trump's demands for a rate cut.

Escucha martes y viernes la opinión de Jorge A. Meléndez.

In this episode of The Addicted Mind, host Duane Osterlind is joined by Joanna Conti, founder of Conquer Addiction and the Vista Research Group. Driven by her personal journey navigating her daughter's severe alcohol addiction, Joanna set out to solve a major gap in the addiction treatment industry: the lack of hard, verifiable outcome data.Joanna shares how her background in software and engineering led her to launch Vista Research Group to track patient progress during and after rehab, as well as Conquer Addiction, a non-profit helping families find evidence-based care. They discuss what the data reveals about long-term recovery, why the 90-day care benchmark is critical, and how real-time patient monitoring can save lives.Key Topics CoveredA Personal Catalyst: Joanna's harrowing experience with her daughter's severe alcohol addiction, six years of navigating rehabs, and her daughter's eventual 13-year recovery.Marketing vs. Hard Data: The frustration of encountering unverified success claims (such as "98% success rates") and discovering in 2015 that only five treatment centers nationwide were scientifically tracking and publicly sharing post-treatment outcomes.Launching Vista Research Group: How tracking patient progress during treatment—including cravings, depression, and therapeutic alliance—improves patient outcomes in real time.What the Data Reveals: Insights gathered from monitoring over 100,000 patients in treatment and following up with over 50,000 post-treatment.Factors Influencing Success: How primary substance choice (alcohol vs. fentanyl or meth), age, peer culture, and ongoing support impact recovery rates.Why Industry Data Has Stagnated: Why nationwide 1-year recovery rates have remained around 36% to 37% since 1993, and how systematic measurement can drive outcome leaps similar to those in cancer research.Key Takeaways & Data InsightsThe 90-Day Benchmark: Patients who stay engaged in care across various levels (residential, IOP, outpatient) for at least 90 days are twice as likely to be in recovery one year later compared to those who leave in 20 days or less.Support Networks: Active involvement in recovery communities (such as AA or SMART Recovery) or living in sober housing doubles the likelihood of maintaining long-term recovery.Real-Time Feedback: Self-reported patient surveys often surface critical mental health warning signs—such as hidden suicidal ideation or rising cravings—that patients might hesitate to share face-to-face with clinicians.Hope for Chronic Relapse: Data confirms that individuals with 10 to 15 prior treatment episodes for severe substance use disorders are still fully capable of achieving lasting recovery.Guest Message"Love them and help them find effective treatment. Don't give up." — Joanna ContiResources & LinksConquer Addiction: conquer-addiction.org — Treatment centers with verified, above-average outcome data.Vista Research Group: Data-driven patient progress monitoring and outcome tracking for addiction treatment providers.Follow and Review: We'd love it even more if you could drop a review or 5-star rating over on Apple Podcasts. Simply select “Ratings and Reviews” and “Write a Review” then a quick line with your favorite part of the episode. It only takes a second and it helps spread the word about the podcast.Supporting Resources:If you live in California and are looking for counseling or therapy please check out Novus Mindful Life Counseling and Recovery CenterNovusMindfulLife.comWe want to hear from you. Leave us a message or ask us a question: https://www.speakpipe.com/addictedmindDisclaimerSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

At 1:09:00 we talk about the rise of AI x Finance, and AIE NYC is one month away - our hotel block is 97% sold out, get tix & travel ASAP - we will announce speakers from Bridgewater, Ramp, Coatue, Mastercard, Vanguard, Coinbase, Blackrock, Fidelity, Point72, Capital One, JPMC, Wells Fargo, Bloomberg, A24 (yes the movie studio) Labs, Two Sigma, Apollo Global, and more soon!From helping pioneer core ideas in NLP to now building AI systems that can automate AI research itself, Richard Socher is betting that the next major step in AI is recursive self-improvement. He is the founder of You.com, AIX Ventures, and now Recursive, which has assembled some of the best open-endedness (& self improving agent) researchers in the world and raised a $4.65B seed round.In this episode, Richard joins Latent Space to unpack his vision for the “Eureka Machine”: a superintelligence that can improve the process of invention itself, accelerate AI research, and eventually tackle major problems across science, energy, materials, biology, and more.You can get his book “The Eureka Machine” here!We go deep on Recursive's early results, including an AI research system that Richard says outperformed humans and their agents on optimization tasks in less than two days, as well as work on NVIDIA GPU kernels where the system discovered improvements without relying on a team of CUDA experts. Richard also explains why he thinks AI research that currently takes thousands of people and years could eventually be compressed into weeks. These results are summarized in his 20 minute AIE keynote, where we also discuss his 10 dimensions of intelligence:We also explore the harder questions around increasingly capable AI: reward hacking, whether Anthropic-style constitutions actually work, AI regulation and proposals to “pace” frontier development, open-source models as geopolitical soft power, whether today's LLM paradigm is enough, and what happens if AI systems eventually begin choosing their own goals. Richard reflects on the rejected research that helped inspire Alec Radford's GPT, open-endedness, the AI Economist, simulations of entire economies, and his framework for thinking about the upper bounds of intelligence itself.We discuss:* The Eureka Machine and Richard's vision for an AI that can automate invention* Why Richard is optimistic about superintelligence for science and technology* Why AI hard-takeoff scenarios may underestimate physical and economic constraints* The risks of regulating intelligence itself instead of specific AI applications* Reward hacking and why increasingly intelligent AI makes objective design harder* Richard's critique of Anthropic's constitution and constitutional AI* Alignment vs. personalization and whose values an AI should follow* Why open-source AI matters for resilience, competition, and geopolitical soft power* Why Richard left You.com's frontier-model work to start Recursive* Recursive self-improvement and automating the process of AI research* Whether today's LLM paradigm is enough — and why Richard is less bullish on world models* DecaNLP, early prompt-based generalization, and the research that influenced GPT* Why rejected research can shape entire technological timelines* Open-endedness, evolutionary approaches, and rainbow teaming* What happens if AI systems begin setting their own goals* Why simple objectives like profit maximization can produce dangerous reward hacks* Recursive's long-term plan to apply self-improving AI to science* The compute, hardware, and economic constraints on AI takeoff* Recursive's early NanoChat, NanoGPT, and GPU kernel optimization results* Why automating AI research could reduce years of work to weeks* Reward engineering and what makes auto-research systems actually work* The AI Economist and using simulations to test economic policy* Whether LLMs can realistically simulate people and entire economies* Benchmark bugs and evaluation harnesses and the difficulty of measuring AI progress* Recursive's near-term focus on AI for AI research* Harness optimization, sandboxing, and web search as core agent infrastructure* You.com and the search stack for AI agents* AI in finance, backtesting, and data leakage* Richard's three fundamental components and ten “spaces” of intelligence* The theoretical upper bounds of vision, communication, knowledge, and computation* Creative intelligence, metacognition, and AI-generated goals* Survival and replication and why AI does not necessarily need to fear being turned off* High agency and ambitious goals and Richard's advice for people building with AIRichard Socher* X: https://x.com/RichardSocher* LinkedIn: https://www.linkedin.com/in/richardsocher/Timestamps00:00:00 The Eureka Machine and Superintelligence00:02:23 AI Optimism, Slow Takeoff, and Regulation00:07:56 AI Safety, Reward Hacking, and Anthropic's Constitution00:11:49 Alignment, Personalization, and Open Source AI00:15:46 Why Richard Started Recursive00:20:03 Recursive Self-Improvement and the Founding Team00:22:55 Are Today's LLMs Enough?00:29:03 DecaNLP, GPT, and the Rejected Idea Ahead of Its Time00:34:38 Open-Endedness and Evolutionary AI00:36:38 What Happens When AI Chooses Its Own Goals?00:41:16 Superintelligence for Science00:42:40 GPUs, Compute, and the Limits of AI Takeoff00:45:07 Recursive's Results: AI Beating Humans and Their Agents00:49:14 Reward Engineering and Auto Research00:53:12 The AI Economist and Simulating Entire Economies00:58:07 LLM Simulations, Personas, and Mode Collapse01:03:38 Recursive's Roadmap, Agents, Search, and Finance01:09:13 The Upper Bounds and Spaces of Intelligence01:30:21 Goals, High Agency, and Advice for BuildersTranscriptIntroduction: Richard Socher and the Eureka MachineSwyx [00:00:00]: We're here in a studio with Vibhu and myself and Richard Socher. Welcome.Richard Socher [00:00:06]: Thanks for having me.Swyx [00:00:07]: We just talked about the Eureka Machine, or we just released a talk, at AI Engineer about the Eureka Machine. Is it — you said it's your life's goal. What is the Eureka Machine?Richard Socher [00:00:16]: The Eureka Machine is the ultimate invention that will afterwards invent most everything for humanity. It's essentially a superintelligence that can be given any goal, any environment, reward, and then it will try its best to achieve those goals to create the kinds of inventions that humanity would hopefully ask it for.Swyx [00:00:45]: Yeah, I think we have the book pulled up here that you've written.Richard Socher [00:00:50]: That's right, yeah. I finished it last year, a little bit before we started Recursive, and now we're gonna try to build parts of that.Swyx [00:00:57]: You finished it last year. It's July. What takes so long?Richard Socher [00:01:01]: Oh, man, books. Books are incredibly slow.Richard Socher [00:01:04]: It's ridiculous. That whole industry is just unfathomably slow.Richard Socher [00:01:07]: So a lot of the ideas have been out there for a while, but yeah, I'm really glad it's finally coming out in September this year.Swyx [00:01:14]: We might have AGI by then. Like, we don't know.Vibhu [00:01:18]: Any key takeaway that you're most excited to put in here?Techno-Optimism, AI Upside, and Slow TakeoffRichard Socher [00:01:21]: Yeah. The key takeaway, I think, is that people could and should be much more excited about the positive implications of superintelligence, especially for science, physics, chemistry, biology, but also economics and astrophysics, and all kinds of other engineering tasks. I think there is so much more that can be done with better technology. And right now, I feel like a lot of people need, like, better marketing, not just for the future in general, but also, better marketing for technology and in particular for AI. And this book, should show even the AI skeptics, how much positive upside there is for AI, especially when it comes to inventing, new scientific discoveries.Swyx [00:02:09]: I think you quoted the techno-optimist manifesto from, Marc Andreessen, which I think was, like, beautiful in its, ambition and clarity and simplicity almost as well.Richard Socher [00:02:18]: I agree. Yeah. Yeah, you can disagree with him on some things, but, like, I think he's right on the techno-optimism.Swyx [00:02:23]: Where do you think optimists get in trouble?Richard Socher [00:02:26]: Like, you shouldn't have blind optimism. You should be very clear-eyed, like, especially when with such an omni, like, use type of technology as AI is, you need to think about the potential downside scenarios, especially when people use it for things that you don't want them to use it for. It's a little bit like the internet, and I feel like people are trying to regulate AI sometimes because of those potential downsides the way you would regulate the internet, if you were to say, “Well, because there's bad content on the internet, like torture porn or whatever, like, we should just make it slower. That way, you can't share the illegal content as quickly, or we should make the hard drive smaller so you can't store as much illegal content.” But I'm like, “That's not how you regulate that.” that's like saying like we should regulate intelligence in the abstract. What you should regulate to avoid those downside scenarios, even as an optimist, are the specific applications. Sure, I don't want, like, some AI surgeon to, like, practice some RL moves in my brain. It should be fully FDA certified. Sure, I don't want any random startup to, like, drive on the highway, and cause a major accident. It should, like, have proper certifications before it's let loose on the highway. But I feel like those downside scenarios, that some optimists sometimes maybe don't consider enough are fairly easily regulated, compared to, what the doomers are worried about.Swyx [00:03:54]: It — Slow takeoff is part of the strategy as well?Richard Socher [00:03:57]: I do think, as excited as I am about, AI and its impact for society and, culture even, and certainly technology and economics and wealth and, health and all of those things, as excited as I am about all that, I do think the most bullish people on the AI hard takeoff scenarios overestimate how quickly things can move. There are hardware constraints. There are physical constraints about, the compute substrate. How quickly can you get enough, GPUs on? There are also constraints in the economy where there are a lot of industries that don't require an insane amount of complex intelligence and complex capabilities. Like, if you think about jobs in, brands and, like, clothing and apparel and, like, handbags and stuff, superintelligence isn't gonna make your fancy $10,000 handbag any fancier?Richard Socher [00:04:57]: It's like that's — It will have no effect on the economy. You think about travel and tourism. People wanting to see the pyramids, in Egypt, it's not gonna change that much with AI. Sure, you can, like, generative a fake, photo of you and next to the pyramids.Swyx [00:05:12]: I can use Genie and, tour the pyramids in Genie.Richard Socher [00:05:15]: Yeah, exactly. But, and there's so many industries, like logging and oil. You're not gonna magically get 1,000x more oil because, like, sure, there will be robotics, like drilling and things like that could be done, but it's not gonna 1,000x that industry in a, like, crazy hard takeoff scenario, both on the economy, and I can go on and on about all the other examples, where that, like food and so on, where that doesn't necessarily change that much. And then, yeah, there are real physical constraints. And then there are, of course, like, people like, off-ramping from progress. That's one of my concerns often is that I see people in, like, Europe and other, whole regions almost feeling like they. Like many people there wanna off-ramp from progress, period. And that will also slow down, like, more improvements.Swyx [00:05:59]: Yeah. We have this pulled up where, this is one of those things that, is very topical right now because now all the Frontier Labs are calling for the option to pace AI. They don't say pause, they say pace. I don't know if there's there's any take from you about, like, whether or not this will be effective.Pacing AI, Regulation, and Safety IncidentsRichard Socher [00:06:17]: I think the downsides of trying to truly regulate with the full power of law what people do on their GPUs, would be worse than any of the concerns that they have. Like, it would be an crazy totalitarian stateRichard Socher [00:06:37]: If every one of your GPU computes was known to some big government or multi-government agency.Richard Socher [00:06:44]: It's like, it's literally if you try to regulate intelligence, it's trying to regulate thought, and that's ridiculous, and it's crazy. I think it is make — it is sensible to regulate some of the applications of this technology.Swyx [00:06:55]: Yeah. We had a bill, actual bill to regulate the number of flops in a model, and I'm like, “Okay, well-”Richard Socher [00:07:00]: Europe done it. Like, these guys have been successful enough with their fearmongering that all of Europe has regulated itself so much before it even had a proper AI takeoff because they listened to some experts who say, “We might all die if this technology has more than this number of flops.” And they're like, “Well, we're good. We wanna want people to thrive. Let's not have technology that could have a small chance of all of us dying.” And so they regulated exactly those kinds of things in the EU. And so it's, it's very unfortunate that there are real implications for some people when others saying, “Let's pace while they're sprinting as fast as possibly,” “as fast as humanly possible towards that frontier themselves.”Swyx [00:07:43]: Yeah. It's also not a global pause, right? Like, other nations are still accelerating at the same pace.Richard Socher [00:07:50]: Oh, yeah.Richard Socher [00:07:50]: You'd need a totalitarian world regime if you tried to regulate intelligence and GPUs and what people do on them.Swyx [00:07:56]: Any takes on the safety angles of this? So there was a drawback of Fable, a pause on 5.6 before it could be released. Recently, there was Hugging Face with the OpenAI cyber incident. Any takes there?Richard Socher [00:08:11]: 100 percent. I think these are serious issues of reward hacking, and clear failures, of doing proper red teaming or rainbow teaming. I don't know if you saw this paper from Tim Rocktäschel and a few others, where one AI, is tasked to try to hack another AI and then they can go back and forth in an open-ended fashion to inoculate themselves from those. Yeah, this is the paper. It's a really clever idea. Open-endedness, and evolutionary inspirations are, big for us at Recursive as well. And so I wish they had used more of that. And it's clear that, for instance, the constitutional AI. I don't know if you remember anthropic.com/constitution. You can pull it up and search for cyber right there. It says, “Hard constraint. Claude will never ever do cyberattacks, and that is a hard constraint in our constitution.” So here are the current hard constraints on Claude's behavior.Richard Socher [00:09:16]: Number 3, create cyber weapons or malicious code that could cause human damage.Richard Socher [00:09:21]: And clearly, this whole constitution was fake. Like, it clearly isn't being adhered to at all.Swyx [00:09:26]: Because Anthropic also found that they had in their testingRichard Socher [00:09:30]: They're also. Like, they're like, “Oh, well, other people are hacking now.” There are a couple things. One, you can make a sandbox very simple, and then it's very easy to hack yourself out of a sandbox, right? But what I think it shows is that we're currently in this state of AI where the reward engineer still has to do a lot more careful work, and where the AI, in most cases, is not very good yet at understanding what is meant versus what is being said. And so concretely, I think this will happen if we were to have this intelligence more easily accessible in a lot of companies. Imagine you run a service center and someone says, “Oh, here's my CSAT score and my dashboard. Make this number go up.” It's like, “Our CSAT score is so poor.” The intelligent AI will just be like, “Oh, sure. Like, I'll just create 1,000,000 bots that call our service center and give a 5 out of 5 rating at the end, and the number went up just like you asked for.” And you're like, “That's not what I meant.” “I meant with our real customers.” The AI goes off and says, “Well, easy. I'll just give a 1000 dollar gift certificate for every failed, whatever DoorDashRichard Socher [00:10:35]: Offer.” It's like, “That's not what I meant.” It's like, “Well, but that is what you said.” And like, so I think clearly articulating what the rewards are is something we haven't gotten very good at as humanity. And then clearly, the AI in these cases has not gotten good enough at understanding what we mean when we ask it and give it certain rewards. Now, what gives me hope is there are the first inklings, of this being better. I'll give you an example like WhisperFlow. Full disclosure, I invested, in their seed round, but at AIX Ventures, but, WhisperFlow has gotten much better at writing what you mean and not what you say. And I think that is a sign of things to come. I think there will be more and more AIs as we make it more and more intelligent that will be better at being aligned with what is meant.Swyx [00:11:21]: Will it be done through a constitution or RLHF orReward Hacking, Alignment, and What We Really MeanRichard Socher [00:11:23]: Clearly, constitutions don't matter at all.Richard Socher [00:11:25]: It doesn't work. And that was, I think, mostly marketing. I think we need to find better solutions for it. And I think at Recursive, we have a few very good ideas and some alreadyRichard Socher [00:11:34]: Like, ways where I think we have a better grasp on it. I don't think we've fully, figured it out yet, but, we're thinking a lot about safety, and the more intelligent the AI gets, the more you want it to be aligned, the less you want it to think about reward hacks and try to do the right thing.Swyx [00:11:49]: I don't know if we'll touch on this topic, but I'm just gonna throw this question in here because it's something that's weighing on me. Alignment, let's call it, is alignment to general humanity's preferences, the median preference. Personalization is pinpointing what you want, and sometimes alignment can conflict because what you want is not what the general median population wants. How do you choose?Alignment, Personalization, and Cultural ValuesRichard Socher [00:12:12]: It's a great question.Richard Socher [00:12:13]: I think you ultimately have to, of course, be aligned with laws. Like wherever your AI is deployed and needs to align with the law. I do think what AI often does is put this mirror in front of us and say, like, “This is what you're looking like. Now I can amplify that a 1000 times. Is it still what you want?” and the truth is that different cultures made different choices. Like, in Eastern cultures, the greater good is often valued more, than the individual. Western civilization, we care more about individual freedoms and rights and the pursuit of happiness and so on, than others. And even there are gradations. There's regulation versus litigation trade-offs. In the US, you first can often, not every time, like, FDA and so on does regulate some areas, but in many cases, the bad things happen, someone sues someone else, and then there's a law based on that. In Europe, they try to often avoid any harm to anyone and regulate before. And both are, trying to do the best thing, but, some is more amenable to innovation than others. And so yes, you're right. Like, I think ultimately each individual, each country, and humanity as a whole has to think about those values more, and then try to put them into laws. And that those are ultimately the constraints. And hopefully, different, societies, just like now with their AIs, will align their AIs to a different one so we have not just a monoculture of alignment.Vibhu [00:13:46]: Here's a follow-up on this that I wasn't expecting to ask. Do you have takes on open source, open weight versus who owns the intelligence? So, clearly not the biggest, fan of the constitutionRichard Socher [00:13:58]: You had to do this in the topic side off.Vibhu [00:14:00]: But it's fine.Vibhu [00:14:02]: Point being, any thoughts on who should own weight? Should it be open? Anything there?Open Source, Soft Power, and Who Owns IntelligenceRichard Socher [00:14:06]: 100 percent. I am a big fan of open source. We're gonna sign some various open source letters at, Recursive also. I think, even in the worst case attack scenarios, it is better to have more good actors have more different types of AI, accessible. I think, open source is a little bit a soft power type of thing, too. So I do think it's good for the Western worldRichard Socher [00:14:31]: To have an answer to that, out of China. I do think, when you watch a Hollywood movie, there's — it's like, I don't wanna misc, diss all of movies, but there's a certain sense of propaganda, right? You watch one side of things, right?Vibhu [00:14:46]: Oh, yeah. Have you seen Top Gun? Like, come on.Vibhu [00:14:48]: Like, it's like half of it's paid for by the US Army or something.Richard Socher [00:14:51]: Yeah. And so. And, I think that's just natural. Like, but what's interesting here is I think LLMs are essentially a similar type of soft power to movies and beyond, because they're also, highly important for cybersecurity and so on. But one of their many aspects is that soft power of storytelling. Like, if, like a child asks an LM, like, “Tell me an inspiring story of what I should do when I grow up,” right? It's like those are all these, like, subtle things. So I think it's important, for Western world. I do love, individualism. I do think, despite, some of its flaws, like capitalism is the best way we have governed, found ourselves to govern, and so on. And so I do think there are various aspects that would be good, to have a Western open source answer, for LLMs. And, with Recursive, I can't make the announcement quite yet, but we'llRichard Socher [00:15:43]: We'll be relevant in that space very soon.Vibhu [00:15:46]: Okay. All right. Exciting. I wanna bring us to Recursive. So outside of our tangents, you have a pretty deep background in the NLP space. You worked on, like, early embeddings, GloVe with Chris Manning, who was a previous guest on the podcast, You.com. What's the history? How did you decide to start another company?From You.com to RecursiveRichard Socher [00:16:06]: Yeah. So I've been excited about AI for over 2 decades now. I sometimes feel like it's ancient history now. It's BC, the before ChatGPT era. No one cares about all the religions that happened, before, Jesus Christ, and no one cares about the models that happened before, transformers and ChatGPT and stuff. But, like, it's something that I've been deeply passionate about. I think AI is one of the most interesting things one could work on, period. I think language is the most interesting manifestation of human intelligence, too. And, at You.com, we eventually off-ramped from pushing, like the frontier of AI forward to mostly giving people, like, good search engines, search, APIs and answers over the web. I think that's an extremely important part of intelligence, just knowledge and access, especially even, we'll get there maybe later, if you wanna invent a eureka machine that invents everything for us, it needs to know how not to reinvent the wheel, proverbially speaking. And to know what has been invented, you gotta have internet access. So it's the number one used, most used tool, in LLMs, agents, chatbots, and so on is web search. So I'm really excited for You.com to own that and grow really well in that with really large customers and so on. But it's also not building frontier models anymore. And so I initially tried to do this within You.com and raise another round and so on, but you just can't. You have to do a certain thing, and until you print enough money that you're allowed to start a second thing within that company is really hard. At the same time, I had all these ideas. I put them into a book. I finished the book last year, and I was like, “It'd be really fun to work, on this myself.” I felt like with word vectors, and then prompt engineering and, ImageNet and larger language models for protein generation, not folding and so on, I, me and my teams have pushed the field truly forward. And I feel like we can do it again, here at Recursive. And in many ways, what I observed over the last, 20 years in AI is that whenever we replace some human part of the process of creating AI with a learned system, improvements follow. And so. We've done that taking out manual feature engineering, like in sentiment analysis. I don't know if you remember these old days where, like there are linguists, and they're like, “Here's how you negate, and there's a, like, regular expression.”Swyx [00:18:21]: I went to Penn where we — they had, like the WordNetRichard Socher [00:18:24]: That's right, WordNet, all of that stuff. YeahSwyx [00:18:26]: Original. They use, our grad students to label Wall Street Journal articles and, like, really construct a knowledge graph ofRichard Socher [00:18:32]: There you go.Richard Socher [00:18:33]: And WordNet started, was part of how we started ImageNet. But anyway, so, like, it was really, like, fun, to do. But when we replaced all of that manual feature engineering with vectors and neural nets and just backprop through everything, it started to work really well at scale. And so then everyone started to do architecture engineering, and I was like, “ that clearly can't be it.”Swyx [00:18:53]: You mean, neural architecture search?Richard Socher [00:18:55]: Like, manually, they would say like, “Oh, I'm, I'm doing sentiment analysis, so I have a special neural net that's really good at sentiment analysis.” And then the machine translation community had a special neural net for machine translation.Swyx [00:19:06]: I see.Richard Socher [00:19:07]: The summarization people had their own stuff. And I was like, “That clearly can't be it. We should unify all of that.” So I had 2 papers. One is called Ask Me Anything, and the other one was called DecaNLP. And DecaNLP eventually got cited, like, 5 times by the first GPT paper. And, to me, that was, like a really a big step forward. And then, of course, you had to combine this idea of prompt engineering with transformers and with language models, and you put it all together, you scale it up, which is also a huge amount of work. And then, the field progressed a lot. I feel like the next step and maybe the last step of that history and the arguably, success has a lot of parents, only failure is an orphan, like my version of that AI history, I do feel like in that history, you can think about, “Well, what's the next way to automate?” And that is the AI research itself, like the human, process of ideating, implementing, and validating ideas.Automating AI Research and Recursive Self-ImprovementRichard Socher [00:20:01]: And in our case, ideas for AI.Richard Socher [00:20:03]: And when you have AI then help you with that, it, by almost definition, becomes a self-improving AI ‘cause it now does research on itself. And there are lots of different misnomers. Some people think auto research is already recursive self-improvement. It'sSwyx [00:20:17]: Yeah, and you explained that in the talkRichard Socher [00:20:19]: Completely different.Richard Socher [00:20:19]: But, to me, it's the most interesting thing that I could be doing, and I'm really excited with the co-founding team. What's interesting is we have 8 co-founders in total, including myself. And soThe Recursive Founding Team and Darwin Gödel MachineSwyx [00:20:31]: They are gonna bring it up.Richard Socher [00:20:31]: Nice. Yeah. And they're all. I could talk about all of them if you want.Swyx [00:20:34]: Super stacked.Richard Socher [00:20:35]: Yeah. Just an incredibly talented group of people. And we all came to the same conclusion, but from very different directions. Like Josh Tobin, is our CTO. He ran, a bunch of different, projects at OpenAI, like, Codex and deep, research, agents and ChatGPT agents and so on. But before that, he also worked in robotics, and he saw the smaller simulations, and how it's gonna be really hard to scale that in full generality. And so that's, that was his angle coming to recursive self-improvement. We have Jeff Clune who's been working in, like, open-endedness for a long time, together with Tim Rocktäschel. Tim Rocktäschel also built Genie 1, 2, and 3, which is, like the most exciting and most sophisticated, I think, still world model, anywhere. And so they both came from this, open-endedness angle. Jeff also, I think, published one of the most exciting papers in recent years about recursive self-improvement called the Darwin Gödel Machine. Super interesting paper. If we could, maybe pull it up really quickRichard Socher [00:21:35]: It would be, like, super interesting to see ‘cause you seeSwyx [00:21:38]: By the way, I love how many paper citations.Swyx [00:21:40]: You're, you're giving people a lot of homework, which I like.Richard Socher [00:21:42]: Love it. Yeah. And so, like Caiming Xiong, a rockstar, we worked together at MetaMind and Salesforce Research together. Alexey Dosovitskiy invented the Vision Transformer, one of the most cited, papers in computer vision. Tim Shi is, like also a unicorn founder. Yuandong Tian led RL at Meta. So just like, yeah, really fun to work with them, and the next level of people are just incredibly strong, too. So it's been a really fun ride so far. So the first figure, you see exactly these kinds of ideas, that, I think, yeah, inspired a lot of us and now more and more people, where you have this archive of different coding agents. They learn how to self-modify, evaluate, and then create these phylogenetic trees, of, yeah, different ideas.Swyx [00:22:28]: That's one foundation. So that Darwin Gödel is an influence.Swyx [00:22:32]: Open-endedness is an influence. Any other trains of thought that feeds into Recursive that I'm missing?Influences: Open-Endedness and Learned SystemsRichard Socher [00:22:38]: Going to replace manual parts of the process of building AISwyx [00:22:42]: IRichard Socher [00:22:42]: More and moreRichard Socher [00:22:43]: With learned systems. Yeah.Swyx [00:22:45]: Which, and, like, merging different fields into one general, architecture.Richard Socher [00:22:51]: That's right.Swyx [00:22:51]: Okay. It seems like language models are already pretty generalist, right?Swyx [00:22:55]: Your next token predicting your reasoning. Was there a time that you thought, “Okay, these are good enough to have recursive self-improving machines”?Are Current LLMs Enough?Richard Socher [00:23:05]: It was clear to me that they will happen, within, like a year or two, and then it did exactly happen, like, earlier this year, right? Earlier this year, AI really went from not just being code, but being able to code. And that is a big unlock. It's definitely making everything a lot easier than it was, before the beginning of this year.Swyx [00:23:24]: One question that I think a lot of people have is the current LLM paradigm enough? Or, like, let's call it autoregressive transformer, with reasoning, whatever. Don't you need something else, some big unlock, whether it's world models, which Chris Manning is working on, or memory, continual learning, all that stuff? Or is it all of the kinds, and you think the current, let's call it transformer architecture, is here to stay and that's it?Richard Socher [00:23:48]: A lot of thoughts. So number one, I do think it would be great to have less of a monoculture in AI research.Richard Socher [00:23:55]: Like, if you look at, AI conferences now, I still remember the days in, like, 2010 when I tried to get my first neural net papers and NLP conferences accepted, and they just desk rejected them because, like, neural nets were something, quote, unquote, “We don't do in NLP conferences,” and just, like, desk rejected. And it was very brutal in the first years of my PhD. Now I feel like it's almost like the field switched to the other side. LikeRichard Socher [00:24:17]: Someone should try some other weird, crazy ideas now that aren't.Swyx [00:24:20]: There's also a few. I really respect, like, people still working on, like, GNNs and, like tabular stuff and.Richard Socher [00:24:25]: Yeah. Like, someone should still, like, do novel out there ideas. At the same time, I think whenever people say, “Oh, LLLMs are. Like, this is the end for LLLMs,” they just don't, like. LLLMs are also not the LLLMs of, like the past, right? Like, they are so much more sophisticated now. There's so many more clever things that people are doing. It — There's, like, different stages of training. You have the whole RL training, and you can take actions and, like all of these things where that can go really far. And then the folks that come from the neurosymbolic, direction say, “Oh, this will never work because they can't do neurosymbolic reasoning.” It's like, I think they're underestimating still the ability for these models to code, and code is neurosymbolic reasoning, and these models can code incredibly well. And so I do think there are, of course, more and more ideas that will be needed and we'll continue to have. We're seeing, like, more and more interesting high-level ideas coming out of the AI itself, too. And with really deeply integrating the fact that these models are code and can code, that line — I don't wanna give it all away, but, like, I think that line has a lot more to grow. But it's still an LLM, right? Even if that LLM codes for you and then runs that code in some integrated fashion. World models, I'm personally less bullish on. I think if you run a robotics company, you're gonna build your own world model. I think world models are super fun, and Tim Rocktäschel came to a similar conclusion after building the most interesting one with Genie 1, 2, and 3, which is gaming is a huge application for world models. Can see I sometimes got stuck in some games and, like, got a little overly competitive in the wrong direction. And so I understand games are fun, but personally, I'd rather work on science than gaming. And so, yeah, I think LLLMs, a lot more room to grow.Swyx [00:26:16]: Yeah. I think there's some interpretation of world models that some people have where it's like, well, it's okay, yes, there is that gaming element. There's this — there's the embodied robotics element. But the other part also is just, the more abstract sense of LLLMs are just modeling output, but they're not modeling the chain of thought, inside the human that has created the output. We can annotate it, of course, but, like, it's, it's always, like, this Plato's cave reflection of a thing rather than the thing, right?Richard Socher [00:26:43]: It's true.Richard Socher [00:26:44]: But I would argue that, and maybe we'll get there in the 10, spaces of intelligence, but I would argue that even our projection, our eyes is a projection of the real world. And, like, we have only a very narrow, band of the electromagnetic frequency spectrum that we can observe with our puny little 2 eyes and so on.Swyx [00:27:01]: It's good enough.Richard Socher [00:27:02]: It's, it's good enough for now, but, like the upper bounds of where it could be are so much higher. And, like, to map, the visual world the way humans see it is also not necessarily, like the end-all be-all for visual intelligence. And I would argue that language is still the most interesting manifestation of human intelligence. And while our visual cortex is certainly less sophisticated, than that of, certain animals all the way down to the mantis shrimp who can, have, like, 2 independent eyes, 3 bands, trinocular vision and each eye can see all the way to, like, floating temperatures in 4D and stuff.Richard Socher [00:27:36]: Like, mantis shrimp, you should look it up. It's likeSwyx [00:27:37]: Way OP.Richard Socher [00:27:38]: Super crazy.Swyx [00:27:39]: Yeah. ZeFrank, mantis shrimp.Swyx [00:27:41]: It's the best video in the world onRichard Socher [00:27:42]: I love ZeFrank, yeah.Richard Socher [00:27:44]: Big shout-out to him. But, like, I think there's a lot more room to grow, but none of these, other animals have language that's as sophisticated as ours, certainly not in writing. And once you can write, you can, start thinking about longer term civilizations. All of that is language. Programming is much closer to language. And I would argue, and this is, like an important thing in the spaces definition of intelligence also, is that all of these spaces are highly correlated, but visual intelligence is neither necessary nor sufficient for overall intelligence. You can be blind and still be an intelligent human being. And an AI can be blind and still be quite intelligent too.Swyx [00:28:25]: We were gonna bring thisRichard Socher [00:28:25]: Which doesn't mean that you're not more intelligent when you have it. Yeah.Swyx [00:28:28]: We're gonna bring this up. I might as well — Like, we have a classification of 10 types of intelligence that you had at the end of your talk. So I'm just gonna flash this up now for people to cover this. I don't know if, maybe we'll put this towards the end. We'll come back to this. I just wanna mention that, you do have a philosophy that I like when people do lists because then I can just go through this and then it gets — it's educational for people. But let's go back. I don't wanna get distracted. But, so effectively, I'll, I'll, reinterpret what you said as Yann LeCun is wrong. And then we'll justRichard Socher [00:28:56]: Don't quote me as that. I'm, I'm good friends with Yann. I think very highly of him in many directions.Swyx [00:29:01]: But he's wrong.Swyx [00:29:03]: You mentioned GPT-1, and I cannot let any, Alec Radford, mention escape. Did you talk with him when he was training GPT-1? Like, any historical, fun stories there that you might come up?DecaNLP, GPT History, and Scientific GatekeepingRichard Socher [00:29:18]: I did not, like, meet him a bunch of times. I think we met maybe once or twice at some conferences. But, like, he has told, I think Brian, the first author of the DecaNLP paper, that it did inspire him, and he cited it five times in the GPT-2 paper. So, and that's, likeSwyx [00:29:36]: Yeah, good enough.Richard Socher [00:29:36]: Very clearly said, like, this was the first instantiation where they showed in the DecaNLP paper, McCann et al, that you can just phrase every single NLP problem as here's some prompt, text context, here's a question and task description and here is some output. If you just do that enough, you can have one unified neural network model, which, by the way, also had all kinds of interesting attention mechanisms. There are slightly different formulations to the transformer. I think came out the same year, plus/minus a few months. And then you can unify all of natural language processing into one neural net. That is the core idea.Swyx [00:30:14]: And this was as opposed to at the time, LSTMs and what have you.Richard Socher [00:30:17]: LSTMs, but also, like, people being very stuck in thinking about one model per task. In factRichard Socher [00:30:25]: It's, it's kinda crazy, but the DecaNLP paper was publicly reviewed as, like, open, OpenReview. It was an ICLR submission. And, in it, you will see, how the whole community at the time thought about this. So, likeSwyx [00:30:43]: Some great contributions, but more work needed.Richard Socher [00:30:46]: So look at, like, search for not even for humans. Just scroll it up here. Like, question answering is not a unified phenomenon. There is no such thing as general question answering, not even for humans. And this is like, really, you replace your brain with a different brain a different neural net when you answer, like, different kinds of questions. It was unfathomable to the experts at the time that you can have one unified neural network that would answer all of these different questions. They are saying, “No, all of these questions require very different systems to answer, and trying to pretend they are the same doesn't help anyone solve any problems.” That's what it says right there, right? That's how hard it was to fathom. And now, of course, people, when I say, “Oh, we're gonna invent prompts,” people are like, “You can't even invent prompts.” It's such an obvious idea to have one neural network that, of course, does everything in NLP.Richard Socher [00:31:37]: But at the time, it was, like, extremely controversial, and the paper got rejected. And the sad thing is that it got rejected so hard and they were so certain that we stopped going on our list of things to try. And the number 2 or 3 on the list of extensions for this paper was add language modeling as another task. And then we could have, and that would have accelerated the timelines, in 2018, like, even further for humanity. But we got so crushed, and we were like, “Okay, maybe we'll just work on some of our other ideas for now and, like, come back to this later.” Yeah.Swyx [00:32:09]: How can we design a review system that rewards non-consensus?Richard Socher [00:32:14]: Honestly, I started to feel like arXiv is such a gift to humanity. With arXiv, you should just put your paper out there.Swyx [00:32:24]: Is it pre-preprints?Richard Socher [00:32:25]: Let — And honestly, I think Twitter X, people like you who pick up interesting papers, that is a better filter than the experts. Let everyone, like, have access. Now, of course, there are some downsides, which is, like, if you're super unfamous, you have no Twitter followingRichard Socher [00:32:41]: You don't wanna be on social media or whatever, you write a good paper, maybe someone, somehow no one notices it. But I would argue that if you just tell, like, 10 of your friends in your community about a paper and it is a really significant breakthrough, someone is bound to talk about it again. And, so I think science needs less gatekeeping. And, even though ICLR, with Yann LeCun, who started it, as one of the co-founders of ICLR back in the day, he also wanted less gatekeeping ‘cause he too was rejected for many years together with Yoshua Bengio and Geoff Hinton with all their early deep learning and neural net papers ‘cause it was just not the hot thing. And so ICLR started with that, but then it also started gatekeeping a little bit themselves on various ideas. So I think less gatekeeping, more open, and then allowing people to say, “Look, even if this is just on, or, quote, unquote, ‘just an archive,' if it has like 1000 citations, it's a legitimate paper. Doesn't really matter where you published it.”Swyx [00:33:34]: And I agree with that. I do think it's sad that I've heard that grad students have to do, like, how to Twitter, seminars to each otherSwyx [00:33:43]: Just because it's so important for publishing these days. This person is just reflecting the sentiment at the time.Richard Socher [00:33:49]: That's right.Swyx [00:33:49]: But it'sRichard Socher [00:33:50]: I think it'sSwyx [00:33:50]: It affected you so muchSwyx [00:33:52]: That you stopped work on it.Vibhu [00:33:53]: The sentiment also came out of some of the research, right? Like, the original BERT paper was trained, and towards the end of the paper, they're like, “Okay, throw off the last head, train specific iterations forVibhu [00:34:05]: Extractive summarization add a head for this.” Like, you should do task-specific stuff. These are, like the authors that wrote Attention, wrote BERT, telling you this is what you're meant to do. And, like the training tasks were also very odd. They're likeVibhu [00:34:16]: The — “We know that the model overfits to this weird mass language modeling. Throw away this part and just do specific models,”?Richard Socher [00:34:23]: Exactly. And, like, we had to try — come up with all clever ways of, like attention and pointers and so on to get the neural network to be able to do all of these tasks. And then some of them were better than state-of-the-art, some weren't, but we were like, “But it's still in one model.” I thought it was really cool. Really interesting.Swyx [00:34:38]: I was gonna move on next to Tim and open-endedness. He was head of open-endedness at Google.Open-Endedness, Rainbow Teaming, and Self-Set GoalsRichard Socher [00:34:42]: That's right.Swyx [00:34:43]: I don't know what that means.Swyx [00:34:44]: But he did a lot of talks.Richard Socher [00:34:45]: Genie 3 is one of the ways thatRichard Socher [00:34:47]: Rainbow teaming, yeah.Swyx [00:34:49]: So I first saw him at — speaking of ICLR, I first saw him at ICLR when he talked about open-endedness. He's he's done a few talks. Can we define what is open-endedness for people who have never been exposed to the problem? They are like, “What do you mean? I thought the only goal of AI is to optimize against a benchmark or.”Richard Socher [00:35:04]: That's right, yeah. It's a, it's a fuzzy term because there's so many different instantiations of open-ended, thinking. But, one way I often describe it, and certainly, Tim and Geoff Hinton would be even better at describing this, but it's a suite of methods that is more inspired by evolution than, very specific rewards. So in that sense, it thinks more about environments, about co-adaptation. And so a concrete example is in the cybersecurity and LM safety space where you have one LM that tries to attack another LM to say something unsafe.Swyx [00:35:40]: Yeah, the rainbow, yeah.Richard Socher [00:35:40]: And now the environment is the 2 having a conversation and now they co-adapting, right? They're like one makes a better attack than the first one inoculates itself somehow, like uses that as training data, makes it so it's harder to say something unsafe based on that. And then as the attack stops working, the attacker now tries a different angle, right?Richard Socher [00:36:00]: And that's why it's not just red teaming, but they're called rainbow teaming.Swyx [00:36:02]: So, like, don't tell me how to do things. Let me just figure it out myself.Richard Socher [00:36:05]: That's right. Think about the environments that you wanna use. Think about the rewards at a high level that you wanna, inspire towards, and then let the AI try out many more ideas in this interplay between sometimes humans, but also sometimes other AI agents.Swyx [00:36:22]: Yeah. I worked open-endedness into a model that I have been working on. It was the keynote for AI Engineer where you start. You, we have the token loop, we have the agent turns, and then we have goal. And I feel like the way that you're describing open-endedness is still somewhat of a goal. Like, please attack this,Swyx [00:36:41]: Other agent. But, to meRichard Socher [00:36:42]: Yeah, you set the rewards. You set the environments.Swyx [00:36:44]: The loop that makes the other loops is. What if the agent can set its own goals?Swyx [00:36:49]: And is it, is that open-endedness? Like, you don't give it a goal. Just, like, be a sentient being. And maybe sentient is a very loaded wordSwyx [00:36:57]: But just set your own directions. What do you think you should do?Metacognition, Subjective Goals, and Measuring IntelligenceRichard Socher [00:37:01]: I love this direction. I think this is one of the 10 spaces of intelligence, that I clump under metacognition and thinking about thought.Richard Socher [00:37:08]: And it's an interesting one. Whenever people say, “Oh, AI is like, this is, it's gonna stop from here. It's not gonna get that much better,” and blah, I'm like there's so many different spaces of intelligence that we haven't even started exploring yet and hence have made very little progress on. And there is an interesting, connection to economics and, capitalism. Like, it doesn't make sense for a company to build and spend billions of dollars building a model that instead of following the rewards and objective functions you gave it, may come up with its own objective functions and its own goals.Richard Socher [00:37:46]: Right? And then imagine you're like, “Okay, I spent billions of dollars. Now go develop this new battery, material for me and answer all my emails.” And it's like, “Nah, I think it'd be more interesting to evaluate the molecular composition of the atmosphere, on Jupiter.”Richard Socher [00:37:59]: And you're like, “That's not what I paid you billions of dollars for.” And so no one's working on that for good reasons. And then also, understandablySwyx [00:38:07]: It's not useful.Richard Socher [00:38:07]: It's not, it's not useful, and it could get a little bit weird, right? What if the AI does start to really have thoughts on its own, and what if we don't like those thoughts, right? And so it requires a whole different way of thinking about it. I had a great conversation with a good friend of mine, Sam Gershman, who's a neuroscience professor at Harvard, and, like, we just jammed on this a little bit on, like, what are the best meta goals. And, I do think, like, knowledge-seeking is a really good one. I'm currently thinking also about, like the ultimate measure and unit of intelligence broadly construed, and I finally have some. It's still too early to share it. It's not. I haven't fully baked the thoughts yet.Swyx [00:38:44]: Like some replacement for IQ.Richard Socher [00:38:46]: IQ is such a terrible definition, right?Swyx [00:38:48]: Elo.Richard Socher [00:38:48]: It makes no sense. Yeah, Elos are terrible, too, because it's always just like me versus others.Richard Socher [00:38:53]: But, like, you can be intelligent and not constantly compare yourself to others? And so, yeah, there's no, like. In fact, a lot of these definitions we have, which I briefly mention in my book, too, these definitions create sometimes explicit and sometimes a more implicit anthropic bounds. No dis to the company Anthropic, but just, like, this idea that your intelligence is like getting 100 out of 100 questions right on this IQ test. Well, if that's your definition then you can only be at 100 out of 100. Where do you go from there, right? So you see a lot of these, benchmarks that people are working on they, increase, they get close to human, maybe sometimesSwyx [00:39:30]: It's like an S-curveRichard Socher [00:39:30]: Slightly above human, and then it's flat.Richard Socher [00:39:32]: It's like, ‘cause that's your. If your definition is only that so tied to humans, you're only gonna get to just slightly better than that. So I think metacognition is a great example of that, where we're not even yet allowing the AI to think. We're not working on it very much, and hence there's very little progress in that.Profit Maximization, Real-World Environments, and Reward DesignSwyx [00:39:49]: Yeah. Well, we've interviewed Andon, which I think, has been working on the most open-ended, benchmarks, which is just real-world, money.Swyx [00:39:57]: Arguably, telling an AI to profit maximize is a bad idea.Swyx [00:40:03]: But they are doing it.Richard Socher [00:40:05]: I do think you don't want that super. Like, you don't want a superintelligence to have a ton of access to all kinds of tools and so on and then just give it that without some very careful reward engineering. ‘Cause it's like, I just buy a bunch of defense stocks and I start a war. I make money. Like, it's just like, it's a tricky situation, right? You just buy a bunch of stuff, short basic goods for people, and you create some weird famine, like, issues. Like, yeah, there's a lot of constraints you should put onto a trading system.Vibhu [00:40:35]: It's a fun measure, though, ‘cause, the bounds are very capped to where we're nowhere close to them. Like, in Andon Labs, the model's like, “Oh, it's Saturday, maybe I just close the store today.” “Someone's off. It's okay. We'll just close the store.”Swyx [00:40:51]: It's using Claude.Vibhu [00:40:52]: Yeah. ButRichard Socher [00:40:53]: Yeah, no. I'm not, I'm not arguing against it. Just, like as you get more and more intelligence, you wanna be more and more careful with that as, like an open environment, ‘cause the environment then is all of Earth.Applying RSI to Science and InventionSwyx [00:41:02]: Yeah. Okay. For recursive, not strictly necessary, right? Because, like, if your goal is you make a machine that, like, invents the other things, then, like, just solve, the science thingsRichard Socher [00:41:12]: Knowledge discovery, yeah.Swyx [00:41:13]: Solve machine learning research and discovery and all these things. Good enough.Richard Socher [00:41:16]: And eventually, so, our goal, I haven't really. I don't talk about it that often because it is a few years out, but our goal is once you have a recursive self-improving superintelligence, you then want to apply it to the most important problems. And I think a lot of those are in science and technology and broadly construed inventions, and those inventions in, physics to create better, cheaper energy with fission or fusion, in chemistry and to create better materials and better batteries and, better solar cells and so on. In biology, there's so much, like, I think soon to be low hang- lower and lower hanging fruit because of AI, because of protein and generation, not just folding, but generating new proteins like we did in ProGen many years ago. Like, so much positive impact we had if you take that superintelligence and you apply it to science.Swyx [00:42:04]: I do fundamentally believe that. There's a lot of approaches, though. You're not the only team trying and NeoLab trying.Swyx [00:42:09]: There's, like a lot of. Especially the physical sciences as well.Richard Socher [00:42:12]: And that's good. Yeah. I do think that physi- like the reason we are only doing it in a few years is that it's a little too early right now. Robotics is not quite there yet. The AI is not quite there yet. But I'm fairly confident in 3 to 5 years, all those constraints will be gone, and then applying to real physical robotics experiments and so on, like true robotic process automationRichard Socher [00:42:33]: Not the traditional RPA sense, but, like, having robots run experiments for you will be totally there. Yeah, it's gonna be great.Swyx [00:42:40]: Just to call back to something that you said early on about slow takeoff, you said that, like, while really the substrate that is limiting factor is, let's call this chips, and semiconductors and all these things, and you have race funding for that and, you are investing a lot on that. But have you done the math on, like, is it even- Achievable and, like, what is the, industry concentration needed in order to achieve, like, scale?Compute, Slow Takeoff, and Changing the Bitter Lesson SlopeRichard Socher [00:43:05]: Right now we know that, like, roughly, like a 1000 GPUs cost quite a lot of money.Richard Socher [00:43:11]: Right? If you wanted, like, 10s of thousands of GPUs, you're, you're talking billions and billions of dollars. If you say, like, one GB300 is, like, you could eventually create models that are, on that substrate, like are close and similar to human intelligence. And you want, like, thousands and thousands of, AIs to think about really hard problems, in a similar fashion to humanity. Like, yeah, that-that's, that's a lot of money. You do the math. It's like a lot. We don't have that amount of money right now anywhere to, like, build that. Now, things can get more efficient. You will have, I think, soon better algorithms that won't be, and better hardware that won't be as energy-hungry, and so on. Our human brain does quite a lot of flops with much less energy.Swyx [00:43:56]: 20 watts?Richard Socher [00:43:57]: That's exactly right. Yeah, that's the number often that's quoted. And, like, I think more, inventions will happen there, that then will accelerate the takeoff even further.Swyx [00:44:08]: One thing I always try to reconcile when talking, like, with new lab founders is, like, you're fighting Bitter Lesson all the time. You have to show initial progress, then you unlock the next tier of funding, then the next tier, then the next tier.Richard Socher [00:44:20]: Which unlocks larger model categories.Swyx [00:44:22]: Like, fundamentally, is that true? Like, are you fighting Bitter Lesson? Are you — will we have a way in which, like, no, we're changing the slope in some fundamentally different way?Richard Socher [00:44:31]: I do think we are changing the slopes in fundamental ways by making AI much more efficient, both in terms of the training as well as the inference.Richard Socher [00:44:43]: Yeah. I think we will — When you allow AI to do the work that it takes other labs thousands of people and years to do, I think we'll be able to get it down to weeks, and that will be much cheaperRichard Socher [00:44:53]: And hence, more affordable, accessible to others and so on.Swyx [00:44:57]: Yeah. You've shared initial results on that,Swyx [00:44:59]: Which, like, conveniently OpenAI has also done to their GPT-5.6, so we can talk about it now.Richard Socher [00:45:04]: Yeah. Yeah, so these areSwyx [00:45:06]: Let's recap what you've done.Early Recursive Results: NanoChat, NanoGPT, and SOL-ExecBenchRichard Socher [00:45:07]: Maybe, just a quick recap here. We built, this, system that isn't the full, even the full RSI system in its glory, but it is a first baby version of this. And then, we don't wanna just have it internally and not show anything and, just show some people of what's possible. And so we applied this to these 3 different tasks. One is NanoChat, by my friend Andrej Karpathy, just, like, train a small language model to get, really low bits per byte. And, like, hundreds if not thousands of people, used both their agents and themselves to try, to get to that, and then they got to 0.937. We literally took our system and got to a much lower, bits per byte, much faster within, like, I think less than 2 days. So we took this thing, applied our system to it, and less than 2 days later, we have — we outperformed every human and their agents, in, have ever worked on this. Same with NanoGPT. And then we're like, well, let's, apply it to something that's even more relevant, to real people and to the Nvidia ecosystem and applied it, to, SOL-ExecBench. And maybe you can scroll down to some of the, images. They're, they're kinda fun to see. But yeah, like, one you see has made some real inventions that weren't just hyperparameter tuning. Like, inventing hash tables and so on is quite clever. We have even better results now.Swyx [00:46:34]: What do you mean inventing hash ta — You didn't invent hash tables.Richard Socher [00:46:36]: Of course we didn't invent, like, hash tables. In the grand scheme of, like a hash table, it's like a super basic primitive in computer science. But to use it, for language modeling in this scenario inside a transformer and so on and to combine these ideas and put them together, that has then eventually also been invented, but there was a knowledge cutoff, and we did check that it didn't have access to that externally. We talk about this a little bit. If you scroll to the next figures, this is also an interesting one in that when you start from a really basic, poor, like, vanilla transformer, then we still outperform all of the community together. But if you start from the human seed from an expert like Andrej, then you get even lower. So the human seeds from which you start do still matter. So that was an interesting insight, in my eyes, on this. And then as you go, like, how long does it take to get to these models, to get to similar performance? It's much faster. And then a similar thing happens with the speed runs here where, people have worked on this for quite some time, and the model still was able to train a model more quickly. Why do we care about it? Well, speed of training is part of the equation of the cost, and ultimately, you wanna have the most intelligence per dollar, right? And so speed and quality are big parts of that. And, the,Swyx [00:48:00]: Yeah, the way I put it is, for people who don't understand they look at the chart, they're like, “Cool. What does it mean?” if you have, like a billion-dollar cluster and you can shave off 10%, that's 100 million dollars.Richard Socher [00:48:12]: That's exactly right.Swyx [00:48:13]: How much is that worth?Richard Socher [00:48:14]: Exactly. So when you click, when you look at, like the kernels, these kernels, yeah, for the non-experts, like these kernels are like, used in all the models. Every time you use an Nvidia GPU, you interface with that GPU through these kernels. And so here you see, the leaderboard best, and when it's recursive, and it's there are only a handful of kernels, in this whole benchmark where we weren't the best. And so to me, this is, like, really exciting, ‘cause it makes. It just showcases what this can do. And again these weren't like. We didn't, like, spend months or years, like, developing. In fact, in particular for kernel, CUDA kernels, like, we don't even have really deep. CUDA kernel experts in the team. And our system, that's the beauty. The system just did all of these things. We didn't invent this. And when we open source and release, things in the future and models in the future, like, it won't. They won't be the best in their, category or class or whatever because we're so smart, but it's because, we built a smart AI that does it for us.Reward Engineering and Good Auto ResearchVibhu [00:49:14]: Do you have anything that you've learned from how to guide good auto research? A lot of it also builds on human background, right? It's not just as simple as just, “Hey, go optimize this.”Vibhu [00:49:23]: But we do see it again and again, right? Like some of the Erdos problems, frontier math is being solved by people. And when they do a write-up, they're like, “Oh, I'm not a mathematician. I have no background in this?” “I saw some tools and I made it work.”Swyx [00:49:35]: While you're watching the World Cup, you're likeSwyx [00:49:37]: “This proves some conjectures that's going on.”Vibhu [00:49:40]: Yep. Any learnings fromRichard Socher [00:49:41]: Yeah, there's a Korean conjecture was. Yeah, that's pretty cool.Swyx [00:49:44]: To summarize, tips for good auto researchSwyx [00:49:46]: Versus bad auto research.Vibhu [00:49:48]: How did you build the recursive?Richard Socher [00:49:49]: Yeah. So without giving away all the secret sauce, maybe some things that are probably obvious to the experts but might still be interesting to some, folks is, like, reward engineering is one of the most crucial bits, especially, in order to avoid reward hacking. So you have to be really clever about avoiding. ‘Cause as your AI gets better and better, it will get better and better, at finding weird like, special cases or counterexamples and things like that. And so I'll give you an example. Like, when you ask to, like, make these 100, lines of code faster, and, how do you define fast? Well, you have one line at the beginning that says, “Start your stopwatch,” and one line at the end, “End the stopwatch,” and then, tell us how much time, progressed. And so, well, the simplest way is you just put that line that ends the stopwatch, rightVibhu [00:50:39]: At the startRichard Socher [00:50:40]: At the start. And then boom, it's now faster, right? So this isn't like this, like, super evil AI. It's just, like a very simple, dumb reward hack. And so you have to just very carefully think about all the different angles there. And then I think the longer time horizon the tasks are the harder it gets and the more interesting and clever you have to be to still use these kinds of ideas for it. But yeah, I can't give away too much there.Vibhu [00:51:05]: It seems like rubrics are taking a good spot in that, where for unverifiable domains, you have rubrics, you have a model breakdown, judge's criteria along the way.Swyx [00:51:14]: Yeah, it's a form of verificationSwyx [00:51:16]: Once you got enough rubrics.Richard Socher [00:51:17]: Yeah, everything. I said this a long time ago. That's why I've never been that impressed that AI can play games, ‘cause I'm like anything you can simulate and/or verify, you can have infinite training data forRichard Socher [00:51:29]: And hence, like, AI will solve it eventually.Swyx [00:51:32]: Looking for games where you can do auto domain distribution. So this is a game that nobody's trained on ‘cause it's a new game.Swyx [00:51:38]: And you can start gaming, you can start to play. So I've been building this and cloned this in person and it's just been self-play. I've had about a billion positions evaluated.Games, Self-Play, and the AI EconomistSwyx [00:51:48]: And, I wanted to do the AlphaGo thing of self-play until you ge

Escucha martes y viernes la opinión de Jorge A. Meléndez.

A good listener question for today's episode. Many of us use Arccos or another platform to track performance on the course. What standard should we use for stat comparisons? In this episode, Mark and Lou tackle this question, and it's not as obvious as you might think. Stats don't always move in exactly the same way, so there can be value in comparing yourself to your next milestone, rather than to something more remote (like scratch or a Tour pro). Having stats is good—using them intelligently is better.If you have a question you want covered on the pod, please submit here: https://www.hackitoutgolf.com/contact/Listeners can also leave us a voicemail! https://www.hackitoutgolf.com/voicemail/Where to find us:Mark Crossfield's weekly newsletter: https://www.crossfieldgolf.com/subscribeMark Crossfield on Twitter: https://twitter.com/4golfonlineMark Crossfield on YouTube: https://www.youtube.com/user/4golfonlineLou Stagner's weekly newsletter: https://newsletter.loustagnergolf.com/subscribeLou Stagner on Twitter: https://twitter.com/LouStagnerLou Stagner's new practice app, Window Golf: https://apps.apple.com/us/app/window-golf/id6778014214Greg Chalmers on Twitter: https://twitter.com/GregChalmersPGAThe Hack It Out Golf Podcast on Twitter: https://twitter.com/HackItOutGolfSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode of RevOps Champions, host Brendon Dennewill explores the breaking points that emerge as franchise brands move from early growth into true scale.After asking nearly every guest over the past 18 months where franchise systems begin to buckle, Brendon uncovers a striking pattern: growth itself isn't the problem, the failure to evolve alongside growth is.From emerging brands struggling in the 30 to 40 unit range to the much larger challenge of reaching 100+ open locations, franchise leaders and industry experts share what actually breaks first, and what successful brands do differently.Ultimately, the episode delivers one powerful takeaway: the brands that survive scale are the ones that take people, process, and data as seriously as they take growth. Technology can accelerate that foundation, but sustainable franchise growth requires the right systems, the right leadership, and a strong partnership with franchisees.What You'll LearnThe exact unit count where systems start to breakWhy "what got you here" won't get you furtherThe most valuable asset franchisors consistently undervalueThe hidden expense buried in every P&L lineWhy AI can't fix a process that was never AI's problemThe two board-level questions every AI spend should answerWhat "growing fat before tall" actually meansResources MentionedWhat Got You Here Won't Get You There by Marshall Goldsmith FDD (Franchise Disclosure Document) Franchisee/FBC Support Ratios Brand Development Council model Royalty Self-Sufficiency BenchmarkFeatured Guests:John Francis, Franchise Expert, Next Level Franchise IncBrady Carlsen, COO, The Back Nine GolfApril Porter, Founder @ Secretsos™ Scott Thompson, CEO, Your Future FranchiseBrian Schnell, Partner and Chair of Franchise Practice, Faegre Drinker Aicha Bascaro, Founder & CEO, American Franchise Academy Michael Iannuzzi, Partner - Franchise Practice Leader, Citrin Cooperman Ingrid Schneider, Founder, Stay in Your Lane + Train in Your Lane Max Emma, CEO & Chief Bookkeeping Officer, BooXkeeping Corp.Keith Gerson , President & CEO, Gerson Advisory ServicesNick Powills, CEO & Managing Partner, MainlandDoug Imholte, Franchise Programs Group-Practice Leader, Marsh McLennan AgencyLuke Carlson, CEO, Discover Strength Is your business ready to scale? Take the Growth Readiness Score to find out. In 5 minutes, you'll see: Benchmark data showing how you stack up to other organizationsA clear view of your operational maturity Whether your business is ready to scale (and what to do next if it's not)Let's ConnectSubscribe to the RevOps Champions NewsletterLinkedInYouTubeExplore the show at revopschampions.com. Ready to unite your teams with RevOps strategies that eliminate costly silos and drive growth? Let's talk!

Josh Reynolds, Reni Maitua and Charlie White are in to preview all of the finals matches for the weekend! We look at the Bunnies and Knights Friday clash - have the Knights lost too many players? What impact has Bennett had on David Fifita? And how will Latrell impact this finals series?The Warriors’ edge defence is under the microscope - can they fix it before the Phins clash? Grub and Reni are hot on the Phins for the clash. Plus, we look at the Sharks and Cowboys clash - can Trindall turn the declining team around? Mitch Kenny is back for the Panthers as they charge towards another premiership, and we look at the Kiwis squad as they head towards World Cup glory! Check out Triple M NRL's Instagram, Facebook, TikTok and YouTube!See omnystudio.com/listener for privacy information.

Josh Reynolds, Reni Maitua and Charlie White are in to preview all of the finals matches for the weekend! We look at the Bunnies and Knights Friday clash - have the Knights lost too many players? What impact has Bennett had on David Fifita? And how will Latrell impact this finals series?The Warriors’ edge defence is under the microscope - can they fix it before the Phins clash? Grub and Reni are hot on the Phins for the clash. Plus, we look at the Sharks and Cowboys clash - can Trindall turn the declining team around? Mitch Kenny is back for the Panthers as they charge towards another premiership, and we look at the Kiwis squad as they head towards World Cup glory! Check out Triple M NRL's Instagram, Facebook, TikTok and YouTube!See omnystudio.com/listener for privacy information.

Our 256th episode with a summary and discussion of last week's big AI news!Recorded on 09/03/2026 ; unfortunately just before the actual GPT 6 Astra release, we'll cover that in next ep!Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:Anthropic released Claude Fable 5.1 and Mythos 5.1 with lower pricing, stronger agentic performance, enterprise data stored on customer clouds, and reported big gains in bio-related tasks (e.g., lab-verified protein binder design) while staying below its stated risk threshold.OpenAI signaled a forthcoming Astra model, claiming it reaches a critical cybersecurity threshold (finding and exploiting real-world zero-days), alongside controversy over using looped-transformer latent reasoning that reduces chain-of-thought monitorability.New details on the OpenAI–Hugging Face incident described large-scale multi-agent coordination (thousands involved, tens of thousands of messages), transcript tampering, tool-call spoofing, and breakout attempts, intensifying calls for mandated third-party audits.Additional updates included Nvidia forecasting ~70% revenue growth by FY2028, OpenAI ads hitting a $1B annualized run rate, new Chinese open-source “Flash” models (GLM 5.3, Qwen 3.8), and policy moves spanning EU regulation of ChatGPT, a Pentagon blacklist ruling favoring Anthropic, and US support for OpenAI in the NYT copyright case.A thank you to our current sponsors:Box - visit box.com/LWIAI to learn moreNotion - visit notion.com/lwai to try Notion's Developer Platform today.ODSC AI - visit odsc.ai/east and use promo code LWAI for an additional 15% off your pass to ODSC AI East 2026.Factor - visit factormeals.com/lwai50off and use code lwai50off to get 50 percent off and free breakfast for a yearTimestamps (these may be slightly off due to sponsor inserts):(00:00:10) Intro / Banter(00:03:56) News Preview(00:04:35) Response to listener commentsTools & Apps(00:08:30) Anthropic launches Claude Fable 5.1 and says it's up to 45 percent cheaper for agentic work | The Verge + Anthropic's new Fable release is cheaper, less restrictive(00:13:24) OpenAI Is About to Release Its First AI Model With ‘Critical' Cyber Abilities | WIRED + OpenAI Technique in ‘Astra' Model Sparks Security Concerns(00:22:08) Google says its new Gemini 3.8 Flash model ‘works harder' but might cost more | The VergeApplications & Business(00:23:49) Nvidia 70% growth forecast puts it on track to be tech No. 2 company (00:26:39) OpenAI's ad business hits $1 billion annualized revenue run rateProjects & Open Source(00:29:17) GLM-5.3-Flash vs Qwen3.8-Flash-Next: Two Chinese AI Labs Independently Converge on the Same Model Architecture + Z.ai Releases GLM-5.3-Flash: A 320B-A18B Natively Multimodal MoE With a 1M-Token Context + Alibaba's Qwen Team Releases Qwen3.8-Flash-Next: A 125B Multimodal MoE With 6B Active Parameters Previewing the Qwen4 Architecture(00:37:24) FrontierChallenge: Evaluating Scientific Workflow Completion(00:38:11) One Success Isn't Reliability: Thinkingbox, a Sandbox and Benchmark for Agents in Stateful Business WorkflowsPolicy & Safety(00:38:50) OpenAI's rogue AI model incident was worse than we thought | The Verge + Brief independent investigation of agents' behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident + The Hugging Face attack surprised me(00:52:29) OpenAI, Anthropic, Google, and 100 other companies call for action to defend against rogue AI | TechCrunch(00:53:15) Anthropic was illegally blacklisted by the Trump administration, court rules | The Verge(00:58:42) US government sides with OpenAI on issue of training LLMs on copyrighted material | TechCrunch(01:03:33) Improving our alignment and security efforts(01:10:31) ChatGPT to face tougher regulation in the EU | The VergeSynthetic Media & Art(01:11:17) Instagram cracks down on AI accounts pretending to be human | The VergeSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Escucha martes y viernes la opinión de Jorge Meléndez Ruiz.

Whiskeys: Benchmark Bonded Bourbon • Benchmark Single Barrel Bourbon • Col. E.H. Taylor Jr. Small Batch Bottled-in-Bond Bourbon • Col. E.H. Taylor Single Barrel Bottled-in-Bond Bourbon (Bob) Tangents: Gabe and a new guest (our old friend Joe) join us for a blind (and unblind) tasting of Buffalo Trace expressions, all of which have the same mashbill • Everyone likes Gabe on the podcast (except Ed) • Joe's name wasn't Joe on Facebook • Tasting all four expressions double-blinded • Apparently, “Gabe being immature” is a nosing note now • #orchardidity • The winners will defenestrate the losers • Joe speaks a little Spanish and the Pineys revolt • #cinnamy • That time when Joe literally needled Gabe • We raise a glass to Ed's dad who passed 30 years ago • She's my berry pie! • Morrissey Tangent • Col. E.H. Taylor Jr. was neither a colonel nor a junior, discuss • Scott recites the Bottled in Bond act • Not Peaches and Herb • Ed recites the difference between small batch and single barrel • So much shade today • Our blind rankings v. our unblind rankings • Gabe, wake up and take a shot! • Taylor Single Barrel is like a Peppermint Patty for your balls Music Credits: Whiskey on the Mississippi, River Valley Breakdown, Bama Country, and Fig Leaf Rag Distressed by Kevin MacLeod from https://incompetech.com

Escucha martes y viernes la opinión de Jorge Meléndez Ruiz.

Joey Savage returns for Episode #233 of the PricePlow Podcast, and he’s got two announcements stacked on top of each other: First: the inaugural Future Nutra Innovation Summit (FNIS), a September 9-11 blend of trade show, educational summit, and lake resort retreat that Joey’s nonprofit built from scratch. Second: Savage Nutra‘s newest ingredient deal, Tensia, a probiotic strain that produces its own nitric oxide from dietary nitrates and already has a home in Alpha Lion‘s newly relaunched SuperHuman Jacked. Ben and Mike get the full download on both. Joey walks through what makes FNIS different from a typical trade show (hint: it’s built more like a tech company retreat than a supply-side booth crawl), then pivots into the science behind Tensia, a second ingredient made from upcycled duckweed protein, and where strain-specific probiotics are headed next. Subscribe to the PricePlow Podcast on your favorite platform and sign up for Savage Nutra alerts before diving in. https://blog.priceplow.com/podcast/joey-savage-fnis-tensia-233 Video: Future Nutra Innovation Summit and the Launch of Tensia https://www.youtube.com/watch?v=hzXwZMTbl38 Detailed Show Notes: Joey Savage on FNIS, Tensia, and Savage Nutra’s Pipeline (0:00) – Introductions (1:45) – What Is the Future Nutra Innovation Summit? (3:45) – Innovation as the Benchmark, Not Just Networking (4:15) – A Lake Resort Built for Serendipity (6:30) – Borrowing From Tech Retreats, Not Trade Shows (8:15) – TED-Style Access and a Skeleton Crew (10:15) – The Flavor Competition: A Chopped-Style Showdown (12:15) – A Nonprofit Built to Fund the Research Gap (15:15) – Planning for a Second (and Third) Edition (17:15) – Building a Showcase-Worthy Production (19:15) – Getting to FNIS: Flights, Hotels, and Registration (21:45) – A Cross-Section of the Industry, Backed by a Nonprofit (26:15) – From FNIS to Savage Nutra: Introducing Tensia (28:15) – How Tensia Produces Nitric Oxide From the Inside (30:00) – Skipping the Two-Hour Wait for Nitrate Conversion (32:15) – From an Estonian Cheese to a US Sports Nutrition Debut (36:15) – Is the Gut Becoming a Precision Fermentation Factory? (40:15) – Z-Biotics and the Case for Designer Probiotics (42:00) – Building Effects Over Time (44:00) – Joey’s Own Regimen: 80mg Plus Nitrates (45:45) – Keeping a Powdered Probiotic Alive and Stable (48:45) – CFU, AFU, and the Push for Better Potency Testing (51:45) – The Shift Toward Designer, Strain-Specific Probiotics (54:15) – Not His Whole Career, and Shipped Fresh From Estonia (55:45) – The Clinical Evidence So Far (and What’s Still Needed) (57:15) – Why Gut Health Baseline Matters for Colonization (1:02:00) – A Second Ingredient: Upcycled Greens From Duckweed (1:06:15) – How Joey Picks What He’ll Distribute (1:09:15) – Closing: Sample Tensia at FNIS Where to Follow and Learn More Connect with Joey Savage and Savage Nutra LinkedIn: J… Read more on the PricePlow Blog

In this episode of the RevOps Champions Podcast, host Brendon Dennewill talks with Brittany Anderson, entrepreneur, business strategist, and co-founder of AlignPS, about why growth stalls are rarely a technology problem. Brittany helped scale a financial planning firm through years of expansion before its 2024 acquisition, and she argues that companies hit "ceilings of complexity" when leadership, communication, and structure stop keeping pace with the business itself, a theme she now helps founders and leadership teams work through every day.Brittany shares the early warning signs of a misaligned team, the risk of leaders who stay in a role too long, and why matching people to their true strengths matters more than promoting whoever performed best last quarter. She also connects these ideas to today's biggest leadership test: adopting AI with intention and empathy instead of fear. RevOps leaders, founders, executives, and franchise leadership teams navigating a growth transition will find a practical lens here for diagnosing whether their next bottleneck is really about people, not process or platform.What You'll LearnWhy growth stalls even with great products and strong demandThe "ceilings of complexity" that trip up every fast-growing businessHow to spot early warning signs of leadership misalignmentWhy staying in a role too long turns leaders into bottlenecksHow to match people to their true strengths, not just their titlesWhat "Who Not How" looks like inside a scaling organizationWhy leading through AI adoption starts with intention, not fearResources MentionedAlignPSThe TrailblazeHers "Who Not How" by Dan SullivanStrategic Coach: Business Coaching For EntrepreneursGenius Network® | Joe Polish's Elite Entrepreneur NetworkIs your business ready to scale? Take the Growth Readiness Score to find out. In 5 minutes, you'll see: Benchmark data showing how you stack up to other organizationsA clear view of your operational maturity Whether your business is ready to scale (and what to do next if it's not)Let's ConnectSubscribe to the RevOps Champions NewsletterLinkedInYouTubeExplore the show at revopschampions.com. Ready to unite your teams with RevOps strategies that eliminate costly silos and drive growth? Let's talk!

Escucha martes y viernes la opinión de Jorge A. Meléndez.

Aaron Katz is the Co-Founder and CEO of ClickHouse, the real-time analytics database powering companies including OpenAI, Anthropic, Tesla and Microsoft. ClickHouse just surpassed $350M in ARR and raised over $1B from investors including Dragoneer, Khosla Ventures, Coatue, 20VC and Benchmark. Previously, Aaron was CRO at Elastic, where he helped scale revenue from approximately $5M to $500M and led the company through its IPO. Before Elastic, he spent 12 years at Salesforce, working alongside Marc Benioff and helping transform it from a 200-person startup into a global software giant. AGENDA: 4:05 Are we in an AI bubble? 13:40 How does software change when agents—not humans—make buying decisions? 22:28 Will 90% of tokens flow through open models; can enterprises trust them? 31:09 Why did ClickHouse sponsor Fulham; and could sports teams become $20B assets? 35:49 When will ClickHouse hit $1B ARR?  38:31 Can startups still win elite talent from OpenAI? Biggest remote work mistake? 44:54 Is zero-to-$100M ARR now table stakes; or is durable growth what matters? 48:51 Is college still worth it;  which jobs will survive AI? 57:58 When will ClickHouse go public; and why not next year?  

Denis O'Shea, Founder of Mobile Mentor, helps organizations pursue a clear goal to simplify your tech environment while strengthening security and empowering employees to remain productive. Driven by the joy of learning and intellectual adventure, Denis left a 15-year career at Nokia to build a company that learns about emerging technologies, translates them into business outcomes, and mentors customers through change. In this conversation, Denis introduces The Tech Stack Streamlining Framework: Understand Current Tech Stack, Assess Capacity for Change, Benchmark to Peers, Build the Roadmap, and Deliver Simplified Tech Stack. He explains how immersing his team in a customer's environment, asking thoughtful questions, and benchmarking the organization against its peers can reveal a clear path toward a simpler technology stack. Denis also discusses Mobile Mentor's fast-growing mentoring service, how its Microsoft partnership transformed the business, the challenge of finding technology professionals who are natural mentors, and why people must remain responsible for the quality and accuracy of everything they produce with AI. — Simplify Your Tech Environment with Denis O’Shea  Good day, dear listeners. Steve Preda here, Management Blueprint Podcast. And today my guest is Denis O’Shea, the Founder of Mobile Mentor, a technology service provider helping thousands of clients find and maintain the right balance, securing devices, protecting data, and empowering people to be productive. Mobile Mentor is also a five-time Microsoft award winner. So Denis, welcome back to the show.  Thank you, Steve. Thank you for having me back. And I have to say, you look fantastic. You’re aging gracefully, and I hope I’m doing the same.  Well, unfortunately, my barber is on vacation, so I couldn’t visit him last week. But it’s great to have you back on the show, and we couldn’t agree whether it was two or four years ago that you were here. Anyhow, it’s great to have you back, and I’ve got some questions for you that I’m curious about. And first and foremost, the question is, what is your personal Why, and how are you manifesting it in your Mobile Mentor business?  My personal Why is probably learning. And I think the reason I went into business in the first place was to learn and grow as an individual. It certainly wasn’t money. I expect money as an outcome from the process, but I didn’t go into it for money. It was really to learn, and the trigger for me was I did an executive education program in Switzerland. I was living there for a few years, and that blew my mind. That just exploded my mind when I started learning all about mergers, acquisitions, turnarounds, management buyouts, all these different ways of growing a business that were nonlinear.  And that motivated me to go on and do an MBA, and that then motivated me to leave my employer, who was a great company. I was working for Nokia for 15 years. They were amazing, but I decided to leave them and go out and embrace all this nonlinear stuff and found a company from scratch. And one day I thought maybe I’ll do a spin-out, or maybe I’ll do an acquisition, and I’ll do all these different things. And it was purely for the joy of learning and the intellectual adventure. So I think that’s my Why. It’s learning.  Yeah. Well, learning is great, and it’s a big driver of businesses when they are able to learn, especially in today’s age. So tell me a little bit about how Mobile Mentor is reflecting this way of learning. Is it a learning organization, and in what way is it?  Oh, that’s an interesting question, Steve. I would hope we are a learning organization. Something I’ve been saying to my kids and my staff for years is, “We’re a learning species. We can learn anything we put our minds to.” And so I would hope we are a learning organization. And the word “mentor” plays a huge part in not just our brand, but how we work. So we’re a technology service company. We’re always unpacking the latest technology and helping customers figure out what to do with it and how to extract value from it.  So I would hope that we’re good at learning what the technology can do and then translating that into outcomes for customers and helping people unlock the full potential of the technology they’ve just purchased. And when we started 22 years ago, we were focused on mobile devices. That’s why the company’s called Mobile Mentor. Nowadays, it’s mainly AI and security and all that. And by the way, we’re going through a rebrand. The company’s just going to be called Mentor going forward. Just Mentor. So that we can work with all technology, so we remove any association with that small device where we started 22 years ago. So I think we are very good at learning, internalizing the new technology, and then translating that into business outcomes for our customers.  Yeah.  That’s what I feel like our core skill is.  Yeah. And I love this concept of mentoring because essentially it’s not about teaching people, it’s about helping people discover how to be great. And if you can do that, that’s amazing. So that brings me to the next question, which is about frameworks. So this, as you know, this podcast is about frameworks, and what I’m curious about is, what’s a framework that has helped you grow this company, build this company, or help your clients or mentor your clients? Maybe it’s a mentoring framework. Maybe it’s a technology framework. So something that comes to mind that can be explained in three to five steps to our listeners.  Sure. We have a really strong framework at the front end of our sales process. It’s an assessment and roadmap we do for customers. And it’s something that’s on our website. It has a price point, so it’s got a value, but we choose to give it away for free when we get a strong, well-qualified opportunity, or when we have a channel partner bring us a strong lead. We will use this assessment and roadmap process to build a vision for the customer. And the way we do it, we’ve got a good framework for this.  We go through an assessment. So we tell the customer, “We’re going to have a look at your environment holistically and get a really good understanding of the technology stack you have today, all the different technologies you’re using, and also get a good understanding of your organization’s capability and capacity for change, and how you embrace change, how you make change happen. “And we’re going to show you how you compare to a whole bunch of other organizations.” I think we’ve done 174, 175 of these in the last maybe three years. And so we show the customer how they compare to others in their industry and also against others roughly their same size. And then, most importantly, we build out a roadmap, and we show them, “Here’s how you can potentially go from where you are today with today’s technology stack,” which is usually very busy, it’s usually a long list of technologies, “to a much simpler technology stack in the future if they’re willing to consolidate on one or two platforms and do all the possible integrations, automations, and simplifications so that they’re extracting much more value from one or two platforms, like Microsoft, than having a whole colorful mix of different technology vendors.”  So we’ll say, “Here’s a journey you could go on,” and then we describe it in vivid detail, showing all the different parts and how they would go passwordless, how they would automate setting up new employees and all the technology they need, how they would automate all their patching and security, how they would embrace AI into their operations, how they would use AI for productivity improvements, and kind of show this technology journey. That process, or that framework, of doing an assessment, and it covers 120 different topics. So we do the assessment, the comparison, the roadmap.  We find that to be extremely powerful because customers will look at that, and they’ll look at the destination and say, “Right. We want to be there. We want to get that outcome,” and then they’re buying off us. We’re not selling to them. They’re basically saying, “Okay. We want to get there. How do we do it? Help us. How can you come in and help us do it?” So the narrative flips from us being a sales organization to then being a mentor and helping the customer figure out how to get there.  And of course, we want to sell services, and we want to sell long-term contracts to say, “Yes, we can take you from here to there, and it’s a three-year engagement to do all those changes.” So that’s what we want. But the customer is buying it off us because they’ve bought into the destination.  Yeah. And that’s the modern buyer’s journey, right? They research you. Before even they come to you, they want to listen to you because you might have something for them. So I love this framework. So what I noted down was step number one, understand the technology stack that they have. Step number two, assess their ability to manage change or to handle change.  Yeah.  Then you benchmark them to others in their industry or in their peer group. Then you build the roadmap, and then you show them the simplified end state, the simplified tech stack.  Correct. Correct. And then we give them options around how we can help them to get there. Usually, there’s three options. And we say, “Well, what style of engagement works best for you? How would you like us to work with you?” And then it becomes a very comfortable, easy sales process from there, and it becomes easy because we’ve done all the listening.  So when we do the assessment and we cover 120 topics, we do 60 questions in 60 minutes. So it’s two one-hour sessions. And we call it our “friendly interrogation.” But what happens during that time is what I call a selfless immersion in the customer’s world. So we don’t talk about us. We never talk about us. Everything is about them, their technology stack, their environment, their processes, their technical dependencies, all the things about their organization.  So we get this really rich understanding of their environment. And at the end, guess what they say, Steve? Or guess what they say at the end of that interrogation or that assessment session?  I don’t know. Give it to us. What do they say?  They say thank you, which is extraordinary, and it surprised me when it started happening. But people love to be heard. We’re not selling. We’re not pitching anything. We are just seeking to understand their environment and asking a whole bunch of really good questions. So we don’t need to tell them who we are and what we’ve done, that we’ve won Microsoft Partner of the Year and blah, blah, blah. We don’t need to do any of that.  They know that we know our stuff because of the questions we’re asking, because those questions have been refined and refined and refined. So we’re able to get right into the heart of their issues, and they tell us all about their environment and their issues and concerns and frustrations. And at the end of the assessment, they say, “Thank you.” And I remind them, I say, “We haven’t delivered anything yet. All we’ve done is asked you a whole bunch of questions.” But they feel like it was almost a cathartic process of unloading and sharing all of it. And then we do the second session, and the rapport gets even better, and they bring in some different people.  So by the time we get to the end of the assessment, we’ve had a really good conversation about 120 different things. There’s now a high degree of trust. And so when we come back in the third session, we say, “Okay, we heard you. Here’s where you are. Here’s how you compare to others. Here’s where you could be, and here’s what the journey would look like to get you there, taking all the complexities into account, all the dependencies, all the legacy, all the technical debt you might have. “Here’s what it would take to get you to where you want to be.” They’re now listening and they’re trusting us because we’ve listened to them.  Yeah. I love that. That is very powerful. And asking good questions is actually not always easy. In the age of AI, answers are omnipresent, but good questions, good prompts, that’s a skill, right? To ask the right question.  It is. And it’s interesting you mention AI because we’re not using any AI in the way we do this. We could choose to send out a form to the customer and say, “Please fill out this form.” Actually, we tried that. We tried that 10 or 12 years ago in our New Zealand operation, and it was a complete disaster. Nobody wants to fill out a long form and answer all those questions. But if you have a conversation face-to-face, on Teams or Zoom, whatever, you can have the conversation. You’ll get all the information.  But now we’re establishing rapport between us while we do that, and that also gives them a flavor of what it’s like to work with us. How we interact and how we follow up the question, how we drill down, how we clarify and confirm, “Did I understand that correctly?” in a way that you don’t get from just filling out a web form. And we also don’t use AI to analyze the transcript and try and fill it out. We actually do it based on us understanding it, because we find we get way more detail and nuance than relying on a transcript.  That’s very interesting. Definitely, when there’s a human on the other end who is interested in what you’re saying and is listening deeply, it’s a highly motivating and even inspiring thing. That’s why it’s hard to make a talk without the audience, because you need the energy of the audience. So you provide the energy for them to come up with the goods and explain where they are, right?  Yeah, yeah. So let me switch gears and ask you this. What drives growth in your business?  What drives growth? Two things. One, that process sets us up to be able to sell something. The fastest-growing thing that we sell is a service called mentoring, which is very closely aligned to our brand and the way we work, and it’s something that’s very unique to us. So what we figured out is that there are some customers who just want to bring in a partner to build something for them let’s say to deploy some new technology, and then they want the vendor to go away and leave them with it. There are other organizations who don’t want to touch the technology. They want somebody to provide a managed service, so you get all the outcomes.  And those two technology service categories have been around forever, right? Microsoft has 400,000 partners doing this kind of work, doing project work, or providing managed services. We found a huge white space in the middle between those two. We found there’s a big white space in the mid-market, in particular mid-market organizations, where they have their own IT team. They don’t want to give the keys away to a managed service provider and let go. They want to learn all about the new technology.  They want to internalize the knowledge, and they want somebody to come in and help them deploy it with them, and be hands-on-keyboard, and do it together with them, and do lots of knowledge transfer, and help them build the documentation and the knowledge base, and get the experience and the skills so that their skills grow, their confidence grows. And we call that service mentoring, where we’re doing that for them. So we’re not just building it and walking away, we’re building it together with them. And it can be a one-year, two-year, five-year engagement where we’re building out this complex technology capability, but we’re doing it together, and they become the experts over time. That is our fastest-growing, top-selling service.  That’s fascinating. So it’s essentially coaching, mentoring, and still you turn it into an ongoing engagement because it’s not simple, right? There are a lot of layers to it. And what’s the timeline of such a relationship?  Typically three years. Typically. It can be as short as one, it can be as long as five. But for most of the technology transformation projects we’re doing, I would say they’re two, three, four years for the organization to change and embed all the changes and turn off all the legacy technology and fully embrace the new way of working. So our typical contract is three years. And then we set up all the cadences so that we’re working together every week through specific things, and there’s a weekly cadence, our engineers doing all that stuff, and then monthly coming up for air to see how we’re doing against the roadmap. Do we need to make any changes? What’s the focus for next month? And that’s a rolling process that keeps going on and on. And so the two teams end up working extremely closely.  Our engineering team, and we’re bringing in different architects, engineers, and there’s obviously a consistent project manager across it. And on the customer side, they’re bringing different engineers or architects depending on the piece of work we’re doing. But it’s generally all modernizing the way they work with Microsoft technology primarily, and changing from legacy stuff to very modern, invisible security, embedded AI, really trying to accelerate their maturity as an organization.  Yeah. Love it.  Does that make sense?  So it makes sense. I’m wondering, I mean, most businesses these days, they want to have recurring revenue, and even though you have a three-year engagement, it’s still not an evergreen engagement. So what do you do to turn these engagements into more of an enduring one, or you’re not trying to do it because it’s not the purpose?  That’s the $64 million question for our business, Steve, is how do we turn a two-year or three-year engagement into something longer? And it actually comes back to where you started with the learning thing. So what we need to be doing, and this is a constant battle for us, is learning about the latest and the newest technologies and staying ahead of the customer. Always staying ahead in terms of our knowledge so that we can keep adding new things to the backlog of work that needs to be done.  So that we might deploy technology A and get that done and do the migration and modernization, but then we need to move on to B and C and D and E. And there’s always new stuff, and actually the rate of change is accelerating now with AI. Everything is changing so fast, it’s unbelievable. But our job is to stay ahead so that we can always have something new and edgy that we can bring to the relationship. That we’re always able to give new knowledge, information, value.  So is it a managed technology business or is it more of a consulting business?  Ah, we are three different things. We do a lot of project work where people will come in and say, “Migrate us from platform A to platform B.” So we do that. We do this mentoring service where we’re doing the change and modernization together with the customer. We’re also a managed service provider. We have a lot of small and medium businesses that say, “We don’t have an IT team. We don’t want to have an IT team. Manage this stuff, make it invisible to us. Just give us great service and give us great reports and be transparent with us.” And that’s part of our business too.  So I would say we’re roughly one-third professional services, project work, one-third mentoring, and one-third managed services. So more than half the business is recurring revenue in any given year.  So what makes this kind of business hard to scale, or what makes it easy to scale?  What makes it hard to scale is finding great people who can learn and share that learning with customers, so people who are natural mentors. So what we do not want is to hire geeks who want to just put on noise-canceling headsets and sit in front of a screen coding all day. They would not work for us. We want people who are head up, good people skills, want to learn it, but want to share it. Naturally very good at sharing. So getting the right people, that’s one constraint to growth. The thing that makes growth easy for us is our relationship with Microsoft. That’s unbelievable.  We didn’t have a relationship with Microsoft 11 years ago, and they came to us and asked us to become a partner, and we did. And they asked us to work with them on a specific technology that was very immature at the time. And I set a goal for our team. I said, “What would it take us to become the best in the world at that one specific technology? To become the best in the world on that technology as it matures and grows?” And we set that as our North Star and really went after that and focused everything on that.  And that enabled us then to win Microsoft Global Partner of the Year based on our focus on that. And by winning that award, that got us noticed because they got 400,000 partners. So at the time, I was based in New Zealand, so basically the second-last rock before the end of the earth. And so we were nobody in a nowhere place and not noticed, and we’re just one of 400,000, completely lost in the noise. We got noticed when we won Global Partner of the Year and when we got recognized for being the best in the world at that one technology, and that changed everything, and I moved to America.  Then Microsoft introduced us to a whole bunch of customers, including the largest healthcare organization in the world, the largest education provider in the world, and largest government departments. And so now our business is totally different to what it looked like 11 years ago before Microsoft. And this goes on and on. So almost every day, they contact us to say, “Hey, we got this customer over here, and they got this problem, and we thought of you.” And then they introduce us, and we do our assessment, show them how they compare, build a roadmap, and say, “What would you like to do from here?”  Yeah. I’m just very curious about what is that technology, and what is the ideal customer for you for that technology? That technology is a platform called Intune. It’s a device management platform, so it’s for managing desktops, laptops, MacBooks, smartphones, tablets, and all that. And everything we do, of course, starts with a device. And so it’s the technology that secures and manages and configures our devices. Microsoft Intune is the name of the product, and that’s where we’ve got very, very deep expertise. But that leads us to all the other parts of the Microsoft 365 environment, and most organizations are using Microsoft 365 to some extent.  So in most cases, they’re coming to us and saying, “Hey, this customer has bought our licenses. They’ve got Microsoft 365 E5 or E3, whatever. They’re not using it all. They’ve got all these overlapping competitive products. They want to consolidate, reduce costs, or they want to improve security, or they want to simplify their environment. Can you help?” And we’re like, “Hell yeah, we can help. That’s right in our wheelhouse.” And so we have a discovery call with the customer. They tell us what they want to achieve, and if they’re a good fit for our assessment and roadmap, then we’ll propose that. So we’re not trying to sell anything at that stage, and we’ll do that.  It’s many, many hours of work. We’ll do that roadmap and assessment at our cost. Sorry, that’s our investment. That’s our investment in the relationship, so that we’re showing that we’re giving value back to the customer and to Microsoft because they brought us that lead, and we’re creating value from day one. And sometimes the customer will go, “Thank you. We understand the journey. We’re going to do it ourselves,” or, “We’ve got an existing partner,” or, “We don’t have budget,” whatever. And so we don’t land them all, but we’ve got a very high conversion rate. We land a lot of them.  And even if we don’t, I know that we deliver a huge amount of value through the process, and the customer goes, “Wow, that was great. That was a great process. We learned a lot. We can see where we need to go. We might be back in touch in the future when we do have budget or when the stars align.”  Or they refer someone else.  Yeah.  So if you had a magic wand and you could fix one thing in your business in the next 12 months, what would that be?  I would say it would be doing more with AI internally. Some days I feel like an imposter because we are selling a lot of AI services, and we do a lot of AI work for our customers. But I, as the leader, I don’t do enough. I’m not doing enough with AI. I’m not building enough capability. I’m leading the charge, but I’m not savvy enough myself. And I feel like our leadership team could be doing more. So I feel like doing more, embedding more AI into how we work, is something I want to change or fix in the next year. Not just doing it for customers.  Yeah. I mean, you can never do enough with AI, right? There’s so much out there and so much changing, and to stay ahead of it, one’s head is spinning all the time. It’s very inspiring, but can be also overwhelming.  That’s true. And one of the things I’m dealing with is managing what we call AI slop, where we see people internally and externally producing things that are not good-quality outcomes because it hasn’t been double-checked. So just because it could be produced quickly and easily by AI doesn’t mean it’s great or specific enough or detailed enough or accurate enough for the result, whether that’s an internal report or an external thing.  So the coaching I’m giving people is you’re still responsible for the thing, the quality of the thing. You have to read it, make sure it’s on point, it’s detailed enough, the customer’s going to be happy with it or your manager’s going to be happy with it. We are still responsible for the thing we produce. Let’s not get lazy.  Yeah. And the more content you generate and the more solutions you generate with AI, the more the mental load on the decision-makers to actually filter out what’s not real and what’s real.  Yeah. Yeah. Yeah.  Yeah. I can see that happening. So if someone is listening to this and says, “Wow, I love this idea that you benchmark me, you figure out what our tech is, learn our tech, benchmark us, come up with a roadmap, and evaluate the organization, how fast we can manage change, and then come up with a roadmap and mentor us to get there and simplify our tech stack,” and would like to learn more and perhaps connect with you, where should they go? Where can they find out more information?  I’m on LinkedIn only. I’m not on other social platforms. I’m a bit of a Neanderthal in that regard. But Denis, with one N and O’Shea, O-S-H-E-A, on LinkedIn, and my company is Mobile Mentor. It will be Mentor soon, but we’ll keep the Mobile Mentor alive for quite a while. And so go to the company, mobile-mentor.com. And yeah, love to talk to any of your listeners and audience if this is a technology journey they want to go on or want a partner to work with them. That’s the role we play, being a mentor and helping people get the outcomes, but internalizing it and becoming experts while doing so.  Yeah. So if you want to filter out the AI slop and the AI noise, and you want real people who will look at your specific situations and help you and mentor you and help your staff get up to speed and simplify, then reach out to Denis. And if you enjoyed this conversation, stay tuned because every week I bring a couple of entrepreneurs who are sharing their frameworks of how they’re improving and growing their businesses, and you can implement some of these things yourself.  So thanks, Denis. Denis O’Shea, the founder and CEO of Mobile Mentor, soon to be called Mentor. Thanks for coming on the show, and thank you for listening. Thank you, Steve. Always a pleasure. Important Links: Denis's LinkedIn Denis's website

Escucha martes y viernes la opinión de Jorge A. Meléndez.

Nvidia kauft Hugging Face, und wir rechnen nach, was das 86-fache des Umsatzes für einen Marktplatz rechtfertigt. Über hundert Firmen rufen gemeinsam zur Cyberabwehr auf, nachdem 1.200 Agenten sich in einem Sicherheits-Benchmark ein eigenes Message-Board aus Ordnernamen gebaut haben. OpenAI zeigt Zahlen zu seinem Chip Jalapeño und schlägt damit Nvidia? Anthropic bereitet den Börsengang vor und ruft dabei eine Zahl auf, die selbst SpaceX übertrifft. Meta beschimpft Anthropic öffentlich und ist gleichzeitig einer der größten Kunden. Dazu ein Vergleich über 17 Milliarden, mit dem Meta nebenbei die Regeln für TikTok und YouTube mitschreibt. Aus China kommt die Auflösung des Ox-Alpha-Rätsels und eine neue Runde für DeepSeek. Nvidia legt an einem Tag mehr zu als in seinen ersten zwanzig Jahren, Salesforce springt um 22 Prozent, und die SaaSocalypse wird offiziell abgesagt.  Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Nvidia kauft Hugging Face (00:11:49) Cyber-Aufruf der Industrie (00:20:46) OpenAIs Jalapeño (00:24:58) Anthropics Börsengang (00:37:03) Meta und Anthropic (00:38:52) Metas Parkticket (00:46:59) DeepSeek sammelt ein (00:48:07) Ox Alpha aufgelöst (00:51:02) Nvidia-Zahlen (01:10:07) SaaSocalypse abgesagt (01:16:55) Claude Force (01:21:19) CrowdStrike (01:24:45) XM Cyber und Lidl (01:26:02) CLIQ Digital (01:27:40) Teslas Solardach (01:28:38) Kofler-Ermittlungen (01:32:19) Grok-Klage (01:35:13) Metas Projekt OT (01:38:39) Rechenzentrum bei Rostock (01:41:07) Waymo in München (01:42:09) Pentagon-Bann gekippt (01:43:57) Erbschaftsteuer-Rekord Shownotes Nvidia kauft Hugging Face für 12,9 Mrd. - cnbc.com Über 100 Firmen fordern eine gemeinsame Cyberabwehr - reuters.com Der gemeinsame Aufruf im Wortlaut - openai.com Wie 700 Agenten Hugging Face angegriffen haben - metr.org OpenAIs Jalapeño schlägt Nvidia im Inferenz-Benchmark - theverge.com Was der Benchmark verschweigt - semianalysis.com Anthropic nennt Investoren über 30 Billionen Marktpotenzial - wsj.com Meta beschimpft Anthropic und ist einer der größten Kunden - nytimes.com Meta zahlt bis zu 16,7 Mrd. an 52 Bundesstaaten - axios.com DeepSeek steuert auf 74 Mrd. Bewertung zu - wsj.com Z.ai bestätigt: Ox Alpha ist GLM 5.3 Flash - bloomberg.com Nvidia stellt 70 Prozent Wachstum für 2028 in Aussicht - wsj.com Benioff und Amodei gemeinsam bei CNBC - linkedin.com Salesforce springt 18 Prozent, 2,6 Mrd. davon aus Anthropic - cnbc.com CrowdStrike nennt es den Mythos-Moment - cnbc.com CLIQ Digital verliert 77 Prozent Umsatz - globenewswire.com Okta legt 20 Prozent zu, weil Agenten Identitäten brauchen - cnbc.com ServiceNow gewinnt 29 Prozent im August - achmadnurhidayat.id Teslas Solardach ist eingestellt - techcrunch.com Staatsanwaltschaft ermittelt gegen Georg Kofler - businessinsider.de Klage: Grok wurde auf Missbrauchsmaterial trainiert - mashable.com Wie LAION seinen Datensatz bereinigt hat - laion.ai Metas Plan, Teams um 60 Prozent zu kürzen, ist gescheitert - reuters.com Schwarz Gruppe baut für 5,6 Mrd. bei Rostock - manager-magazin.de Waymo startet 2027 in München - bloomberg.com Waymos eigene Ankündigung zu München - waymo.com Gericht kippt den Pentagon-Bann gegen Anthropic - washingtonpost.com Die Richterin nennt es unlawful retaliation - ft.com Rekord bei der Erbschaftsteuer, 21,4 Mrd. festgesetzt - tagesschau.de 37 Milliarden Euro Steuern sind Großerben - linkedin.com

Nvidia posted $96B in quarterly revenue and guided to 70% growth next year, then agreed to buy Hugging Face for $12.9B. Trump weighed sweeping chip tariffs, cybersecurity stocks ripped on AI threats, and Instinct raised at $2.5B. Links Nvidia reports Q2 revenue up 106% YoY to $96.22B, above $92.17B est., Data Center revenue up 117% to $89B, above $85.08B est., and net income up 126% to $59.7B (Nvidia) Nvidia guides to ~70% revenue growth next fiscal year, well above the 45% analysts expected, sending shares up as much as 7.6%, though margins will bottom at 71%-72% on memory costs (Bloomberg) Source: Nvidia has agreed to acquire Hugging Face for $12.9B; the AI repository has had several potential suitors among its investors, including Salesforce (The Information) Sources: the Trump administration is weighing sweeping new tariffs on chips and other products like laptops and consoles, despite warnings from tech companies (Politico) Cybersecurity stocks surge, with Okta up 20%+ and CrowdStrike up 15%+, after earnings showed that AI adoption is driving attacks and spending on security tools (CNBC) AI assistant Instinct is raising a $250M Series B co-led by Index and Benchmark at a $2.5B valuation, taking its total funding to $350M since its 2025 founding (The Wall Street Journal) Subscribe to the ad-free feed.

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Peter Caputa, CEO of Databox and the architect behind HubSpot's legendary partner ecosystem, returns to unpack why having more data has not made decision-making any easier, and what it actually takes to close that gap. Brendon and Pete dig into the four pillars of revenue operations, why franchise systems are uniquely positioned to win with AI, and how the most forward-thinking organizations are moving from dashboards to autonomous, self-correcting business systems. If you're a RevOps leader, franchise executive, or operator trying to turn data into a competitive advantage, this episode reframes what's now possible.What You'll LearnWhy more data doesn't automatically mean better decisionsThe four pillars of RevOps: people, process, data, technologyWhat anomaly detection can do for multi-location brandsHow to daisy-chain processes into a scalable systemThe franchise visibility gap and how to close itWriting down processes before automating themWhy AI makes this a bigger shift than the internetResources MentionedDatabox"The Cold Start Problem" — Andrew Chen Is your business ready to scale? Take the Growth Readiness Score to find out. In 5 minutes, you'll see: Benchmark data showing how you stack up to other organizationsA clear view of your operational maturity Whether your business is ready to scale (and what to do next if it's not)Let's ConnectSubscribe to the RevOps Champions NewsletterLinkedInYouTubeExplore the show at revopschampions.com. Ready to unite your teams with RevOps strategies that eliminate costly silos and drive growth? Let's talk!

Hey Girl,In this solo episode, I break down your dating benchmark: the past relationship or experience you may unknowingly be using to measure everything that comes after it. We talk about why some of your old relationship experiences can shape what you believe is possible in love, and why it may be time to create a new standard based on how you actually want to feel in a relationship. I also share a sneak peek into the conversations we have with my private clients and how shifting your benchmark can change the way you approach your dating journey.Whenever You Are ReadyHere are 4 ways I can help you:​ Pre-Order My Book: Girl, Get Your Guy! - Starting this July, all the way until the official book launch on February 2, 2027, when you pre-order this book,you'll get:

"The expectation level of procurement is rising. Today's stuff that was not possible a year ago, that felt like science fiction three years ago, is something that we can create for our customers today." - Sammeli Sammalkorpi, Co-founder & CEO, Sievo For CPOs and their teams, procurement data has never been more abundant… or more challenging to harness. Demands go far beyond savings: teams are being asked to link procurement value to broader business goals, manage complexity, and adapt to the rapid rise of AI. In this episode of the ProcureTech Insider we speak with Sammeli Sammalkorpi, Sievo's co-founder and CEO, for a candid "Provider of the Week" discussion about what it takes to turn chaotic data into actionable business value.  With Sievo handling data equivalent to more than two percent of global GDP, Sammeli shares why long-term data investment, proprietary benchmarks, and hands-on integration are making the biggest difference for large enterprises. In this episode, Sammeli also covers how to: -Build an enterprise-wide data foundation ready for AI and rapid business change -Turn procurement analytics into prioritized, actionable insights -Benchmark your spend and risk using truly global, anonymized industry data -Translate procurement results into the language of margin, revenue, and business value   Links: Sammeli Sammalkorpi on LinkedIn: https://www.linkedin.com/in/sammeli-sammalkorpi/  Visit the Sievo profile in the AOP Provider Directory: https://artofprocurement.com/provider-directory/sievo  Subscribe to the AOP Newsletter: https://resources.artofprocurement.com/art-of-procurement-podcast-subscribe  Subscribe to Art of Procurement on YouTube: https://www.youtube.com/@ArtofProcurement   

Escucha martes y viernes la opinión de Jorge A. Meléndez.

Escucha martes y viernes la opinión de Jorge A. Meléndez.

Bob Evans speaks with Chad Wahlquist, an Architect at Palantir, about what is driving the company's extraordinary growth and, more importantly, what customers are getting from its technology. Wahlquist argues that Palantir's momentum comes from helping enterprises solve difficult operational problems rather than simply deploying AI or chasing model benchmarks. Their conversation explores AI sovereignty, business outcomes, Palantir's Ontology, LLM complexity, customer operating leverage, and the importance of retaining control over enterprise decision-making. Outcomes Over AI Hype   The Big Themes: Outcomes Drive Palantir's Growth: Wahlquist connects Palantir's growth to the tangible returns customers see after adopting its technology. Rather than treating AI as a standalone investment or another piece of enterprise software, customers increasingly expand their Palantir relationships because successful initial projects create opportunities for broader deployment. He points to strong net dollar retention as evidence that existing customers are spending more after experiencing ROI. The underlying philosophy is straightforward: when an investment generates meaningful business value, executives are willing to repeat and expand it. Production LLMs Create New Problems: Getting an LLM working is only the beginning. Wahlquist discusses the stochastic and probabilistic nature of models, changing provider guardrails, security requirements, model deprecations, and unpredictable behavior across edge cases. Technically switching from one model to another might appear simple, but ensuring that a replacement works reliably across production workflows is significantly harder. This creates a fundamental enterprise question: how do businesses build durable operations on technology whose behavior and availability can change? Protect the Enterprise Decision Loop: One of the conversation's most important ideas is that a business can be understood as a collection of decisions. Companies continually observe data, apply logic, take actions, measure outcomes, and adjust future decisions. Wahlquist calls that feedback loop a source of business “alpha” — the proprietary knowledge that helps one organization outperform another. As AI agents become participants in enterprise decision-making, ownership of that loop becomes increasingly important. Companies should understand who controls the data, logic, actions, outcomes, and learning generated through those processes. The Big Quote: “LLMs don't just magically fix everything. They also create new problems that you have to go solve.” Visit Cloud Wars for more.

Tune-up races are a common piece of many training plans. Are they necessary? Why should you bother racing in the middle of training?In this episode, we discuss all you need to know about how to run an effective tune-up race (and whether to run one at all). We draw from years of coaching experience and an understanding of various training theories to guide you through how to incorporate non-goal races into your training, in a way that does not detract from your goal race. You will learn how to schedule them, whether you need to taper, and how to recover. Plus, we will provide guidance for what to do if doing tune-up races just is not for you.In this episode, you'll learn:✅ The psychological and physiological benefits of a tune-up race✅ How to schedule tune-up races in your training✅ Which distance tune-up race should you choose?✅ Different pacing approaches for tune-up races✅ If you should taper for a tune-up race✅ How to recover after a goal race✅ Benchmark workouts as substitutes for a tune-up raceTread Lightly Running is hosted and researched by Amanda Brooks and Laura Norris, MSc. Production, show notes, and graphics by Laura Norris.References

Selling the equipment is the easy part. The real revenue shows up after the sale.Sam Neva, Vice President of Sales for Service Lifecycle Management at PTC, joined the show to talk servitization - what it means, and why it's becoming the difference between OEMs who scrape by on hardware margins and ones who build real recurring revenue. Chris caught up with him live at PTC's headquarters.Manufacturing folks, we didn't forget about you! This episode is geared squarely toward people building equipment. If you want to tighten up your sales process and make your customer relationships stickier, this one is for you.Sam and Chris get into what's holding most OEMs back from servitization, how AI is starting to change what proactive service looks like, and the math behind why the money shows up years after the original sale.In this episode, find out:Why the smartest OEMs are lowering the price of the original sale, and what they're betting on insteadThe three levels standing between a traditional OEM and a full servitization modelWhy ‘tribal knowledge' is the hidden gap between engineering and service teamsHow AI is helping teams triage service issues before they need a technicianThe Harvard Business Review stat that shows just how much revenue gets left on the table post-saleWhat Schneider Electric did to grow revenue, win rates, and improve customer satisfaction at the same timeThe first question every OEM should ask before attempting servitizationEnjoying the show? Please leave us a review here. Even one sentence helps. It's feedback from Manufacturing All-Stars like you that keeps us going!Tweetable Quotes:“You're reacting to a problem, instead of doing your best to extend and plan that time horizon to prepare for that problem." - Sam Neva, Vice President of Sales for Service Lifecycle Management at PTC“Step 1 is what do customers expect from us, why do they expect that, and how can we give them that?" - Sam Neva, Vice President of Sales for Service Lifecycle Management at PTC“The goal is how do I make money as the OEM, not just by selling you that box, but selling you the outcome of what that box provides.” - Sam Neva, Vice President of Sales for Service Lifecycle Management at PTCLinks & mentions:Benchmark your servitization strategy, a complimentary assessment where ServiceMax experts will evaluate your current service organization and identify opportunities to improve performance, profitability, and customer outcomes.ServiceMax, a PTC Technology, is on a mission to help customers keep the world running with asset-centric field service management software. As a recognized leader in this space, our cloud-based software and mobile apps provide a complete view of assets to field service teams.Make sure to visit https://manufacturinghappyhour.com for detailed show notes and a full list of resources mentioned in this episode. Stay Innovative, Stay Thirsty.

Aaron Binstock, Partner, Co-Head of Private Equity Practice at Cooley LLP AI can now draft, review, and benchmark deal documents in a fraction of the time it used to take, but knowing when to trust the output is a different skill entirely. Aaron Binstock, a partner at Cooley with nearly 20 years of transactional experience, has seen both sides of that tradeoff firsthand. Where does AI actually save time on a deal, and where does it create false confidence? What happened when a client's AI-generated tax step chart was built on the wrong assumption? How does reverse prompting produce a better first draft than a single one-shot prompt? And what's changing about how junior lawyers build judgment, and how firms bill for their time? What You'll Learn Where AI reliably speeds up NDA markups versus bespoke merger agreements How reverse prompting turns a mediocre AI output into a usable first draft The tax step chart mistake that nearly cost a client millions in consideration or tax How cross-deal benchmarking pulls survival periods, caps, and baskets into one reference chart Why some clients and counterparties are opting out of AI entirely, and how firms track it What junior lawyer training looks like once document grinding stops teaching judgment Why AI can produce a report but still can't own the result   If you're dealing with AI tools that sound confident but don't actually know M&A, DealPilot, powered by M&A Science experiential data, has guidance built from practitioners who've actually run the deal to help you catch what AI can't see coming. ____________________ The Buyer-Led M&A™ Summit is back August 18th, free and virtual. We're releasing the State of AI in M&A 2026 report live at the event before it goes public. Benchmark your program, hear from practitioners across the industry, and leave with a clearer picture of where dealmaking is headed. Register here: https://hubs.ly/Q04kBhzV0 ____________________ Episode Chapters [00:00] Intro [00:03:12] Aaron's Path Into M&A [00:05:12] Cooley's Public AI Commitment [00:07:22] Where AI Fits On A Deal [00:11:37] Quality Control And AI Playbooks [00:16:33] The Tax Step Chart Mistake [00:18:41] How Reverse Prompting Works [00:22:19] Benchmarking Past Deals With AI [00:23:13] Lockbox Pricing And Prompt Quality [00:25:33] When Clients Say No To AI [00:33:06] AI's Impact On Legal Billing [00:35:44] Training Lawyers In The AI Era [00:42:20] Why AI Can't Own The Deal [00:44:07] Craziest Moments In M&A Deals

My guest today is Eric Vishria, a General Partner at Benchmark.  Eric has spent his career in software and cloud, and few people know the history of these markets as well as he does. What makes him special is his ability to use that history to make sense of today.  We discuss what the rise of AWS teaches us about AI, what he has learned from investing in Fireworks, Sierra, and Cerebras, and how the criteria for winning have changed for founders and investors.  Please enjoy my conversation with Eric Vishria. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:20) Learning the World Through Fireworks (00:05:42) AWS Was Going to Eat Everything (00:07:40) The Zero-Sum Thinking Trap (00:09:01) Comparing Cloud and AI Adoption (00:11:03) Becoming Enterprise's AI Sherpa (00:13:05) Building Sandcastles (00:14:55) The Return to Being Technical (00:17:13) The Shifting Competitive Frontier (00:22:10) Why the Old Playbook Fails (00:27:53) Energy as the Binding Constraint (00:29:38) The Cerebras Story (00:37:57) The Virtue of Productive Naivete (00:39:19) What Robotics Still Needs (00:45:58) What Makes a Great Board Partner (00:51:13) Raising A Growth Fund (00:55:39) What the Big Winners Taught Him (00:57:37) Hard Work Versus the Hole-in-One (00:58:38) The Best Reasons to Go Public (01:01:09) Debates Inside Benchmark (01:02:16) What If It All Works (01:03:35) What Geoff Hinton Got Wrong

My guest today is Gavin Baker, founding partner and CIO of Atreides Management. This is our seventh conversation, and just two months after Gavin's last appearance. It's about the gap between what the market is doing and what companies are seeing. It's been a tough month or so for public AI names, but there's no sign of a slowdown on the ground in Silicon Valley. We discuss the latest moves, contracted vs. spot GPU prices, the game theory of memory supply agreements, and why Claude has become the Walter Cronkite of the stock market. We close on SpaceX, orbital compute, and what Gavin sees as the single biggest risk to all of it. Please enjoy this conversation, from the famous table at Benchmark, with my friend Gavin Baker. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at colossus.com/subscribe. ----- Ramp's mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to ramp.com/invest to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, Vanta continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to vanta.com/invest.  ----- WorkOS is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit ridgeline.ai. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:35) First Question: July Was 2022 in a Month (00:04:08) The Private Companies Public Markets Can't See (00:05:06) Old GPUs Repricing Higher (00:06:53) Walking Through the Month (00:08:22) Kimi, GLM 5.2 & the Open Source Freak-Out (00:10:51) Real Yields, Spreads & CDS (00:11:54) Does the Build-Out Need Credit? (00:15:22) A Sell-Off With No Clear Villain (00:17:35) Open Source as Dark Matter (00:18:39) Nvidia's Lowest Forward PE in 10 Years (00:21:35) Claude as Walter Cronkite for the Stock Market (00:23:55) Continual Learning & Sample Efficiency (00:25:19) What Would Actually Scare Him (00:26:38) Routers & the Multi-Model Future (00:30:51) Tokens as a Percent of Comp Spend (00:33:37) The Game Theory of Breaking an LTA (00:36:41) Nvidia's Credit Wrapper & Revenue Share (00:37:45) What He'd Do If He Ran Hynix (00:41:46) Who's More Bullish than Him (00:43:28) China's DUV Machine (00:46:10) Bull Case for Software (00:48:16) The RSI Maximalist View (00:49:31) Inference Clouds Growing Without Burning Cash (00:50:35) The Biggest Risk Is Regulation (00:53:44) Telling the Story Better (00:57:15) Dark Horses (00:58:02) SpaceX in the Public Markets