Podcasts about companies

A.M. Edition for Feb. 24. The Trump administration is considering new national security tariffs on a half-dozen industries, after the Supreme Court last week invalidated many of the president's second-term levies. That ruling has prompted companies like FedEx, Revlon and Costco to file suit. Plus, President Trump is expected to tout the U.S. economy in his State of the Union later. But as WSJ's Alex Frangos explains, the economic report card is a bit more mixed. And, Ukraine marks a grim milestone as the war with Russia enters its fifth year. Daniel Bach hosts. A look at Apple's push to build an all-American chip. Sign up for the WSJ's free What's News newsletter. Learn more about your ad choices. Visit megaphone.fm/adchoices

Josh Rogin examines whether the US-China rivalry is a new Cold War and how the CCP enforces its political agenda on American companies like the NBA through exported censorship. 5

We look back at stories of companies that were disrupted -- Siebel Systems and Apple (NASDAQ: AAPL( -- to better understand how disruption emerges and whether history can be a guide for disruption during the AI paradigm shift. Asit Sharma, David Meier, and Tim Beyers discuss: - Disruption stories from history. - The three signs of disruption and why they matter now more than ever. - Two companies that may be at serious risk for disruption now and for the long term. Don't wait! Be sure to get to your local bookstore and pick up a copy of David's Gardner's new book — Rule Breaker Investing: How to Pick the Best Stocks of the Future and Build Lasting Wealth. It's on shelves now; get it before it's gone! Companies discussed: FIG, TOST, CRM, HUBS, TTD Host: Tim Beyers Guests: Asit Sharma, David Meier Producer: Anand Chokkavelu Engineer: Dan Boyd Disclosure: Advertisements are sponsored content and provided for informational purposes only. The Motley Fool and its affiliates (collectively, “TMF”) do not endorse, recommend, or verify the accuracy or completeness of the statements made within advertisements. TMF is not involved in the offer, sale, or solicitation of any securities advertised herein and makes no representations regarding the suitability, or risks associated with any investment opportunity presented. Investors should conduct their own due diligence and consult with legal, tax, and financial advisors before making any investment decisions. TMF assumes no responsibility for any losses or damages arising from this advertisement. We're committed to transparency: All personal opinions in advertisements from Fools are their own. The product advertised in this episode was loaned to TMF and was returned after a test period or the product advertised in this episode was purchased by TMF. Advertiser has paid for the sponsorship of this episode. Learn more about your ad choices. Visit ⁠⁠⁠⁠⁠megaphone.fm/adchoices⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Episode Title: Chaos, Radicalization & Political Retribution: America on Edge Runtime: ~45–50 minutes Tone: Urgent, edgy, investigative

Send a text"RAISE CAPITAL LIKE A LEGEND: https://go.fundraisecapital.co/apply"How do you turn a single student rental house into a $10 billion real estate empire? In this episode of Making Billions, host Ryan Miller sits down with Jason Castellan, the Co-Founder and CEO of Skyline Group of Companies, to deconstruct the journey of building one of Canada's most successful private equity and alternative asset management firms.Jason reveals the three major inflection points that shifted Skyline from a small-town operation in Guelph, Ontario, to an institutional powerhouse.Whether you're interested in syndications, REIT structures, or clean energy infrastructure, Jason's "Crawl, Walk, Run" philosophy provides the blueprint for sustainable growth and multi-generational wealth.Subscribe on YouTube:https://www.youtube.com/channel/UCTOe79EXLDsROQ0z3YLnu1QQConnect with Ryan Miller:Linkedin: https://www.linkedin.com/in/rcmiller1/Instagram: https://www.instagram.com/makingbillionspodcast/X: https://x.com/_MakingBillionsWebsite: https://making-billions.com/[THE HOST]: Ryan Miller is a recovering CFO turned angel investor in technology and energy.[THE GUEST]: Jason Castellan is the CEO and Co-Founder of Skyline Group of Companies, leading the strategic direction across all business units, including asset acquisitiSupport the showDISCLAIMER: The information in every podcast episode “episode” is provided for general informational purposes only and may not reflect the current law in your jurisdiction. By listening or viewing our episodes, you understand that no information contained in the episodes should be construed as legal or financial advice from the individual author, hosts, or guests, nor is it intended to be a substitute for legal, financial, or tax counsel on any subject matter. No listener of the episodes should act or refrain from acting on the basis of any information included in, or accessible through, the episodes without seeking the appropriate legal or other professional advice on the particular facts and circumstances at issue from a lawyer, finance, tax, or other licensed person in the recipient's state, country, or other appropriate licensing jurisdiction. No part of the show, its guests, host, content, or otherwise should be considered a solicitation for investment in any way. All views expressed in any way by guests are their own opinions and do not necessarily reflect the opinions of the show or its host(s). The host and/or its guests may own some of the assets discussed in this or other episodes, including compensation for advertisements, sponsorships, and/or endorsements. This show is for entertainment purposes only and should not be used as financial, tax, legal, or any advice whatsoever.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Jake Hamilton, founder of Groundwire and Nockbox, to explore zero-knowledge proofs, Bitcoin identity systems, and the intersection of privacy-preserving cryptography with AI and blockchain technology. They discuss how ZK proofs could offer an alternative to invasive identity verification systems being rolled out by governments worldwide, the potential for continual learning AI models to shift the balance between centralized and open-source development, and why building secure, auditable computing infrastructure on platforms like Urbit matters more than ever as we face an explosion of AI agents and automated systems. Jake also explains Nockchain's approach to creating a global repository of cryptographically verified facts that can power trustless programmable systems, and how these technologies might converge to solve problems around supply chain security, personal data sovereignty, and resistance to censorship.Timestamps00:00 Introduction to Groundwire and Knockbox02:48 Understanding Zero-Knowledge Proofs06:04 Government Adoption of ZK Proofs08:55 The Future of Identity Verification11:52 AI and ZK Proofs: A New Era14:54 The Role of Urbit in Technology18:03 The Impact of COVID on Trust20:51 The Evolution of AI and Data Privacy23:47 The Future of AI Models26:54 The Need for Local AI Solutions29:51 Interoperability of Knockchain and BitcoinKey Insights1. Zero-Knowledge Proofs Enable Privacy-Preserving Verification: Jake explains that ZK proofs allow you to prove computational outcomes without revealing the underlying data. For example, you could prove you're over 18 without exposing your full identity or driver's license information. The proof demonstrates that a specific program ran through certain steps and reached a particular conclusion, and validating this proof is fast and compact. This technology has profound implications for age verification, identity systems, and protecting privacy while maintaining necessary compliance, potentially offering a middle path between surveillance states and complete anonymity.2. Government Adoption of Privacy Technology Remains Uncertain: There are three competing motivations driving government identity verification systems: genuine surveillance desires, bureaucratic efficiency seeking, and legitimate child protection concerns. Jake believes these groups can be separated, with some officials potentially supporting ZK-based solutions if positioned correctly. He notes the EU is exploring ZK identity verification, and UK officials have shown interest. The key is framing privacy-preserving technology as protection against "the swamp" rather than just abstract privacy benefits, which could resonate with certain political constituencies.3. The COVID Era Destroyed Institutional Trust at Unprecedented Scale: The conversation identifies COVID as potentially the largest institutional trust-burning event in human history, with numerous institutions simultaneously losing credibility with large portions of the population. This represents a dramatic shift from the boomer generation's default trust in authority figures and mainstream media. This collapse is compounded by the incoming AI revolution, creating a perfect storm where established bureaucracies cannot adapt quickly enough to manage rapidly evolving technology, leaving society in fundamentally unmanageable territory.4. Centralized AI Models Create Dangerous Dependencies: Both speakers acknowledge growing dependence on centralized AI services like Claude, with some users spending thousands monthly on tokens. This dependency creates vulnerability to price increases and service disruptions. Jake advocates for local AI deployment using models like DeepSeek R1, running on personal hardware to maintain control and privacy. The shift toward continuous learning models will fundamentally change the AI landscape, making personal data harvesting even more valuable and raising urgent questions about compensation and consent for training data contribution.5. High-Quality Training Data Is Becoming the Primary AI Bottleneck: Stewart argues that AI development is now limited more by high-quality training data than by compute power. The industry has exhausted easily accessible internet data and body-shop-style data labeling. Companies are now using specialized boutique services with techniques like head-mounted cameras for live-streaming world model training. This scarcity is subtly driving price increases across AI services and will fundamentally reshape the economics of AI development, with implications for who controls these increasingly powerful systems.6. Urbit Offers a Foundation for Trustworthy Computing: Jake positions Urbit as essential infrastructure for the AI age because its 30,000-line codebase (versus Unix's three million lines) can be understood by individual humans. Its deterministic, purely functional, and strictly typed design aims for eventual ossification—software that doesn't require constant security patches. This "tiny and diamond perfect" approach addresses the fundamental insecurity of systems requiring monthly vulnerability patches. In an era of AI agents and potential prompt injection attacks, having verifiable, comprehensible computing infrastructure becomes existentially important rather than merely desirable.7. Nockchain Creates a Global Repository of Provable Truth: Jake's vision for Nockchain combines ZK proofs with blockchain technology to create a globally available "truth repository" where verified facts can be programmatically accessed together. This enables smart contracts or programs gated on combinations of proven facts—such as temperature readings from secure devices, supply chain events, and payment confirmations. By using Nock's abstract, simple design optimized for ZK proof generation, the system can validate complex real-world conditions without exposing underlying data, creating infrastructure for coordinating action based on verifiable private information at global scale.

Here is a FAQ Video on the Courses: https://youtu.be/0F7imrzjXWs Here is a deep dive into which course is best for you: https://youtu.be/JM_jgS8M-iU https://www.b2bRevenue.com - Get Your Free E-Book on How Companies make Decisions. FAQ: 1 YEAR ACCESS, PAY MONTHLY OR ANNUALLY NOT A SUBSCRIPTION OFFICE HOURS EVERY  OTHER WEEK VIA ZOOM. 1 HOUR GROUP Q&A. UNLIMITED 1-ON-1'S  ARE FREE AS LONG AS THEY CAN BE SHARED IN THE COURSE. 1-ON-1 ARE FULL ACCESS ON DAY ONE - NOTHING IS GATED OR TIME RELEASED. ALL CONTENT IS VIDEO BASED AND SELF PACED I RECOMMEND TAKE COURSE ONCE WITHOUT NOTES OR APPLYING IT SO YOU UNDERSTAND THE BIG PICTURE FIRST. THEN TAKE AND APPLY IT STEP BY STEP. YOU START WHEN YOU WANT AND GO AS FAST OR SLOW AS NEEDED.   Email me additional questions: briangburns@me.com     — SAMPLE EMAIL TO EXPENSE THE COURSE MGR,   I have been listening to the brutal truth about sales podcast for X months and it speaks to the issues we face.   They currently offer a course that includes video instruction, group Q&A and One-on-One coaching. I'm committed to my own personal development and would like your help in expensing the course.   It would pay for itself if I closed only one new deal of $X value.   Please let me know by Friday if I can move forward with this 1 year course.   Thanks, ME Here are some student interviews from the courses:      ———————————————————————————————————— Audible 30 day Free Trial: http://www.audibletrial.com/BrutalTruth  

In this episode of the Game Deflators podcast, hosts John and Ryan discuss their recent game pickups, including horror titles and RPGs, and share insights on the gaming industry, including Sony's monetization strategies and the closure of Bluepoint Games. They also review Michael Jackson's Moonwalker, highlighting its gameplay and nostalgic value, while addressing broader industry trends and controversies in fast news segments. 00:00 Introduction to the Game Deflators Podcast 01:27 Recent Game Pickups and Discussions 06:00 Current Gameplay Experiences 10:12 Fast News and Industry Insights 24:53 The Necessity of Mini Games in Storytelling 27:50 Monetization Strategies for PS5 33:00 The Closure of Bluepoint Games 38:42 Michael Jackson's Moonwalker: A Retro Review   Find us on TheGameDeflators.com Twitter - www.twitter.com/GameDeflators Facebook - www.facebook.com/TheGameDeflators Instagram - www.instagram.com/thegamedeflators   The views and opinions expressed on this channel are solely those of the author. The content within these recordings are property of their respective Designers, Writers, Creators, Owners, Organizations, Companies and Producers. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted. Permission for intro and outro music provided by Matthew Huffaker http://www.youtube.com/user/teknoaxe 2_25_18

As we prepare for the 2026 Player Development Summit in Detroit, Michigan (May 29–30), this episode gives you a clear and honest look at what this experience is really about.This is not just another conference.This is a room built for the people doing the unseen work in athletics.In this episode, you'll hear:• A 2026 Summit insight explaining exactly who this event is designed for• A powerful speaker spotlight from the 2025 Summit• A real attendee reflection from Kansas City that captures the heart of this community

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In this episode of Run the Numbers, CJ sits down with Mateo Bryant, CFO of Minted. They break down Minted's life-event flywheel and decades-long LTV, managing extreme seasonality when half the year happens in one month, and balancing long-term CAC with short-term monetization. Mateo also shares lessons from scaling Uber and Amazon globally, localization missteps, and making marketplaces work in emerging markets.—SPONSORS:Abacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.aiBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsMetronome is real-time billing built for modern software companies. Metronome turns raw usage events into accurate invoices, gives customers bills they actually understand, and keeps finance, product, and engineering perfectly in sync. That's why category-defining companies like OpenAI and Anthropic trust Metronome to power usage-based pricing and enterprise contracts at scale. Focus on your product — not your billing. Learn more and get started at https://www.metronome.comRightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjTabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/run—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNMateo: https://www.linkedin.com/in/bryantmatt/Minted: https://www.minted.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:Peter Oey, CFO of Grab:https://youtu.be/tdq0AZO0dLU—TIMESTAMPS:00:00 Intro03:16 Fixer to CFO05:32 Mexico City Startups09:00 Minted Flywheel10:24 LTV Expansion11:04 Entry Points12:18 CAC and Cohorts13:42 Sponsors: Metronome | RightRev | Rillet17:06 Wedding Lifecycle19:49 Holiday Forecasting22:23 Retail Calendar24:03 Cash Flow Swings25:05 Marketing Over Sales26:06 Email Limits27:41 Sponsors: Tabs | Abacum | Brex31:02 Retail Strategy35:08 Global Experience40:47 Uber Cash Economics46:04 Cost of Not Localizing50:19 Importer of Record53:17 No Google Lesson55:34 QBR Mistake56:48 High Leverage Hours59:03 Finance Stack59:50 Seven Day Cruise Expense#RunTheNumbersPodcast #MarketplaceStrategy #EcommerceFinance #GigEconomy #CFOInsights

Plus is Roku a buy right now?

The Supreme Court is taking up a landmark climate case this fall, pitting a medium sized county against fossil fuel corporations. The AP's Jennifer King reports.

What if the biggest barrier to learning AI isn't the tools—it's how we approach learning itself?In this episode of Supra Insider, Marc Baselga and Ben Erez sit down with Gagan Biyani, CEO and co-founder of Maven, to unpack why this moment is critical for mid-career professionals to prioritize self-learning. Gagan shares lessons from running a cohort-based learning platform and conducting 30-50 interviews with companies struggling to adopt AI. He explains why AI is like witnessing the internet as a child—you can't afford not to learn it—and why building the learning habit matters more than what you learn first.They explore the five problems companies face with AI education: trying to generalize training when every role needs different tools, listening to tinkerers instead of bridge adopters, and delegating to chiefs of staff instead of having C-level sponsors run the trainings. Gagan shares Maven's own journey—why their design team needed to rebuild the design system before AI could be useful, how they're changing team ratios from 3-4 engineers per designer to just 2, and why social media is terrible for learning anything that requires weeks of dedication.If you're a mid-career professional feeling overwhelmed by AI, a leader trying to build a culture of self-learning at your company, or wondering how to actually integrate AI into your workflows—this episode is for you.All episodes of the podcast are also available on Spotify, Apple and YouTube.New to the pod? Subscribe below to get the next episode in your inbox

Is test prep a lifeline or a scam? Jessica Wynn reveals who's really cashing in on your SAT anxiety here on Skeptical Sunday!Welcome to Skeptical Sunday, a special edition of The Jordan Harbinger Show where Jordan and a guest break down a topic that you may have never thought about, open things up, and debunk common misconceptions. This time around, we're joined by writer and researcher Jessica Wynn!Full show notes and resources can be found here: jordanharbinger.com/1288On This Week's Skeptical Sunday:The test prep industry is a multi-billion-dollar machine built on manufactured anxiety — not better education. Companies exploit the fear that a single test determines your entire future, turning parental stress and student panic into a lucrative marketplace where confusion plus fear equals profit.The same corporations that create standardized tests often sell the prep materials to pass them — a staggering conflict of interest. It's vertical integration at its most cynical: they've engineered both the problem and the solution, and students pay on both ends.Standardized tests like the SAT don't predict college success as well as high school GPA does, and access to expensive prep widens inequality rather than leveling the playing field. Kids in the top 1% of income have a 1 in 4 shot at elite schools — kids in the bottom 20% have a 1 in 300 chance.Social media has supercharged test prep anxiety, turning studying into a performative competition. Students spiral comparing their materials and scores to strangers online, and prep companies profit without even advertising — the students do it for them through posts and affiliate links.You don't need to spend a fortune to prepare well. Start with official practice tests and free resources like Khan Academy, use proven techniques like spaced repetition and the Feynman method, and remember — one good resource used properly beats five expensive ones you never open.Connect with Jordan on Twitter, Instagram, and YouTube. If you have something you'd like us to tackle here on Skeptical Sunday, drop Jordan a line at jordan@jordanharbinger.com and let him know!Connect with Jessica Wynn at Instagram and Threads, and subscribe to her newsletters: Between the Lines and Where the Shadows Linger!And if you're still game to support us, please leave a review here — even one sentence helps! Sign up for Six-Minute Networking — our free networking and relationship development mini course — at jordanharbinger.com/course!Subscribe to our once-a-week Wee Bit Wiser newsletter today and start filling your Wednesdays with wisdom!Do you even Reddit, bro? Join us at r/JordanHarbinger!This Episode Is Brought To You By Our Fine Sponsors: HexClad: 10% off: hexclad.com/jordanBombas: Go to bombas.com/jordan to get 20% off your first orderWayfair: Start renovating: wayfair.comHomes.com: Find your home: homes.comThe President's Daily Brief: Listen here or wherever you find fine podcasts!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Chris Holman welcomes back Tom Kelly, President & CEO, Automation Alley, Troy, MI. This time around is their Integr8 2026 discussion: Welcome back Tom, briefly remind the Michigan business community what Integr8 is and how it has evolved? From a business leader's perspective, what makes the 2026 Integr8 Series different from other manufacturing or tech events? How should manufacturers think about balancing technology investments with workforce readiness as AI and automation accelerate? Which of the 2026 roundtable topics do you think will have the most immediate impact on Michigan manufacturers' bottom lines? What practical takeaways can small and midsize manufacturers expect from the Integr8 playbooks and discussions? For companies considering sponsorship or participation, what's the real business value of being part of these roundtable conversations? » Visit MBN website: www.michiganbusinessnetwork.com/ » Subscribe to MBN's YouTube: www.youtube.com/@MichiganbusinessnetworkMBN » Like MBN: www.facebook.com/mibiznetwork » Follow MBN: twitter.com/MIBizNetwork/ » MBN Instagram: www.instagram.com/mibiznetwork/ Automation Alley announces 2026 Integr8 Roundtable Series focused on the technologies redefining manufacturing Six curated roundtables will explore AI, data, automation and global manufacturing trends – with workforce transformation integrated across all topics TROY, Mich., 2025 – Automation Alley, Michigan's Digital Transformation Insight Center, today announced its 2026 Integr8 Series featuring six thought-provoking roundtables focused on the technologies, strategies and global forces shaping the future of digital transformation. 2026 Integr8 Roundtable Series Each roundtable will generate a companion playbook summarizing key insights and strategies from the discussion. The 2026 series includes: February – Vibe Manufacturing: Where AI Meets Additive Exploring the convergence of artificial intelligence and additive manufacturing and how “Vibe Manufacturing” is shaping the next generation of production. March – Industrial Intelligence: Making Data Work on the Factory Floor Examining how manufacturers can move from data overload to actionable insight through smarter, connected systems. April – The Next Industrial Workforce: What Jobs Will the Future Demand? Addressing how AI and automation are transforming the nature of work and what it means for future talent pipelines. May – Trade, Tariffs & Tension: Manufacturing in a Fractured Global Economy Discussing how manufacturers can remain agile and competitive amid shifting trade dynamics and geopolitical uncertainty. August – Automation in the Real World: Aligning Supply Chain & Strategy Highlighting how companies are successfully integrating automation across complex multi-tiered supply chains. September – Digital Defense: Cybersecurity Strategies for Small and Midsize Manufacturers Focusing on practical, scalable approaches to protect connected factories from evolving cyber threats. Roundtable participation will be limited so that attendees are better able to participate in meaningful discussions. Manufacturers of all sizes as well as companies within the technology, government, academia and professional services industries are encouraged to request an invitation to be considered. Companies can find the full agenda with full descriptions of each roundtable and request an invitation at https://integr8series.com/. Sponsorship Opportunities Available The 2026 Integr8 Series offers a unique opportunity for companies to showcase their thought leadership and elevate their brand visibility among manufacturing and technology leaders. Exclusive sponsorship opportunities are available for each roundtable, providing sponsors with direct engagement with key decision-makers and positioning their organizations as leaders in digital transformation. Visit https://integr8series.com/sponsorship/ or contact Automation Alley at sponsorships@automationalley.com.

This week, we look at the link between food and cars. Did you know that Volkswagen makes sausages.And Rolls-Royce makes honey.Did you know cars have even been named after foods and beverages – like the Mitsubishi Pistachio and the Suzuki Cappuccino.It's true – and it's tasty marketing.We know you want to listen to all the ads in this show. On the off-chance you don't, subscribe ad-free here. Hosted on Acast. See acast.com/privacy for more information.

Trevor McGregor is a former Tony Robbins coach that has since gone his own way to propel the core of Tony's messaging, with his own unique spin on it. Get Trevor's Resoruces HERE:https://trevormcgregor.com/-----Hosted by Derek VidellLearn How to Run Profitable Facebook Ads Yourself: socialbamboo.com/30 (free call) social bamboo.com/5roas (free course) socialbamboo.com/dwy (paid program) I have DWY and DFY Meta Ads services available. Book a free call to start. Build a Perfectly Trained AI Chatbot: https://pro-bots.ai/trial (free course + 14 day software trial)Instagram | YouTube | SocialBamboo.com

The incredible demand for data centers is also creating competition above our atmosphere. Companies like Sidus Space are taking the lead in making that future possible. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Join Our Medical Device Sales Program: https://click.newtomedicaldevicesales.com/yt-440If you're new to my channel, my name is Jacob McLaughlin. I'm the founder of New to Medical Device Sales, an exclusive training program designed to help people break into the competitive field of medical device sales. Our average person lands a six-figure role in just 9.5 weeks, earning $113,760 annually. With thousands of success stories from candidates with all kinds of backgrounds, our program equips you with the tools to succeed in this industry.4 years ago I moved out to Arizona not knowing anyone and had $1200 to my name.I came to this exact spot to journal and share how excited I was to be starting my journey in life.Last night I took time to reflect over the past 4 years. It's truly amazing how you can change your life in such a small amount of time.My take aways:1. Go after your dream because even if it doesn't workout like you thought it would, it will bring your right where you're suppose to be.2. Believe in yourself. Nobody is going to believe in you as much as you will, know that good things will happen.3. Change is inevitable. Change is going to happen so you can either accept it and keep moving forward or not.Please bet on yourself and go after your dreams because your life can be better than you ever thought it could be if you do

Companies like Meta and other social platforms are facing serious scrutiny over the effects of social media on mental health, teens, misinformation, and society at large. Lawmakers are circling. Hearings are happening. Headlines are dramatic. But is this real regulatory momentum… or political theater? Joe and Robert debate: Whether meaningful regulation is actually possible What history tells us about tech antitrust moments And what marketers should prepare for if something does change Are we watching the beginning of a structural shift, or just another PR cycle? OpenAI Buys OpenClaw OpenAI makes another strategic move, acquiring OpenClaw. Smart vertical integration or signs of pressure? Joe and Robert explore: What this signals about OpenAI's long-term positioning If this move strengthens the moat or exposes vulnerability Desperate land grab or calculated chess move? Apple Moves into Video. Too Late? Apple continues expanding its footprint in video podcasts and entertainment. But in a world dominated by established streaming giants and creator-driven platforms, is Apple behind? The discussion covers: Apple's historical pattern of entering late and winning anyway Whether hardware advantage still matters If brand trust gives Apple an edge in a saturated market What this means for content creators and marketers Is Apple playing the long game… or missing the moment? Marketing Winners and Losers Winners Joe shares a win from Surfside and what "winning" looks like in Key West. Sometimes the lesson isn't scale. It's positioning, timing, and owning a moment. Losers Robert discusses the Ring backlash and how they just didn't read the room. Rants and Raves Robert's Rant The evolving role of the AI creator. Is the curator the new role? Joe's Rave Differentiation is not louder messaging. It's clearer identity. In a world drowning in synthetic sameness, the brands and creators who stand for something specific will win. As always, Joe and Robert cut through the noise so you can focus on what matters. Subscribe. Share. And don't miss this one. Subscribe and Follow: Follow Joe Pulizzi and Robert Rose on LinkedIn for insights, hot takes, and weekly updates from the world of content and marketing.  ------- This week's sponsor: Did you know that most businesses only use 20% of their data? That's like reading a book with most of the pages torn out. Point is, you miss a lot. Unless you use HubSpot. Their customer platform gives you access to the data you need to grow your business. The insights trapped in emails, call logs, and transcripts.  All that unstructured data that makes all the difference. Because when you know more, you grow more. Visit https://www.hubspot.com/ to hear how HubSpot can help you grow better. ------- Get all the show notes: https://www.thisoldmarketing.com/ Get Joe's new book, Burn the Playbook, at http://www.joepulizzi.com/books/burn-the-playbook/ Subscribe to Joe's Newsletter at https://www.joepulizzi.com/signup/. Get Robert Rose's new book, Valuable Friction, at https://robertrose.net/valuable-friction/  Subscribe to Robert's Newsletter at https://seventhbearlens.substack.com/ ------- This Old Marketing is part of the HubSpot Podcast Network: https://www.hubspot.com/podcastnetwork

The scary (Dystopia)Microsoft AI chief gives it 18 months—for all white-collar work to be automated by AIAI Will Destroy Millions of White Collars Jobs in the Coming Months, Andrew Yang Warns, Driving Surge of Personal BankruptciesRing cancels Flock deal after dystopian Super Bowl ad prompts mass outrageAmazon and Flock Safety have ended a partnership that would've given law enforcement access to a vast web of Ring cameras. The decision came after Amazon faced substantial backlash for airing a Super Bowl ad that was meant to be warm and fuzzy, but instead came across as disturbing and dystopian.Ring's Founder Knows You Hated That Super Bowl Ad. Since the commercial aired, Jamie Siminoff has been trying to quell an outcry over privacy concerns with his doorbell cameras.Platforms bend over backward to help DHS censor ICE critics, advocates say MMAnthropic is clashing with the Pentagon over AI useAnthropic's relationship with the Department of Defense is “under review” as the two sides negotiate over how the company's AI models can be used.The startup wants assurance that its models will not be used for autonomous weapons or mass surveillance.The DOD wants to use Anthropic's models “for all lawful use cases” without limitationDavid Sacks, the venture capitalist serving as the administration's AI and crypto czar, has accused Anthropic of supporting “woke AI” because of its stance on regulation.Our Big Data OverlordsMeta Begins $65 Million Election Push to Advance A.I. AgendaMark Zuckerberg faces jury in landmark trial over alleged youth harm linked to social mediaThe lawsuit, K.G.M. v. Meta Platforms, Inc., et al., was filed by a 20-year-old California woman identified by her initials. She alleges that Meta and other tech companies deliberately engineered their platforms to hook young users, contributing to her depression and suicidal thoughts, and seeks to hold them accountable.Regarding Instagram's enforcement efforts, plaintiffs asked whether Meta removed all 4 million under-13 users the company had identified on the platform in 2018. Zuckerberg responded that while the company did not remove all of them, it had implemented tools to detect and address underage accounts and was working to improve those systems.According to reports, Zuckerberg has not directly answered the central question of the case: whether Instagram is addictive. The plaintiff's attorney, Mark Lanier, asked if people tend to use something more if it's addictive. “I'm not sure what to say to that,” Zuckerberg said. “I don't think that applies here.”He said he believes in the “basic assumption” that “if something is valuable, people will use it more because it's useful to them.”When he was asked about his compensation, Zuckerberg said he has pledged to give “almost all” of his money to charity, focusing on scientific research. Lanier asked him how much money he has pledged to victims impacted by social media, to which Zuckerberg replied, “I disagree with the characterization of your question.”Zuckerberg's courthouse entourage showed up in Meta Ray-BansMeta Adding Facial Recognition to Its Smart Glasses That Identifies People in Real Time, Hoping the Public Is Too Distracted by Political Turmoil to Care MMApple sued by West Virginia for alleged failure to stop child sexual abuse material on iCloud, iOS devicesSpaceX said to weigh dual-class IPO shares to empower MuskMacron Blasts Social Media's Free Speech Defense as ‘Bullshit'The stupid (ESG edition)Goldman Sachs to Drop D.E.I. Criteria for Board Members MMThe move would be the Wall Street firm's latest retreat from diversity mandates that its chief executive, David Solomon, had once made a priority.The decision is a result of a deal that Goldman struck with the National Legal and Policy Center, a conservative nonprofit group that has been pressuring numerous companies to drop diversity, equity and inclusion mandates, the people said.As part of its agreement with Goldman, the National Legal and Policy Center, which has a small investment in the bank, withdrew a shareholder proposal demanding that diversity criteria for the board be dropped.In March 2019, Mr. Solomon, his top deputy John Waldron and the firm's chief financial officer at the time, Stephen M. Scherr, declared diversity and inclusion “a top priority.”“When we unite around a common goal, we make progress together,” the men wrote in an email to the staff. They said they would “improve each year” toward goals that included a new recruiting class comprising “50 percent women, 11 percent Black professionals and 14 percent Hispanic/Latino professionals in the Americas, and 9 percent Black professionals in the U.K.”The next year, Mr. Solomon said Goldman would no longer take a company public in the United States or Europe unless it had at least one “diverse” board member. By 2021, a company would need at least two diverse board members in order for Goldman to agree to work on its initial public offering.Inspire Investing CEO: Nike's DEI Is A Legal Liability, Shareholders Coming For AnswersNike's DEI fight is no longer just a social media "culture war" argument. The U.S. Equal Employment Opportunity Commission (EEOC) is investigating Nike over allegations the company's DEI practices discriminated against white employees and job applicants.Robert Netzly, CEO of Inspire Investing: "Discrimination, whether it's black people or white people, gay people or straight people, is discrimination."Robert Netzly is a globally recognized authority in the Biblically Responsible Investing (BRI) movement, author of the book "Biblically Responsible Investing: On Wall Street As It Is In Heaven." Robert holds a B.S. degree in Liberal Studies from an online university. This article was from OutKick, which aims to expose the destructive nature of "woke" activism and is the antidote to the mainstream sports media that often serves an elite, left-leaning minority instead of the American sports fan. OutKick is owned by Fox Sports' parent company Fox CorporationFederal agency sues Coca-Cola bottler over work event that excluded menA Coca-Cola distributor and bottler is being sued for alleged sexual discrimination over a corporate networking event that excluded men, announced the U.S. Equal Employment Opportunity Commission, which filed the lawsuitAccording to the EEOC's lawsuit, in September 2024, Bedford, N.H.-headquartered Coca-Cola Northeast held a two-day employer-sponsored trip and networking event at the Mohegan Sun Casino and Resort in Connecticut. Coca-Cola Northeast privately invited female employees and then excused the female employees who attended the event from their normal work duties on Sept. 10 and 11, 2024, and paid them their normal salary or wages without requiring them to use vacation or other paid time off. Coca-Cola Northeast did not invite any male employees to the event.Trump revokes landmark ruling that greenhouse gases endanger public healthUS President Donald Trump has reversed a key Obama-era scientific ruling that underpins all federal actions on curbing planet-warming gases.The so-called 2009 "endangerment finding" concluded that a range of greenhouse gases were a threat to public health. It's become the legal bedrock of federal efforts to rein in emissions, especially in vehicles.Bill Maher Eviscerates Donald Trump Over ‘Biggest Dick Move in American History'The boring (ESG edition)Starbucks' investor group urges shareholders to replace directors over labor rowStarbucks faced fresh pressure on Wednesday from a coalition of investors including public-sector pension funds that urged shareholders ‌to vote against the reelection of two directors, citing persistent failure ‌to manage labor relations.The move against Starbucks' lead independent director, Jorgen Vig Knudstorp, and Beth ​Ford, chair of the board's Nominating and Corporate Governance Committee, comes as the company is locked in a prolonged effort to reach a collective agreement with its unionized baristas.Companies are cycling through CEOs—and replacing them with first-timers MMSome 168 new CEOs were appointed in 2025, the highest total since 2010. The defining shift was who got the job. Among incoming CEOs, 84% were serving in their first enterprise CEO role, reversing a multi-year tilt toward leaders with prior public-company experience.As recently as 2024, more than one in five new CEOs had already led a public company. That share fell sharply in 2025. Of the 140 first-time CEOs appointed, 116 had no prior enterprise CEO experience. Two-thirds had never served on a public company board, meaning many are stepping into the role without prior exposure to shareholder oversight or public company governance.CEO hopefuls have a new rival for the top job: their own board directorsAppointing board directors as CEOs was once a “break glass in case of emergency” strategy reserved for scandal, illness, or sudden resignation. While it remains a minority path compared with traditional internal promotions, it is no longer an anomaly.New data from Spencer Stuart highlights the shift. Of the 168 new S&P 1500 chief executives appointed in 2025, the highest annual total since 2010, 19 were drawn from their own company boards, the most since 2020. Spencer Stuart classifies directors as outsiders because they lack day-to-day operating responsibility. Even so, more boards are turning to them.Wall Street banks are paying their CEOs like it's 2006 againMorgan Stanley CEO Ted Pick's pay rises 32% to $45mlnBank of America Lifts Moynihan's Pay 17% to $41 Million for 2025Barclays Ceo Pay Hike: Barclays lifts CEO Venkatakrishnan's pay to over £15 million as bonus pool risesCitigroup bumps CEO Jane Fraser's pay to record $59mBro Culture (The Epstein Edition)Thomas Pritzker, Named in Epstein Files, Retires as Hyatt Executive ChairmanTom Pritzker Retires as Executive Chairman of Hyatt After 22 Years of Service and Will Not Stand for Reelection to Board of DirectorsThe Board has appointed Mark S. Hoplamazian, Hyatt's President and Chief Executive Officer, to succeed Mr. Pritzker as Chairman of the Board“Tom's leadership has been instrumental in shaping Hyatt's strategy and long-term growth, and we thank him for his service and dedication to Hyatt,” said Richard Tuttle, Chair of the Board's Nominating and Corporate Governance Committee. “The Board has engaged in thoughtful succession planning, and we are confident that Mark's deep knowledge of Hyatt's business, strong relationships with owners and colleagues, and proven track record as CEO of nearly two decades positions him well to serve as Chairman and continue driving Hyatt's long-term success.”In a letter to the Hyatt Hotels' Board of Directors, Tom Pritzker wrote, “My job and responsibility is to provide good stewardship. That is important to me. Good stewardship includes ensuring a proper transition at Hyatt. Following discussions with my fellow Board members, I have decided, after serving as Executive Chairman since 2004, and with the company in a strong position, that now is the right time for me to retire from Hyatt. Good stewardship also means protecting Hyatt, particularly in the context of my association with Jeffrey Epstein and Ghislaine Maxwell, which I deeply regret. I exercised terrible judgment in maintaining contact with them, and there is no excuse for failing to distance myself sooner. I condemn the actions and the harm caused by Epstein and Maxwell, and I feel deep sorrow for the pain they inflicted on their victims.”Dubai's DP World replaces CEO after Epstein links emergeDubai's DP World announced Essa Kazim was the new chairman of its board of directors and Yuvraj Narayan was its new group chief executive officer, replacing Sultan Ahmed bin Sulayem.Sulayem had been the CEO of Dubai's largest port operator since 2016 and chairman since 2007.DOJ records showed years of exchanges with Epstein, but Sulayem has not been accused of any criminal wrongdoing.Casey Wasserman to sell talent agency following Jefferey Epstein controversyCasey Wasserman has confirmed that he has started the process of selling his talent agency after it was uncovered that he had ties with Jefferey Epstein. The announcement comes as artists began to leave the agency after it was uncovered that the Wasserman CEO had extensive ties with Jeffrey Epstein and had sent flirtatious emails to Ghislaine Maxwell. Despite denying that he had any personal or business ties with either, Wasserman sent an apology to the 4,000 employees who work at his sports marketing and talent agency, confirming that he would be stepping down from the company. He said: “I'm deeply sorry that my past personal mistakes have caused you so much discomfort […] It's not fair to you, and it's not fair to the clients and partners we represent so vigorously and care so deeply about.”Former Victoria's Secret CEO Les Wexner testifies in House Epstein investigationThe billionaire behind the retail empire that once blanketed shopping malls with names such as Victoria's Secret and Abercrombie & Fitch told members of Congress on Wednesday that he was “duped by a world-class con man” — close financial adviser Jeffrey Epstein. Les Wexner also denied knowing about the late sex offender's crimes or participating in Epstein's abuse of girls and young women.“I was naive, foolish, and gullible to put any trust in Jeffrey Epstein. He was a con man. And while I was conned, I have done nothing wrong and have nothing to hide.”Wexner described himself to the lawmakers as a philanthropist, community builder and grandfather who always strove “to live my life in an ethical manner in line with my moral compass,” according to the statement.Top Goldman Sachs lawyer Kathy Ruemmler to resign over Epstein linksThe latest Justice Department release revealed a trove of communication between the two, including about potential jobs, her romantic life and gifts Epstein had given her. (She called him “sweetie” and “Uncle Jeffrey.”)Goldman's CEO David Solomon says he 'reluctantly' let top lawyer Kathy Ruemmler go after Epstein fallout MMKing Charles' brother Andrew arrested on suspicion of misconductWhite House Shrugs Off Lutnick's Epstein TiesCommerce Secretary Howard Lutnick has acknowledged traveling to Jeffrey Epstein's island and meeting him on another occasion.Elon's bro quits Burning Man board amid outrage over Epstein connectionBlowhard IndexSalesforce cofounder 'not OK' with Benioff's ICE crack: 'Marc made a very bad joke.'The comments occurred during a keynote address at the company's annual internal "Company Kickoff" (CKO) event in Las Vegas, sparking a significant backlash from employees and leadership alike.During the keynote, Benioff reportedly asked employees who had traveled to the event from outside the United States to stand up for recognition. Once they were standing, he made a "joke" to the effect of: "Thank you! Just so the ICE agents [in the building] know [who you are]."He reportedly made a follow-up "callback" later in the presentation, suggesting that ICE agents were also monitoring those who hadn't yet used a specific new Slackbot tool.And another joke about ICE surveilling employee travel: when there are literally employees afraid to travel for work due to current situationSalesforce famously promotes a culture of "Ohana" (family) and equality.Parker Harris (Cofounder): In a follow-up meeting, Harris reportedly called the jokes a "violation of the Code of Conduct" and even noted they could be considered a "fireable offense" for a typical employee.Rob Seaman (Slack GM): The head of the Salesforce-owned platform Slack sent a memo to staff stating he "cannot defend or explain" the jokes and that they did not align with his values.Salesforce employees call on CEO Benioff to cancel ICE ‘opportunities'Elon Musk says Anthropic's philosopher has no stake in the future because she doesn't have kidsPalantir, Which Is Powering ICE, Says Immigration Crackdown May Hurt Hiring MMFrom 10-K filed 2 days ago: “if we are not able to recruit, hire, or retain the talent we need because of increased regulation of immigration or work visas … it could be more difficult to staff our personnel on customer engagements and could increase our costs … Additionally, laws and regulations, such as restrictive immigration laws, may limit our ability to recruit outside of the United States ... If we fail to attract new personnel or to retain our current personnel, our business and operations could be harmed.”

Tornado touches down in Bloomington. Hammer & Nigel's interview with Beech Grove Chief and FOP President. Trump championing his affordability agenda. Trump gives Iran a deadline. Hamas Debunks the ‘Genocide’ Narrative. US Women's Hockey gets the Gold over Canada. Mayor Brandon Johnson upset over Bears possible move to Indiana. Trump upset with Obama for admitting alien information. The IndyStar can sometimes be really gross. Today’s Popcorn Moment: Rep Danny Lopez on his vote to bring the Bears to Indiana. Today on the Marketplace: Collectable Star Trek glasses. Leave it to the IndyStar to make death of Carmel man by an illegal alien an anti-Trump screed. Indy stadium board distributes survey seeking feedback on Major League Soccer effort. Trump executive order threatens MAGA & MAHA movement. Teachers can indoctrinate children, but we're not allowed to talk to them about the dangers from illegal immigration? Disappointing 4th quarter gdp number.. Companies passing on the costs to the consumer. TV Theme Song: Film Friday - Romeo JulietSee omnystudio.com/listener for privacy information.

In episode #355, Ben breaks down the top invoicing solutions used by SaaS and AI companies based on his 7th Annual Tech Stack Survey. With 57 different invoicing solutions named in the survey, this category shows far more fragmentation than core accounting. The top five solutions account for 55% of reported usage, but there's still a long tail of specialized billing and revenue management platforms. Ben walks through the most widely used tools and explains how invoicing increasingly overlaps with revenue management, subscription billing, and payment processing. Resources Mentioned 7th Annual SaaS Tech Stack Survey: https://www.thesaascfo.com/surveys/finance-accounting-tech-stack-survey/ Metronome, sponsor of the invoicing category: https://metronome.com/ What You'll Learn The top invoicing and billing solutions used in software companies Why QuickBooks and Stripe remain dominant in early and growth-stage SaaS Which newer platforms are gaining traction How fragmented the invoicing and billing landscape has become Why It Matters Invoicing is a critical link between bookings, cash flow, revenue recognition, and ARR reporting Poor billing infrastructure can break your MRR schedules and retention calculations As pricing models evolve (subscription, usage, hybrid), your invoicing system must handle complexity Revenue management tools increasingly sit between CRM, payments, and your general ledger Clean invoicing data is essential for accurate financial modeling, KPI tracking, and due diligence

Jed Ellerbroek highlights Comfort Systems (FIX) earnings as a sign that AI spending remains “feverish.” His top picks include Amazon (AMZN), which he thinks will benefit from tariffs being struck down on the retail side while their tech side remains strong. HALO, or heavy assets low obsolescence companies, are what investors are looking for in a jittery market. Jed explains how he's sorting winners from losers in the software sector. ======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

LEGO is all about celebration. LEGO House shows it's love for its birthplace, Bricklink Designs go on pre-order, and Snoopy makes a sleepy cute entrance! All that and more (as always) on this week's Bricking LEGO News!FOLLOW my YouTube channel: Back 2 BrickSet Review: 75423 Smart Play Luke's Red Five X-WingRebrickable Review: Saturn V Gundam alt Build by (void*)main()I Heart BillundMclaren MCL39F! helmetsMandalorian and Grogu setsSnoopy's DoghousePokemon is lostNew York Toys FairSandcrawler rumorsCrocs sold outAnimal Crossing polybagHydro electric education3-in-1 DUPLO fun!Bricklink Designer Program Series 7 Pre-ordersBricklink Designer Program Series 10 votingJedi Bob tells allNo gold trophyfree lightsaber!more stores in IndiaThank you, Patrons! - Bellefonte Bricks Studio, Jimmy Tucker, David, Paul Snellen, Lee Jackson, Pop's Block Shop, Steve Miles, David Support the showSee some of the designs I've built - REBRICKABLE.COMHead over to Back2brick.com for links to the latest LEGO set discounts!Support the podcast through our affiliate links AND join the Back 2 Brick Patreon!Have a question? Want to be a guest? Send me a message!backtobrick@gmail.comBack 2 Brick Podcast is not an affiliate nor endorsed by the LEGO Group.LEGO, the LEGO logo, the Minifigure, and the Brick and Knob configurations are trademarks of the LEGO Group of Companies. ©2025 The LEGO Group.

This episode is brought to you by B2B Better. We turned down SaaS clients and e-commerce brands to become the only video-first podcast agency for service-based B2B businesses. Specificity is the strategy.  If you've ever said "we can do that" to every client who walks in, this episode is your wake-up call. Host Jason Bradwell breaks down why niching down is the fastest path to becoming the obvious choice, and why being a bit of everything for everyone means you're actually nothing for nobody. Jason's core point is clear: when he set out to build B2B Better, he committed to being specific on two levels - who they serve and what they do. Not just B2B, because B2B is a hemisphere. They went one layer deeper: service-based businesses. Consulting firms, agencies, system integrators, compliance specialists. Companies that sell expertise, not products. People, not software. Trust and relationships, not features and pricing. That's what lends itself to their service: video-first podcasts that turn your point of view into pipeline. Nothing else. The same principle applies to podcasts. When a client says they want to launch a show, Jason's first question is: what's your superpower? Here's the formula. "This is a podcast about X, and unlike other podcasts about X, only we do Y." Most B2B podcasts fail this test. They say "we're a podcast about technology" or "leadership" or "AI." So are thousands of others. There's no "and." There's no reason to choose you. Add the "and" and everything changes. One show in the B2B Better portfolio is Data and Biotech: "a podcast about data science, and unlike other data science podcasts, only we explore it through the lens of biotech manufacturing." Suddenly if you're a data scientist in biotech, there's only one show for you. That specificity drives 75% to 80% episode completion rates, nearly double the industry average because every listener is exactly the right person. The fear of niching down is real. Every founder worries about leaving money on the table. But saying yes to everyone dilutes your positioning, creates operational inefficiency, and kills pricing power. What actually happens when you niche properly: the funnel gets narrower at first, but the people who raise their hand are perfect fits. They convert faster, pay more, stay longer, and refer others in the same niche. Year one it feels limiting. Year three it feels like leverage. Year five it feels like a moat. The framework to choose your niche: look at your best clients, not biggest. Validate the economics. Test your thesis before announcing publicly. Then commit hard and communicate clearly—change the website, the LinkedIn, the pitch deck. Say who you serve and who you don't. Resonance over reach. Always. Chapter Markers 00:00 - The "we can do anything" agency problem 01:00 - Why B2B isn't a niche, it's a hemisphere 02:00 - Choosing service-based businesses as the core niche 03:00 - Selling expertise, not products: why podcasts fit perfectly 04:00 - Video-first podcasts and the full service offering 05:00 - The superpower formula for podcast positioning 06:00 - Data and Biotech: the power of the "and" 07:00 - 75 to 80% completion rates and what resonance looks like 08:00 - Deeply engaged beats loosely interested every time 09:00 - Addressing the fear of leaving money on the table 10:00 - How niching compounds: pricing, referrals, close rates 11:00 - Four-step framework to choose your niche 12:00 - Specialists compound, generalists reset to zero 13:00 - Resonance is a revenue metric, reach is vanity 14:00 - Direct, systematic, results-driven: the B2B Better approach 15:00 - Write your "and" statement this week Useful Links Connect with Jason Bradwell on LinkedIn Listen to Pipe Dream on Podbean Explore B2B Better website and the Pipe Dream podcast 

It's Thursday, and that means it's time to catch up on politics with The Times-Picayune/New Orleans Advocate's editorial director and columnist, Stephanie Grace. Today, she breaks down some of the satirical floats during Mardi Gras season, which krewes criticized the presidential administration, and which krewes defended it.Last year, some Louisiana residents experienced a scheduled blackout, and many of them blamed — perhaps unfairly — the Midcontinent Independent System Operator (MISO).  Later this year, MISO will conduct what's called a load pocket risk assessment. It's a look at Louisiana's electrical grid, its capabilities and inadequacies. Madelyn Smith, Louisiana program manager for the Southeastern Wind Coalition, a major promoter of MISO membership, explains what this means. Major Louisiana industries are placing multibillion-dollar bets on blue ammonia — a product made from fossil fuels and extra technology in order to capture planet-warming gases and store them underground. But despite promising major emissions cuts, a recent investigation by Floodlight found that similar carbon capture projects can still cause pollution and environmental damage. Ames Alexander, investigative reporter for Floodlight News, tells us more. —Today's episode of Louisiana Considered was hosted by Bob Pavlovich. Our managing producer is Alana Schreiber. We get production support from Garrett Pittman and our assistant producer, Aubry Procell.You can listen to Louisiana Considered Monday through Friday at noon and 7 p.m. It's available on Spotify, the NPR App and wherever you get your podcasts. Louisiana Considered wants to hear from you!  Please fill out our pitch line to let us know what kinds of story ideas you have for our show. And while you're at it, fill out our listener survey! We want to keep bringing you the kinds of conversations you'd like to listen to.Louisiana Considered is made possible with support from our listeners. Thank you!

Amy Jo Martin built one of the first social media agencies because Shaq told her to. True story. Seven years later, she shut it down. Not because it failed, but because it worked in a way that locked her into a life she didn't want. Walking away gave her the freedom to decide what to build next. Since then, she's scaled multiple 8-figure companies, written bestselling books, and hosts the Why Not Now? podcast where she's interviewed countless celebrities.  This conversation is packed with value for entrepreneurs building at every stage. We also go deep on what building a social media agency in 2009 can teach us about AI today — and what that means if you're building anything right now.   Key Takeaways with Amy Jo Martin (00:00) Intro (01:25) Social Media in 2009 vs AI Today (04:18) The Only Metric That Actually Matters (06:38) Shaq Told Me to Quit My Job (11:39) Is AI a Trampoline or a Trap? (15:32) Why the Agency Model Keeps Breaking (19:18) Can AI Improve Your Relationships? (23:34) The LinkedIn Hack That Replaces Hours of Biz Dev (28:03) This Kills The Traditional Brainstorm Meeting (31:20) Taking Tony Hsieh's Money (34:44) Why She Shut Down a Profitable Company (38:34) When Personal Brand Becomes a Liability (43:24) Why AI Won't Save Bad Marketing (45:52) The Real AI Problem Is Organizational Culture (51:06) The Renegade Reinvention Experiment (57:41) Can AI Help You Feel More Alive? (01:06:25) Action Creates Clarity (01:07:49) Don't Raise Money Too Early     Watch on YouTube: https://youtu.be/kdos8mOBLgk      Let's Connect: Website | Instagram | YouTube | TikTok | Twitter | Facebook

Most companies don't realize it yet, but the way they built their technology foundations is quietly becoming a liability.Cloud costs are rising. Platforms change underneath you. AI is reshaping infrastructure from hardware to data to governance. And the strategies that once felt “safe” are now the ones creating the most risk.In this episode of IT Visionaries, host Chris Brandt sits down with Mano Bhattacharya, CTO of Nutanix, to unpack what's really happening inside enterprise technology right now. This isn't a conversation about chasing the newest tools or betting on a single future. It's about why adaptability has become the most important design principle in modern tech.Mano explains why many organizations are rethinking long-held assumptions about virtualization, cloud, and containers, and why the smartest teams are building infrastructure that gives them options over the next three to five years. They explore how AI changes the entire stack, not just applications, why data has become the real bottleneck, and why moving fast without a coherent plan can be more dangerous than moving slowly. Chapters:00:00 - The VMware Exodus Wave is Coming03:34 - VMware Broadcom Acquisition: What Changed and Why It Matters05:56 - Three Migration Paths: Stay, Move to Cloud, or Modernize09:59 - Why Containers on VMs Make Sense for Most Enterprises15:40 - The Five Stages of VMware Migration Grief21:20 - VMware Admin to Nutanix Admin: Closing the Skills Gap24:14 - The Cloud-in-a-Box Philosophy: From Boxes to Software32:30 - Opening Up the Platform: Pure Storage and Third-Party Integrations40:54 - AI Infrastructure: The End-to-End Challenge48:01 - Enterprise AI Strategy: Use Cases, Economics, and Governance56:44 - What's Next: Building the Invisible Platform for AI  -- This episode of IT Visionaries is brought to you by Meter - the company building better networks. Businesses today are frustrated with outdated providers, rigid pricing, and fragmented tools. Meter changes that with a single integrated solution that covers everything wired, wireless, and even cellular networking. They design the hardware, write the firmware, build the software, and manage it all so your team doesn't have to.That means you get fast, secure, and scalable connectivity without the complexity of juggling multiple providers. Thanks to meter for sponsoring. Go to meter.com/itv to book a demo.---IT Visionaries is made by the team at Mission.org. Learn more about our media studio and network of podcasts at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of No Priors, Sarah and Elad dive into the evolving landscape of software, exploring how AI is transforming the traditional SaaS model. They discuss whether SaaS as we know it is coming to an end, what new business and sales strategies are emerging, and how AI is reshaping the way software is built, sold, and scaled. The conversation also examines whether or not these shifts are a good thing for both big and small companies, and how coders and software experts are reacting to abrupt AI transitions. They also dig into how AI is reshaping sales, automating workflows, and enabling more predictive customer strategies. Beyond individual companies, they examine how tech giants are increasingly dominating the S&P 500, and what this concentration of power means for the future of startups, innovation, and the broader entrepreneurial ecosystem. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil |  Chapters: 00:00 – Cold Open 00:35 – The SaaS-polcalypse discussion  4:55 – AI Change Management in Large vs. Small Companies 05:43 – “Is Software Eating the World?”  08:38 – Addressing the Unsolved Problems  14:00 – The Noise of the Last Month vs. Excitement  21:32  – What Proportion of GDP is Tech?  23:20 – Market Cap Shifts 25:02 – As a Company, When Should You Sell?  29:05 – Multi-Product Bundle Defense  30:45 – Conclusion

In this episode of Building Billions, I break down the exact system I used to go from washing dishes in Corvallis, Oregon, to building three companies worth over $100 million each. I reveal the three-part strategy that anyone can apply: model, mimic, and master. You’ll learn why choosing the right example to follow is critical, how to duplicate proven systems, and why mastering those systems through others is the ultimate wealth multiplier. I also dive into how to turn your own skills into a scalable process by teaching others to replicate your success. This is the framework that allowed me to lead massive teams, grow companies to billion-dollar valuations, and build real wealth. If you want to shortcut trial and error, multiply your impact, and create a system that generates results long after you step back, this episode is your blueprint.Support the show: http://cardoneventures.comSee omnystudio.com/listener for privacy information.

On today's Consumer Finance Monitor podcast, we are releasing an episode about a timely and wide-ranging discussion on one of the most significant and fastest-evolving developments in commercial finance: the rapid "consumerization" of small business lending law. In this episode, host Alan Kaplinsky welcomes Louis Caditz-Peck, Executive Director of the Responsible Business Lending Coalition (RBLC), for an in-depth conversation about the proliferation of state small business lending protection statutes, the policy debates driving them, and what they mean for lenders, fintechs, banks, and small business borrowers. From Self-Regulation to State Law: How We Got Here For decades, commercial lending operated under a fundamentally different regulatory framework than consumer credit. The prevailing assumption was that business borrowers were sophisticated, negotiated their transactions, and did not need standardized disclosures or suitability-type protections. That assumption has eroded. As Louis explains, since the financial crisis, and particularly with the growth of online and fintech lending, small business financing has changed dramatically. Community banks have pulled back. Non-bank online platforms have expanded. New products, including merchant cash advances and other revenue-based financing arrangements, have proliferated. At the same time, concerns have grown about: Opaque pricing structures Misleading "interest rate" representations Broker incentives that steer borrowers into higher-cost products Repeated refinancing of unaffordable obligations These concerns led to the development of the Small Business Borrower's Bill of Rights, a set of industry standards first launched in 2015 at the Aspen Institute by a coalition of lenders, small business groups, and nonprofit advocates. What began as a voluntary, self-regulatory effort quickly became a blueprint for legislation. California's SB 1235 in 2018 marked the first major small business truth-in-lending law. Since then, according to Louis, 19 small business financial protection laws have been enacted across multiple states, with California and New York leading the way. The "Consumerization" of Small Business Lending A central theme of the episode is whether we are witnessing the "consumerization" of small business lending. Many of the new state laws borrow heavily from consumer credit concepts, including: APR-style cost disclosures Total cost of financing disclosures Payment schedule requirements Prepayment and fee transparency Restrictions on certain contractual provisions Some states have layered on licensing or registration requirements for small business finance providers. Others incorporate or supplement state UDAP (unfair and deceptive acts and practices) standards, which may apply to certain business-to-business transactions as well as consumer transactions. The policy rationale is straightforward: many "Main Street" businesses are effectively sole proprietorships or closely-held operations without in-house finance or legal teams. Legislators increasingly view these borrowers as closer to consumers than to large corporations with treasury departments and inside or outside counsel. As Alan and Louis discuss, the regulatory shift raises serious operational and compliance challenges, particularly given the state-by-state patchwork of requirements. The Compliance Conundrum: Patchwork and Harmonization A recurring concern is whether the proliferation of state laws imposes disproportionate burdens on smaller lenders and startups, especially compared to large institutions with robust legal and compliance infrastructures. Louis emphasizes that RBLC has actively worked to promote interstate harmonization, particularly between California and New York. For example: Advocating for standardized disclosure forms that can be used in multiple states Aligning definitions and disclosure triggers Encouraging estimated APR calculations for revenue-based financing However, not all states have followed a harmonized approach. Some laws, particularly those focused narrowly on merchant cash advances, have created divergent requirements, complicating multi-state compliance. As Alan notes, the trend presents both risk and opportunity for lenders and their counsel. The regulatory environment is no longer static. Companies offering small business financing must assume that: Cost disclosures will likely be required in more states Registration or licensing may apply Enforcement risk—particularly under state UDAP statutes—will increase Section 1071 and Federal Uncertainty The episode also explores the role of the CFPB under Section 1071 of the Dodd-Frank Act, which requires data collection on small business lending to: 1.     Identify potential discrimination, and 2.     Assess whether certain markets are underserved. The CFPB finalized its 1071 rule in 2023 under then Director Rohit Chopra. Multiple legal challenges followed. Under the current administration, a notice of proposed rulemaking has sought to scale back and slow implementation. At the same time, the Federal Trade Commission has signaled an interest in using its enforcement authority to address unfair or deceptive acts or practices affecting small businesses—underscoring an intriguing tension within federal regulatory policy. As Louis observes, the debate is not simply about reducing or expanding government. It is about how government authority will be used and whether transparency and enforcement will be advanced through rulemaking, litigation, or state initiatives. Merchant Cash Advances and Revenue-Based Financing A particularly nuanced part of the discussion focuses on merchant cash advances (MCAs) and other sales-based financing products. These arrangements typically involve: An advance of funds in exchange for a fixed repayment amount Payments tied to a percentage of daily or periodic sales Variable duration depending on business performance RBLC's position, as Louis explains, is product neutral. The coalition does not advocate banning product categories or imposing rate caps. Instead, it focuses on responsible practices, including transparent pricing and assessment of ability to repay. Importantly, none of the major state lending protection laws impose interest rate caps. The emphasis is on disclosure and market transparency rather than price regulation. Who Is Covered—and Who Is Not? Most state small business truth-in-lending statutes apply to financing of $500,000 or less (with some variation, such as New York's $2.5 million threshold following gubernatorial revision). Coverage often includes: Closed-end loans Open-end lines of credit Sales-based financing/MCAs Factoring (in some states) Banks are generally exempt from these statutes, though non-bank "providers" presenting the offer of credit may still have disclosure obligations even in bank partnership models. As Alan highlights, this raises interesting competitive and policy questions about level playing fields across banks and non-banks. Looking Ahead to 2026 Both speakers agree: this trend is not going away. With significant percentages of small business owners reporting difficulty accessing affordable capital—and a substantial minority reporting harm from predatory practices—state legislators remain motivated to act. The key policy question is not whether regulation will expand, but how. Well-designed transparency frameworks can: Promote price competition Reward responsible innovation Improve borrower decision-making Poorly harmonized or overly rigid frameworks, however, risk increasing compliance costs and reducing credit availability. As Alan notes in his closing remarks, small business finance regulation is becoming a core area of growth for law firms and compliance professionals historically focused on consumer financial services. The line between consumer and commercial finance continues to blur.  Alan noted that the Consumer Financial Services Group which he founded and chaired for 25 years has counseled and represented small business lenders for decades. For lenders, fintechs, banks, and their advisors, understanding these developments is no longer optional—it is essential. Consumer Finance Monitor is hosted by Alan Kaplinsky, Senior Counsel at Ballard Spahr, and the founder and former chair of the firm's Consumer Financial Services Group. We encourage listeners to subscribe to the podcast on their preferred platform for weekly insights into developments in the consumer finance industry.

Tickets for AIEi Miami and AIE Europe are live, with first wave speakers announced!From pioneering software-defined networking to backing many of the most aggressive AI model companies of this cycle, Martin Casado and Sarah Wang sit at the center of the capital, compute, and talent arms race reshaping the tech industry. As partners at a16z investing across infrastructure and growth, they've watched venture and growth blur, model labs turn dollars into capability at unprecedented speed, and startups raise nine-figure rounds before monetization.Martin and Sarah join us to unpack the new financing playbook for AI: why today's rounds are really compute contracts in disguise, how the “raise → train → ship → raise bigger” flywheel works, and whether foundation model companies can outspend the entire app ecosystem built on top of them. They also share what's underhyped (boring enterprise software), what's overheated (talent wars and compensation spirals), and the two radically different futures they see for AI's market structure.We discuss:* Martin's “two futures” fork: infinite fragmentation and new software categories vs. a small oligopoly of general models that consume everything above them* The capital flywheel: how model labs translate funding directly into capability gains, then into revenue growth measured in weeks, not years* Why venture and growth have merged: $100M–$1B hybrid rounds, strategic investors, compute negotiations, and complex deal structures* The AGI vs. product tension: allocating scarce GPUs between long-term research and near-term revenue flywheels* Whether frontier labs can out-raise and outspend the entire app ecosystem built on top of their APIs* Why today's talent wars ($10M+ comp packages, $B acqui-hires) are breaking early-stage founder math* Cursor as a case study: building up from the app layer while training down into your own models* Why “boring” enterprise software may be the most underinvested opportunity in the AI mania* Hardware and robotics: why the ChatGPT moment hasn't yet arrived for robots and what would need to change* World Labs and generative 3D: bringing the marginal cost of 3D scene creation down by orders of magnitude* Why public AI discourse is often wildly disconnected from boardroom reality and how founders should navigate the noiseShow Notes:* “Where Value Will Accrue in AI: Martin Casado & Sarah Wang” - a16z show* “Jack Altman & Martin Casado on the Future of Venture Capital”* World Labs—Martin Casado• LinkedIn: https://www.linkedin.com/in/martincasado/• X: https://x.com/martin_casadoSarah Wang• LinkedIn: https://www.linkedin.com/in/sarah-wang-59b96a7• X: https://x.com/sarahdingwanga16z• https://a16z.com/Timestamps00:00:00 – Intro: Live from a16z00:01:20 – The New AI Funding Model: Venture + Growth Collide00:03:19 – Circular Funding, Demand & “No Dark GPUs”00:05:24 – Infrastructure vs Apps: The Lines Blur00:06:24 – The Capital Flywheel: Raise → Train → Ship → Raise Bigger00:09:39 – Can Frontier Labs Outspend the Entire App Ecosystem?00:11:24 – Character AI & The AGI vs Product Dilemma00:14:39 – Talent Wars, $10M Engineers & Founder Anxiety00:17:33 – What's Underinvested? The Case for “Boring” Software00:19:29 – Robotics, Hardware & Why It's Hard to Win00:22:42 – Custom ASICs & The $1B Training Run Economics00:24:23 – American Dynamism, Geography & AI Power Centers00:26:48 – How AI Is Changing the Investor Workflow (Claude Cowork)00:29:12 – Two Futures of AI: Infinite Expansion or Oligopoly?00:32:48 – If You Can Raise More Than Your Ecosystem, You Win00:34:27 – Are All Tasks AGI-Complete? Coding as the Test Case00:38:55 – Cursor & The Power of the App Layer00:44:05 – World Labs, Spatial Intelligence & 3D Foundation Models00:47:20 – Thinking Machines, Founder Drama & Media Narratives00:52:30 – Where Long-Term Power Accrues in the AI StackTranscriptLatent.Space - Inside AI's $10B+ Capital Flywheel — Martin Casado & Sarah Wang of a16z[00:00:00] Welcome to Latent Space (Live from a16z) + Meet the Guests[00:00:00] Alessio: Hey everyone. Welcome to the Latent Space podcast, live from a 16 z. Uh, this is Alessio founder Kernel Lance, and I'm joined by Twix, editor of Latent Space.[00:00:08] swyx: Hey, hey, hey. Uh, and we're so glad to be on with you guys. Also a top AI podcast, uh, Martin Cado and Sarah Wang. Welcome, very[00:00:16] Martin Casado: happy to be here and welcome.[00:00:17] swyx: Yes, uh, we love this office. We love what you've done with the place. Uh, the new logo is everywhere now. It's, it's still getting, takes a while to get used to, but it reminds me of like sort of a callback to a more ambitious age, which I think is kind of[00:00:31] Martin Casado: definitely makes a statement.[00:00:33] swyx: Yeah.[00:00:34] Martin Casado: Not quite sure what that statement is, but it makes a statement.[00:00:37] swyx: Uh, Martin, I go back with you to Netlify.[00:00:40] Martin Casado: Yep.[00:00:40] swyx: Uh, and, uh, you know, you create a software defined networking and all, all that stuff people can read up on your background. Yep. Sarah, I'm newer to you. Uh, you, you sort of started working together on AI infrastructure stuff.[00:00:51] Sarah Wang: That's right. Yeah. Seven, seven years ago now.[00:00:53] Martin Casado: Best growth investor in the entire industry.[00:00:55] swyx: Oh, say[00:00:56] Martin Casado: more hands down there is, there is. [00:01:00] I mean, when it comes to AI companies, Sarah, I think has done the most kind of aggressive, um, investment thesis around AI models, right? So, worked for Nom Ja, Mira Ia, FEI Fey, and so just these frontier, kind of like large AI models.[00:01:15] I think, you know, Sarah's been the, the broadest investor. Is that fair?[00:01:20] Venture vs. Growth in the Frontier Model Era[00:01:20] Sarah Wang: No, I, well, I was gonna say, I think it's been a really interesting tag, tag team actually just ‘cause the, a lot of these big C deals, not only are they raising a lot of money, um, it's still a tech founder bet, which obviously is inherently early stage.[00:01:33] But the resources,[00:01:36] Martin Casado: so many, I[00:01:36] Sarah Wang: was gonna say the resources one, they just grow really quickly. But then two, the resources that they need day one are kind of growth scale. So I, the hybrid tag team that we have is. Quite effective, I think,[00:01:46] Martin Casado: what is growth these days? You know, you don't wake up if it's less than a billion or like, it's, it's actually, it's actually very like, like no, it's a very interesting time in investing because like, you know, take like the character around, right?[00:01:59] These tend to [00:02:00] be like pre monetization, but the dollars are large enough that you need to have a larger fund and the analysis. You know, because you've got lots of users. ‘cause this stuff has such high demand requires, you know, more of a number sophistication. And so most of these deals, whether it's US or other firms on these large model companies, are like this hybrid between venture growth.[00:02:18] Sarah Wang: Yeah. Total. And I think, you know, stuff like BD for example, you wouldn't usually need BD when you were seed stage trying to get market biz Devrel. Biz Devrel, exactly. Okay. But like now, sorry, I'm,[00:02:27] swyx: I'm not familiar. What, what, what does biz Devrel mean for a venture fund? Because I know what biz Devrel means for a company.[00:02:31] Sarah Wang: Yeah.[00:02:32] Compute Deals, Strategics, and the ‘Circular Funding' Question[00:02:32] Sarah Wang: You know, so a, a good example is, I mean, we talk about buying compute, but there's a huge negotiation involved there in terms of, okay, do you get equity for the compute? What, what sort of partner are you looking at? Is there a go-to market arm to that? Um, and these are just things on this scale, hundreds of millions, you know, maybe.[00:02:50] Six months into the inception of a company, you just wouldn't have to negotiate these deals before.[00:02:54] Martin Casado: Yeah. These large rounds are very complex now. Like in the past, if you did a series A [00:03:00] or a series B, like whatever, you're writing a 20 to a $60 million check and you call it a day. Now you normally have financial investors and strategic investors, and then the strategic portion always still goes with like these kind of large compute contracts, which can take months to do.[00:03:13] And so it's, it's very different ties. I've been doing this for 10 years. It's the, I've never seen anything like this.[00:03:19] swyx: Yeah. Do you have worries about the circular funding from so disease strategics?[00:03:24] Martin Casado: I mean, listen, as long as the demand is there, like the demand is there. Like the problem with the internet is the demand wasn't there.[00:03:29] swyx: Exactly. All right. This, this is like the, the whole pyramid scheme bubble thing, where like, as long as you mark to market on like the notional value of like, these deals, fine, but like once it starts to chip away, it really Well[00:03:41] Martin Casado: no, like as, as, as, as long as there's demand. I mean, you know, this, this is like a lot of these sound bites have already become kind of cliches, but they're worth saying it.[00:03:47] Right? Like during the internet days, like we were. Um, raising money to put fiber in the ground that wasn't used. And that's a problem, right? Because now you actually have a supply overhang.[00:03:58] swyx: Mm-hmm.[00:03:59] Martin Casado: And even in the, [00:04:00] the time of the, the internet, like the supply and, and bandwidth overhang, even as massive as it was in, as massive as the crash was only lasted about four years.[00:04:09] But we don't have a supply overhang. Like there's no dark GPUs, right? I mean, and so, you know, circular or not, I mean, you know, if, if someone invests in a company that, um. You know, they'll actually use the GPUs. And on the other side of it is the, is the ask for customer. So I I, I think it's a different time.[00:04:25] Sarah Wang: I think the other piece, maybe just to add onto this, and I'm gonna quote Martine in front of him, but this is probably also a unique time in that. For the first time, you can actually trace dollars to outcomes. Yeah, right. Provided that scaling laws are, are holding, um, and capabilities are actually moving forward.[00:04:40] Because if you can put translate dollars into capabilities, uh, a capability improvement, there's demand there to martine's point. But if that somehow breaks, you know, obviously that's an important assumption in this whole thing to make it work. But you know, instead of investing dollars into sales and marketing, you're, you're investing into r and d to get to the capability, um, you know, increase.[00:04:59] And [00:05:00] that's sort of been the demand driver because. Once there's an unlock there, people are willing to pay for it.[00:05:05] Alessio: Yeah.[00:05:06] Blurring Lines: Models as Infra + Apps, and the New Fundraising Flywheel[00:05:06] Alessio: Is there any difference in how you built the portfolio now that some of your growth companies are, like the infrastructure of the early stage companies, like, you know, OpenAI is now the same size as some of the cloud providers were early on.[00:05:16] Like what does that look like? Like how much information can you feed off each other between the, the two?[00:05:24] Martin Casado: There's so many lines that are being crossed right now, or blurred. Right. So we already talked about venture and growth. Another one that's being blurred is between infrastructure and apps, right? So like what is a model company?[00:05:35] Mm-hmm. Like, it's clearly infrastructure, right? Because it's like, you know, it's doing kind of core r and d. It's a horizontal platform, but it's also an app because it's um, uh, touches the users directly. And then of course. You know, the, the, the growth of these is just so high. And so I actually think you're just starting to see a, a, a new financing strategy emerge and, you know, we've had to adapt as a result of that.[00:05:59] And [00:06:00] so there's been a lot of changes. Um, you're right that these companies become platform companies very quickly. You've got ecosystem build out. So none of this is necessarily new, but the timescales of which it's happened is pretty phenomenal. And the way we'd normally cut lines before is blurred a little bit, but.[00:06:16] But that, that, that said, I mean, a lot of it also just does feel like things that we've seen in the past, like cloud build out the internet build out as well.[00:06:24] Sarah Wang: Yeah. Um, yeah, I think it's interesting, uh, I don't know if you guys would agree with this, but it feels like the emerging strategy is, and this builds off of your other question, um.[00:06:33] You raise money for compute, you pour that or you, you pour the money into compute, you get some sort of breakthrough. You funnel the breakthrough into your vertically integrated application. That could be chat GBT, that could be cloud code, you know, whatever it is. You massively gain share and get users.[00:06:49] Maybe you're even subsidizing at that point. Um, depending on your strategy. You raise money at the peak momentum and then you repeat, rinse and repeat. Um, and so. And that wasn't [00:07:00] true even two years ago, I think. Mm-hmm. And so it's sort of to your, just tying it to fundraising strategy, right? There's a, and hiring strategy.[00:07:07] All of these are tied, I think the lines are blurring even more today where everyone is, and they, but of course these companies all have API businesses and so they're these, these frenemy lines that are getting blurred in that a lot of, I mean, they have billions of dollars of API revenue, right? And so there are customers there.[00:07:23] But they're competing on the app layer.[00:07:24] Martin Casado: Yeah. So this is a really, really important point. So I, I would say for sure, venture and growth, that line is blurry app and infrastructure. That line is blurry. Um, but I don't think that that changes our practice so much. But like where the very open questions are like, does this layer in the same way.[00:07:43] Compute traditionally has like during the cloud is like, you know, like whatever, somebody wins one layer, but then another whole set of companies wins another layer. But that might not, might not be the case here. It may be the case that you actually can't verticalize on the token string. Like you can't build an app like it, it necessarily goes down just because there are no [00:08:00] abstractions.[00:08:00] So those are kinda the bigger existential questions we ask. Another thing that is very different this time than in the history of computer sciences is. In the past, if you raised money, then you basically had to wait for engineering to catch up. Which famously doesn't scale like the mythical mammoth. It take a very long time.[00:08:18] But like that's not the case here. Like a model company can raise money and drop a model in a, in a year, and it's better, right? And, and it does it with a team of 20 people or 10 people. So this type of like money entering a company and then producing something that has demand and growth right away and using that to raise more money is a very different capital flywheel than we've ever seen before.[00:08:39] And I think everybody's trying to understand what the consequences are. So I think it's less about like. Big companies and growth and this, and more about these more systemic questions that we actually don't have answers to.[00:08:49] Alessio: Yeah, like at Kernel Labs, one of our ideas is like if you had unlimited money to spend productively to turn tokens into products, like the whole early stage [00:09:00] market is very different because today you're investing X amount of capital to win a deal because of price structure and whatnot, and you're kind of pot committing.[00:09:07] Yeah. To a certain strategy for a certain amount of time. Yeah. But if you could like iteratively spin out companies and products and just throw, I, I wanna spend a million dollar of inference today and get a product out tomorrow.[00:09:18] swyx: Yeah.[00:09:19] Alessio: Like, we should get to the point where like the friction of like token to product is so low that you can do this and then you can change the Right, the early stage venture model to be much more iterative.[00:09:30] And then every round is like either 100 k of inference or like a hundred million from a 16 Z. There's no, there's no like $8 million C round anymore. Right.[00:09:38] When Frontier Labs Outspend the Entire App Ecosystem[00:09:38] Martin Casado: But, but, but, but there's a, there's a, the, an industry structural question that we don't know the answer to, which involves the frontier models, which is, let's take.[00:09:48] Anthropic it. Let's say Anthropic has a state-of-the-art model that has some large percentage of market share. And let's say that, uh, uh, uh, you know, uh, a company's building smaller models [00:10:00] that, you know, use the bigger model in the background, open 4.5, but they add value on top of that. Now, if Anthropic can raise three times more.[00:10:10] Every subsequent round, they probably can raise more money than the entire app ecosystem that's built on top of it. And if that's the case, they can expand beyond everything built on top of it. It's like imagine like a star that's just kind of expanding, so there could be a systemic. There could be a, a systemic situation where the soda models can raise so much money that they can out pay anybody that bills on top of ‘em, which would be something I don't think we've ever seen before just because we were so bottlenecked in engineering, and this is a very open question.[00:10:41] swyx: Yeah. It's, it is almost like bitter lesson applied to the startup industry.[00:10:45] Martin Casado: Yeah, a hundred percent. It literally becomes an issue of like raise capital, turn that directly into growth. Use that to raise three times more. Exactly. And if you can keep doing that, you literally can outspend any company that's built the, not any company.[00:10:57] You can outspend the aggregate of companies on top of [00:11:00] you and therefore you'll necessarily take their share, which is crazy.[00:11:02] swyx: Would you say that kind of happens in character? Is that the, the sort of postmortem on. What happened?[00:11:10] Sarah Wang: Um,[00:11:10] Martin Casado: no.[00:11:12] Sarah Wang: Yeah, because I think so,[00:11:13] swyx: I mean the actual postmortem is, he wanted to go back to Google.[00:11:15] Exactly. But like[00:11:18] Martin Casado: that's another difference that[00:11:19] Sarah Wang: you said[00:11:21] Martin Casado: it. We should talk, we should actually talk about that.[00:11:22] swyx: Yeah,[00:11:22] Sarah Wang: that's[00:11:23] swyx: Go for it. Take it. Take,[00:11:23] Sarah Wang: yeah.[00:11:24] Character.AI, Founder Goals (AGI vs Product), and GPU Allocation Tradeoffs[00:11:24] Sarah Wang: I was gonna say, I think, um. The, the, the character thing raises actually a different issue, which actually the Frontier Labs will face as well. So we'll see how they handle it.[00:11:34] But, um, so we invest in character in January, 2023, which feels like eons ago, I mean, three years ago. Feels like lifetimes ago. But, um, and then they, uh, did the IP licensing deal with Google in August, 2020. Uh, four. And so, um, you know, at the time, no, you know, he's talked publicly about this, right? He wanted to Google wouldn't let him put out products in the world.[00:11:56] That's obviously changed drastically. But, um, he went to go do [00:12:00] that. Um, but he had a product attached. The goal was, I mean, it's Nome Shair, he wanted to get to a GI. That was always his personal goal. But, you know, I think through collecting data, right, and this sort of very human use case, that the character product.[00:12:13] Originally was and still is, um, was one of the vehicles to do that. Um, I think the real reason that, you know. I if you think about the, the stress that any company feels before, um, you ultimately going one way or the other is sort of this a GI versus product. Um, and I think a lot of the big, I think, you know, opening eyes, feeling that, um, anthropic if they haven't started, you know, felt it, certainly given the success of their products, they may start to feel that soon.[00:12:39] And the real. I think there's real trade-offs, right? It's like how many, when you think about GPUs, that's a limited resource. Where do you allocate the GPUs? Is it toward the product? Is it toward new re research? Right? Is it, or long-term research, is it toward, um, n you know, near to midterm research? And so, um, in a case where you're resource constrained, um, [00:13:00] of course there's this fundraising game you can play, right?[00:13:01] But the fund, the market was very different back in 2023 too. Um. I think the best researchers in the world have this dilemma of, okay, I wanna go all in on a GI, but it's the product usage revenue flywheel that keeps the revenue in the house to power all the GPUs to get to a GI. And so it does make, um, you know, I think it sets up an interesting dilemma for any startup that has trouble raising up until that level, right?[00:13:27] And certainly if you don't have that progress, you can't continue this fly, you know, fundraising flywheel.[00:13:32] Martin Casado: I would say that because, ‘cause we're keeping track of all of the things that are different, right? Like, you know, venture growth and uh, app infra and one of the ones is definitely the personalities of the founders.[00:13:45] It's just very different this time I've been. Been doing this for a decade and I've been doing startups for 20 years. And so, um, I mean a lot of people start this to do a GI and we've never had like a unified North star that I recall in the same [00:14:00] way. Like people built companies to start companies in the past.[00:14:02] Like that was what it was. Like I would create an internet company, I would create infrastructure company, like it's kind of more engineering builders and this is kind of a different. You know, mentality. And some companies have harnessed that incredibly well because their direction is so obviously on the path to what somebody would consider a GI, but others have not.[00:14:20] And so like there is always this tension with personnel. And so I think we're seeing more kind of founder movement.[00:14:27] Sarah Wang: Yeah.[00:14:27] Martin Casado: You know, as a fraction of founders than we've ever seen. I mean, maybe since like, I don't know the time of like Shockly and the trade DUR aid or something like that. Way back in the beginning of the industry, I, it's a very, very.[00:14:38] Unusual time of personnel.[00:14:39] Sarah Wang: Totally.[00:14:40] Talent Wars, Mega-Comp, and the Rise of Acquihire M&A[00:14:40] Sarah Wang: And it, I think it's exacerbated by the fact that talent wars, I mean, every industry has talent wars, but not at this magnitude, right? No. Yeah. Very rarely can you see someone get poached for $5 billion. That's hard to compete with. And then secondly, if you're a founder in ai, you could fart and it would be on the front page of, you know, the information these days.[00:14:59] And so there's [00:15:00] sort of this fishbowl effect that I think adds to the deep anxiety that, that these AI founders are feeling.[00:15:06] Martin Casado: Hmm.[00:15:06] swyx: Uh, yes. I mean, just on, uh, briefly comment on the founder, uh, the sort of. Talent wars thing. I feel like 2025 was just like a blip. Like I, I don't know if we'll see that again.[00:15:17] ‘cause meta built the team. Like, I don't know if, I think, I think they're kind of done and like, who's gonna pay more than meta? I, I don't know.[00:15:23] Martin Casado: I, I agree. So it feels so, it feel, it feels this way to me too. It's like, it is like, basically Zuckerberg kind of came out swinging and then now he's kind of back to building.[00:15:30] Yeah,[00:15:31] swyx: yeah. You know, you gotta like pay up to like assemble team to rush the job, whatever. But then now, now you like you, you made your choices and now they got a ship.[00:15:38] Martin Casado: I mean, the, the o other side of that is like, you know, like we're, we're actually in the job hiring market. We've got 600 people here. I hire all the time.[00:15:44] I've got three open recs if anybody's interested, that's listening to this for investor. Yeah, on, on the team, like on the investing side of the team, like, and, um, a lot of the people we talk to have acting, you know, active, um, offers for 10 million a year or something like that. And like, you know, and we pay really, [00:16:00] really well.[00:16:00] And just to see what's out on the market is really, is really remarkable. And so I would just say it's actually, so you're right, like the really flashy one, like I will get someone for, you know, a billion dollars, but like the inflated, um, uh, trickles down. Yeah, it is still very active today. I mean,[00:16:18] Sarah Wang: yeah, you could be an L five and get an offer in the tens of millions.[00:16:22] Okay. Yeah. Easily. Yeah. It's so I think you're right that it felt like a blip. I hope you're right. Um, but I think it's been, the steady state is now, I think got pulled up. Yeah. Yeah. I'll pull up for[00:16:31] Martin Casado: sure. Yeah.[00:16:32] Alessio: Yeah. And I think that's breaking the early stage founder math too. I think before a lot of people would be like, well, maybe I should just go be a founder instead of like getting paid.[00:16:39] Yeah. 800 KA million at Google. But if I'm getting paid. Five, 6 million. That's different but[00:16:45] Martin Casado: on. But on the other hand, there's more strategic money than we've ever seen historically, right? Mm-hmm. And so, yep. The economics, the, the, the, the calculus on the economics is very different in a number of ways. And, uh, it's crazy.[00:16:58] It's cra it's causing like a, [00:17:00] a, a, a ton of change in confusion in the market. Some very positive, sub negative, like, so for example, the other side of the, um. The co-founder, like, um, acquisition, you know, mark Zuckerberg poaching someone for a lot of money is like, we were actually seeing historic amount of m and a for basically acquihires, right?[00:17:20] That you like, you know, really good outcomes from a venture perspective that are effective acquihires, right? So I would say it's probably net positive from the investment standpoint, even though it seems from the headlines to be very disruptive in a negative way.[00:17:33] Alessio: Yeah.[00:17:33] What's Underfunded: Boring Software, Robotics Skepticism, and Custom Silicon Economics[00:17:33] Alessio: Um, let's talk maybe about what's not being invested in, like maybe some interesting ideas that you would see more people build or it, it seems in a way, you know, as ycs getting more popular, it's like access getting more popular.[00:17:47] There's a startup school path that a lot of founders take and they know what's hot in the VC circles and they know what gets funded. Uh, and there's maybe not as much risk appetite for. Things outside of that. Um, I'm curious if you feel [00:18:00] like that's true and what are maybe, uh, some of the areas, uh, that you think are under discussed?[00:18:06] Martin Casado: I mean, I actually think that we've taken our eye off the ball in a lot of like, just traditional, you know, software companies. Um, so like, I mean. You know, I think right now there's almost a barbell, like you're like the hot thing on X, you're deep tech.[00:18:21] swyx: Mm-hmm.[00:18:22] Martin Casado: Right. But I, you know, I feel like there's just kind of a long, you know, list of like good.[00:18:28] Good companies that will be around for a long time in very large markets. Say you're building a database, you know, say you're building, um, you know, kind of monitoring or logging or tooling or whatever. There's some good companies out there right now, but like, they have a really hard time getting, um, the attention of investors.[00:18:43] And it's almost become a meme, right? Which is like, if you're not basically growing from zero to a hundred in a year, you're not interesting, which is just, is the silliest thing to say. I mean, think of yourself as like an introvert person, like, like your personal money, right? Mm-hmm. So. Your personal money, will you put it in the stock market at 7% or you put it in this company growing five x in a very large [00:19:00] market?[00:19:00] Of course you can put it in the company five x. So it's just like we say these stupid things, like if you're not going from zero to a hundred, but like those, like who knows what the margins of those are mean. Clearly these are good investments. True for anybody, right? True. Like our LPs want whatever.[00:19:12] Three x net over, you know, the life cycle of a fund, right? So a, a company in a big market growing five X is a great investment. We'd, everybody would be happy with these returns, but we've got this kind of mania on these, these strong growths. And so I would say that that's probably the most underinvested sector.[00:19:28] Right now.[00:19:29] swyx: Boring software, boring enterprise software.[00:19:31] Martin Casado: Traditional. Really good company.[00:19:33] swyx: No, no AI here.[00:19:34] Martin Casado: No. Like boring. Well, well, the AI of course is pulling them into use cases. Yeah, but that's not what they're, they're not on the token path, right? Yeah. Let's just say that like they're software, but they're not on the token path.[00:19:41] Like these are like they're great investments from any definition except for like random VC on Twitter saying VC on x, saying like, it's not growing fast enough. What do you[00:19:52] Sarah Wang: think? Yeah, maybe I'll answer a slightly different. Question, but adjacent to what you asked, um, which is maybe an area that we're not, uh, investing [00:20:00] right now that I think is a question and we're spending a lot of time in regardless of whether we pull the trigger or not.[00:20:05] Um, and it would probably be on the hardware side, actually. Robotics, right? And the robotics side. Robotics. Right. Which is, it's, I don't wanna say that it's not getting funding ‘cause it's clearly, uh, it's, it's sort of non-consensus to almost not invest in robotics at this point. But, um, we spent a lot of time in that space and I think for us, we just haven't seen the chat GPT moment.[00:20:22] Happen on the hardware side. Um, and the funding going into it feels like it's already. Taking that for granted.[00:20:30] Martin Casado: Yeah. Yeah. But we also went through the drone, you know, um, there's a zip line right, right out there. What's that? Oh yeah, there's a zip line. Yeah. What the drone, what the av And like one of the takeaways is when it comes to hardware, um, most companies will end up verticalizing.[00:20:46] Like if you're. If you're investing in a robot company for an A for agriculture, you're investing in an ag company. ‘cause that's the competition and that's surprising. And that's supply chain. And if you're doing it for mining, that's mining. And so the ad team does a lot of that type of stuff ‘cause they actually set up to [00:21:00] diligence that type of work.[00:21:01] But for like horizontal technology investing, there's very little when it comes to robots just because it's so fit for, for purpose. And so we kinda like to look at software. Solutions or horizontal solutions like applied intuition. Clearly from the AV wave deep map, clearly from the AV wave, I would say scale AI was actually a horizontal one for That's fair, you know, for robotics early on.[00:21:23] And so that sort of thing we're very, very interested. But the actual like robot interacting with the world is probably better for different team. Agree.[00:21:30] Alessio: Yeah, I'm curious who these teams are supposed to be that invest in them. I feel like everybody's like, yeah, robotics, it's important and like people should invest in it.[00:21:38] But then when you look at like the numbers, like the capital requirements early on versus like the moment of, okay, this is actually gonna work. Let's keep investing. That seems really hard to predict in a way that is not,[00:21:49] Martin Casado: I think co, CO two, kla, gc, I mean these are all invested in in Harvard companies. He just, you know, and [00:22:00] listen, I mean, it could work this time for sure.[00:22:01] Right? I mean if Elon's doing it, he's like, right. Just, just the fact that Elon's doing it means that there's gonna be a lot of capital and a lot of attempts for a long period of time. So that alone maybe suggests that we should just be investing in robotics just ‘cause you have this North star who's Elon with a humanoid and that's gonna like basically willing into being an industry.[00:22:17] Um, but we've just historically found like. We're a huge believer that this is gonna happen. We just don't feel like we're in a good position to diligence these things. ‘cause again, robotics companies tend to be vertical. You really have to understand the market they're being sold into. Like that's like that competitive equilibrium with a human being is what's important.[00:22:34] It's not like the core tech and like we're kind of more horizontal core tech type investors. And this is Sarah and I. Yeah, the ad team is different. They can actually do these types of things.[00:22:42] swyx: Uh, just to clarify, AD stands for[00:22:44] Martin Casado: American Dynamism.[00:22:45] swyx: Alright. Okay. Yeah, yeah, yeah. Uh, I actually, I do have a related question that, first of all, I wanna acknowledge also just on the, on the chip side.[00:22:51] Yeah. I, I recall a podcast that where you were on, i, I, I think it was the a CC podcast, uh, about two or three years ago where you, where you suddenly said [00:23:00] something, which really stuck in my head about how at some point, at some point kind of scale it makes sense to. Build a custom aic Yes. For per run.[00:23:07] Martin Casado: Yes.[00:23:07] It's crazy. Yeah.[00:23:09] swyx: We're here and I think you, you estimated 500 billion, uh, something.[00:23:12] Martin Casado: No, no, no. A billion, a billion dollar training run of $1 billion training run. It makes sense to actually do a custom meic if you can do it in time. The question now is timelines. Yeah, but not money because just, just, just rough math.[00:23:22] If it's a billion dollar training. Then the inference for that model has to be over a billion, otherwise it won't be solvent. So let's assume it's, if you could save 20%, which you could save much more than that with an ASIC 20%, that's $200 million. You can tape out a chip for $200 million. Right? So now you can literally like justify economically, not timeline wise.[00:23:41] That's a different issue. An ASIC per model, which[00:23:44] swyx: is because that, that's how much we leave on the table every single time. We, we, we do like generic Nvidia.[00:23:48] Martin Casado: Exactly. Exactly. No, it, it is actually much more than that. You could probably get, you know, a factor of two, which would be 500 million.[00:23:54] swyx: Typical MFU would be like 50.[00:23:55] Yeah, yeah. And that's good.[00:23:57] Martin Casado: Exactly. Yeah. Hundred[00:23:57] swyx: percent. Um, so, so, yeah, and I mean, and I [00:24:00] just wanna acknowledge like, here we are in, in, in 2025 and opening eyes confirming like Broadcom and all the other like custom silicon deals, which is incredible. I, I think that, uh, you know, speaking about ad there's, there's a really like interesting tie in that obviously you guys are hit on, which is like these sort, this sort of like America first movement or like sort of re industrialized here.[00:24:17] Yeah. Uh, move TSMC here, if that's possible. Um, how much overlap is there from ad[00:24:23] Martin Casado: Yeah.[00:24:23] swyx: To, I guess, growth and, uh, investing in particularly like, you know, US AI companies that are strongly bounded by their compute.[00:24:32] Martin Casado: Yeah. Yeah. So I mean, I, I would view, I would view AD as more as a market segmentation than like a mission, right?[00:24:37] So the market segmentation is, it has kind of regulatory compliance issues or government, you know, sale or it deals with like hardware. I mean, they're just set up to, to, to, to, to. To diligence those types of companies. So it's a more of a market segmentation thing. I would say the entire firm. You know, which has been since it is been intercepted, you know, has geographical biases, right?[00:24:58] I mean, for the longest time we're like, you [00:25:00] know, bay Area is gonna be like, great, where the majority of the dollars go. Yeah. And, and listen, there, there's actually a lot of compounding effects for having a geographic bias. Right. You know, everybody's in the same place. You've got an ecosystem, you're there, you've got presence, you've got a network.[00:25:12] Um, and, uh, I mean, I would say the Bay area's very much back. You know, like I, I remember during pre COVID, like it was like almost Crypto had kind of. Pulled startups away. Miami from the Bay Area. Miami, yeah. Yeah. New York was, you know, because it's so close to finance, came up like Los Angeles had a moment ‘cause it was so close to consumer, but now it's kind of come back here.[00:25:29] And so I would say, you know, we tend to be very Bay area focused historically, even though of course we've asked all over the world. And then I would say like, if you take the ring out, you know, one more, it's gonna be the US of course, because we know it very well. And then one more is gonna be getting us and its allies and Yeah.[00:25:44] And it goes from there.[00:25:45] Sarah Wang: Yeah,[00:25:45] Martin Casado: sorry.[00:25:46] Sarah Wang: No, no. I agree. I think from a, but I think from the intern that that's sort of like where the companies are headquartered. Maybe your questions on supply chain and customer base. Uh, I, I would say our customers are, are, our companies are fairly international from that perspective.[00:25:59] Like they're selling [00:26:00] globally, right? They have global supply chains in some cases.[00:26:03] Martin Casado: I would say also the stickiness is very different.[00:26:05] Sarah Wang: Yeah.[00:26:05] Martin Casado: Historically between venture and growth, like there's so much company building in venture, so much so like hiring the next PM. Introducing the customer, like all of that stuff.[00:26:15] Like of course we're just gonna be stronger where we have our network and we've been doing business for 20 years. I've been in the Bay Area for 25 years, so clearly I'm just more effective here than I would be somewhere else. Um, where I think, I think for some of the later stage rounds, the companies don't need that much help.[00:26:30] They're already kind of pretty mature historically, so like they can kind of be everywhere. So there's kind of less of that stickiness. This is different in the AI time. I mean, Sarah is now the, uh, chief of staff of like half the AI companies in, uh, in the Bay Area right now. She's like, ops Ninja Biz, Devrel, BizOps.[00:26:48] swyx: Are, are you, are you finding much AI automation in your work? Like what, what is your stack.[00:26:53] Sarah Wang: Oh my, in my personal stack.[00:26:54] swyx: I mean, because like, uh, by the way, it's the, the, the reason for this is it is triggering, uh, yeah. We, like, I'm hiring [00:27:00] ops, ops people. Um, a lot of ponders I know are also hiring ops people and I'm just, you know, it's opportunity Since you're, you're also like basically helping out with ops with a lot of companies.[00:27:09] What are people doing these days? Because it's still very manual as far as I can tell.[00:27:13] Sarah Wang: Hmm. Yeah. I think the things that we help with are pretty network based, um, in that. It's sort of like, Hey, how do do I shortcut this process? Well, let's connect you to the right person. So there's not quite an AI workflow for that.[00:27:26] I will say as a growth investor, Claude Cowork is pretty interesting. Yeah. Like for the first time, you can actually get one shot data analysis. Right. Which, you know, if you're gonna do a customer database, analyze a cohort retention, right? That's just stuff that you had to do by hand before. And our team, the other, it was like midnight and the three of us were playing with Claude Cowork.[00:27:47] We gave it a raw file. Boom. Perfectly accurate. We checked the numbers. It was amazing. That was my like, aha moment. That sounds so boring. But you know, that's, that's the kind of thing that a growth investor is like, [00:28:00] you know, slaving away on late at night. Um, done in a few seconds.[00:28:03] swyx: Yeah. You gotta wonder what the whole, like, philanthropic labs, which is like their new sort of products studio.[00:28:10] Yeah. What would that be worth as an independent, uh, startup? You know, like a[00:28:14] Martin Casado: lot.[00:28:14] Sarah Wang: Yeah, true.[00:28:16] swyx: Yeah. You[00:28:16] Martin Casado: gotta hand it to them. They've been executing incredibly well.[00:28:19] swyx: Yeah. I, I mean, to me, like, you know, philanthropic, like building on cloud code, I think, uh, it makes sense to me the, the real. Um, pedal to the metal, whatever the, the, the phrase is, is when they start coming after consumer with, uh, against OpenAI and like that is like red alert at Open ai.[00:28:35] Oh, I[00:28:35] Martin Casado: think they've been pretty clear. They're enterprise focused.[00:28:37] swyx: They have been, but like they've been free. Here's[00:28:40] Martin Casado: care publicly,[00:28:40] swyx: it's enterprise focused. It's coding. Right. Yeah.[00:28:43] AI Labs vs Startups: Disruption, Undercutting & the Innovator's Dilemma[00:28:43] swyx: And then, and, but here's cloud, cloud, cowork, and, and here's like, well, we, uh, they, apparently they're running Instagram ads for Claudia.[00:28:50] I, on, you know, for, for people on, I get them all the time. Right. And so, like,[00:28:54] Martin Casado: uh,[00:28:54] swyx: it, it's kind of like this, the disruption thing of, uh, you know. Mo Open has been doing, [00:29:00] consumer been doing the, just pursuing general intelligence in every mo modality, and here's a topic that only focus on this thing, but now they're sort of undercutting and doing the whole innovator's dilemma thing on like everything else.[00:29:11] Martin Casado: It's very[00:29:11] swyx: interesting.[00:29:12] Martin Casado: Yeah, I mean there's, there's a very open que so for me there's like, do you know that meme where there's like the guy in the path and there's like a path this way? There's a path this way. Like one which way Western man. Yeah. Yeah.[00:29:23] Two Futures for AI: Infinite Market vs AGI Oligopoly[00:29:23] Martin Casado: And for me, like, like all the entire industry kind of like hinges on like two potential futures.[00:29:29] So in, in one potential future, um, the market is infinitely large. There's perverse economies of scale. ‘cause as soon as you put a model out there, like it kind of sublimates and all the other models catch up and like, it's just like software's being rewritten and fractured all over the place and there's tons of upside and it just grows.[00:29:48] And then there's another path which is like, well. Maybe these models actually generalize really well, and all you have to do is train them with three times more money. That's all you have to [00:30:00] do, and it'll just consume everything beyond it. And if that's the case, like you end up with basically an oligopoly for everything, like, you know mm-hmm.[00:30:06] Because they're perfectly general and like, so this would be like the, the a GI path would be like, these are perfectly general. They can do everything. And this one is like, this is actually normal software. The universe is complicated. You've got, and nobody knows the answer.[00:30:18] The Economics Reality Check: Gross Margins, Training Costs & Borrowing Against the Future[00:30:18] Martin Casado: My belief is if you actually look at the numbers of these companies, so generally if you look at the numbers of these companies, if you look at like the amount they're making and how much they, they spent training the last model, they're gross margin positive.[00:30:30] You're like, oh, that's really working. But if you look at like. The current training that they're doing for the next model, their gross margin negative. So part of me thinks that a lot of ‘em are kind of borrowing against the future and that's gonna have to slow down. It's gonna catch up to them at some point in time, but we don't really know.[00:30:47] Sarah Wang: Yeah.[00:30:47] Martin Casado: Does that make sense? Like, I mean, it could be, it could be the case that the only reason this is working is ‘cause they can raise that next round and they can train that next model. ‘cause these models have such a short. Life. And so at some point in time, like, you know, they won't be able to [00:31:00] raise that next round for the next model and then things will kind of converge and fragment again.[00:31:03] But right now it's not.[00:31:04] Sarah Wang: Totally. I think the other, by the way, just, um, a meta point. I think the other lesson from the last three years is, and we talk about this all the time ‘cause we're on this. Twitter X bubble. Um, cool. But, you know, if you go back to, let's say March, 2024, that period, it felt like a, I think an open source model with an, like a, you know, benchmark leading capability was sort of launching on a daily basis at that point.[00:31:27] And, um, and so that, you know, that's one period. Suddenly it's sort of like open source takes over the world. There's gonna be a plethora. It's not an oligopoly, you know, if you fast, you know, if you, if you rewind time even before that GPT-4 was number one for. Nine months, 10 months. It's a long time. Right.[00:31:44] Um, and of course now we're in this era where it feels like an oligopoly, um, maybe some very steady state shifts and, and you know, it could look like this in the future too, but it just, it's so hard to call. And I think the thing that keeps, you know, us up at [00:32:00] night in, in a good way and bad way, is that the capability progress is actually not slowing down.[00:32:06] And so until that happens, right, like you don't know what's gonna look like.[00:32:09] Martin Casado: But I, I would, I would say for sure it's not converged, like for sure, like the systemic capital flows have not converged, meaning right now it's still borrowing against the future to subsidize growth currently, which you can do that for a period of time.[00:32:23] But, but you know, at the end, at some point the market will rationalize that and just nobody knows what that will look like.[00:32:29] Alessio: Yeah.[00:32:29] Martin Casado: Or, or like the drop in price of compute will, will, will save them. Who knows?[00:32:34] Alessio: Yeah. Yeah. I think the models need to ask them to, to specific tasks. You know? It's like, okay, now Opus 4.5 might be a GI at some specific task, and now you can like depreciate the model over a longer time.[00:32:45] I think now, now, right now there's like no old model.[00:32:47] Martin Casado: No, but let, but lemme just change that mental, that's, that used to be my mental model. Lemme just change it a little bit.[00:32:53] Capital as a Weapon vs Task Saturation: Where Real Enterprise Value Gets Built[00:32:53] Martin Casado: If you can raise three times, if you can raise more than the aggregate of anybody that uses your models, that doesn't even matter.[00:32:59] It doesn't [00:33:00] even matter. See what I'm saying? Like, yeah. Yeah. So, so I have an API Business. My API business is 60% margin, or 70% margin, or 80% margin is a high margin business. So I know what everybody is using. If I can raise more money than the aggregate of everybody that's using it, I will consume them whether I'm a GI or not.[00:33:14] And I will know if they're using it ‘cause they're using it. And like, unlike in the past where engineering stops me from doing that.[00:33:21] Alessio: Mm-hmm.[00:33:21] Martin Casado: It is very straightforward. You just train. So I also thought it was kind of like, you must ask the code a GI, general, general, general. But I think there's also just a possibility that the, that the capital markets will just give them the, the, the ammunition to just go after everybody on top of ‘em.[00:33:36] Sarah Wang: I, I do wonder though, to your point, um, if there's a certain task that. Getting marginally better isn't actually that much better. Like we've asked them to it, to, you know, we can call it a GI or whatever, you know, actually, Ali Goi talks about this, like we're already at a GI for a lot of functions in the enterprise.[00:33:50] Um. That's probably those for those tasks, you probably could build very specific companies that focus on just getting as much value out of that task that isn't [00:34:00] coming from the model itself. There's probably a rich enterprise business to be built there. I mean, could be wrong on that, but there's a lot of interesting examples.[00:34:08] So, right, if you're looking the legal profession or, or whatnot, and maybe that's not a great one ‘cause the models are getting better on that front too, but just something where it's a bit saturated, then the value comes from. Services. It comes from implementation, right? It comes from all these things that actually make it useful to the end customer.[00:34:24] Martin Casado: Sorry, what am I, one more thing I think is, is underused in all of this is like, to what extent every task is a GI complete.[00:34:31] Sarah Wang: Mm-hmm.[00:34:32] Martin Casado: Yeah. I code every day. It's so fun.[00:34:35] Sarah Wang: That's a core question. Yeah.[00:34:36] Martin Casado: And like. When I'm talking to these models, it's not just code. I mean, it's everything, right? Like I, you know, like it's,[00:34:43] swyx: it's healthcare.[00:34:44] It's,[00:34:44] Martin Casado: I mean, it's[00:34:44] swyx: Mele,[00:34:45] Martin Casado: but it's every, it is exactly that. Like, yeah, that's[00:34:47] Sarah Wang: great support. Yeah.[00:34:48] Martin Casado: It's everything. Like I'm asking these models to, yeah, to understand compliance. I'm asking these models to go search the web. I'm asking these models to talk about things I know in the history, like it's having a full conversation with me while I, I engineer, and so it could be [00:35:00] the case that like, mm-hmm.[00:35:01] The most a, you know, a GI complete, like I'm not an a GI guy. Like I think that's, you know, but like the most a GI complete model will is win independent of the task. And we don't know the answer to that one either.[00:35:11] swyx: Yeah.[00:35:12] Martin Casado: But it seems to me that like, listen, codex in my experience is for sure better than Opus 4.5 for coding.[00:35:18] Like it finds the hardest bugs that I work in with. Like, it is, you know. The smartest developers. I don't work on it. It's great. Um, but I think Opus 4.5 is actually very, it's got a great bedside manner and it really, and it, it really matters if you're building something very complex because like, it really, you know, like you're, you're, you're a partner and a brainstorming partner for somebody.[00:35:38] And I think we don't discuss enough how every task kind of has that quality.[00:35:42] swyx: Mm-hmm.[00:35:43] Martin Casado: And what does that mean to like capital investment and like frontier models and Submodels? Yeah.[00:35:47] Why “Coding Models” Keep Collapsing into Generalists (Reasoning vs Taste)[00:35:47] Martin Casado: Like what happened to all the special coding models? Like, none of ‘em worked right. So[00:35:51] Alessio: some of them, they didn't even get released.[00:35:53] Magical[00:35:54] Martin Casado: Devrel. There's a whole, there's a whole host. We saw a bunch of them and like there's this whole theory that like, there could be, and [00:36:00] I think one of the conclusions is, is like there's no such thing as a coding model,[00:36:04] Alessio: you know?[00:36:04] Martin Casado: Like, that's not a thing. Like you're talking to another human being and it's, it's good at coding, but like it's gotta be good at everything.[00:36:10] swyx: Uh, minor disagree only because I, I'm pretty like, have pretty high confidence that basically open eye will always release a GPT five and a GT five codex. Like that's the code's. Yeah. The way I call it is one for raisin, one for Tiz. Um, and, and then like someone internal open, it was like, yeah, that's a good way to frame it.[00:36:32] Martin Casado: That's so funny.[00:36:33] swyx: Uh, but maybe it, maybe it collapses down to reason and that's it. It's not like a hundred dimensions doesn't life. Yeah. It's two dimensions. Yeah, yeah, yeah, yeah. Like and exactly. Beside manner versus coding. Yeah.[00:36:43] Martin Casado: Yeah.[00:36:44] swyx: It's, yeah.[00:36:46] Martin Casado: I, I think for, for any, it's hilarious. For any, for anybody listening to this for, for, for, I mean, for you, like when, when you're like coding or using these models for something like that.[00:36:52] Like actually just like be aware of how much of the interaction has nothing to do with coding and it just turns out to be a large portion of it. And so like, you're, I [00:37:00] think like, like the best Soto ish model. You know, it is going to remain very important no matter what the task is.[00:37:06] swyx: Yeah.[00:37:07] What He's Actually Coding: Gaussian Splats, Spark.js & 3D Scene Rendering Demos[00:37:07] swyx: Uh, speaking of coding, uh, I, I'm gonna be cheeky and ask like, what actually are you coding?[00:37:11] Because obviously you, you could code anything and you are obviously a busy investor and a manager of the good. Giant team. Um, what are you calling?[00:37:18] Martin Casado: I help, um, uh, FEFA at World Labs. Uh, it's one of the investments and um, and they're building a foundation model that creates 3D scenes.[00:37:27] swyx: Yeah, we had it on the pod.[00:37:28] Yeah. Yeah,[00:37:28] Martin Casado: yeah. And so these 3D scenes are Gaussian splats, just by the way that kind of AI works. And so like, you can reconstruct a scene better with, with, with radiance feels than with meshes. ‘cause like they don't really have topology. So, so they, they, they produce each. Beautiful, you know, 3D rendered scenes that are Gaussian splats, but the actual industry support for Gaussian splats isn't great.[00:37:50] It's just never, you know, it's always been meshes and like, things like unreal use meshes. And so I work on a open source library called Spark js, which is a. Uh, [00:38:00] a JavaScript rendering layer ready for Gaussian splats. And it's just because, you know, um, you, you, you need that support and, and right now there's kind of a three js moment that's all meshes and so like, it's become kind of the default in three Js ecosystem.[00:38:13] As part of that to kind of exercise the library, I just build a whole bunch of cool demos. So if you see me on X, you see like all my demos and all the world building, but all of that is just to exercise this, this library that I work on. ‘cause it's actually a very tough algorithmics problem to actually scale a library that much.[00:38:29] And just so you know, this is ancient history now, but 30 years ago I paid for undergrad, you know, working on game engines in college in the late nineties. So I've got actually a back and it's very old background, but I actually have a background in this and so a lot of it's fun. You know, but, but the, the, the, the whole goal is just for this rendering library to, to,[00:38:47] Sarah Wang: are you one of the most active contributors?[00:38:49] The, their GitHub[00:38:50] Martin Casado: spark? Yes.[00:38:51] Sarah Wang: Yeah, yeah.[00:38:51] Martin Casado: There's only two of us there, so, yes. No, so by the way, so the, the pri The pri, yeah. Yeah. So the primary developer is a [00:39:00] guy named Andres Quist, who's an absolute genius. He and I did our, our PhDs together. And so like, um, we studied for constant Quas together. It was almost like hanging out with an old friend, you know?[00:39:09] And so like. So he, he's the core, core guy. I did mostly kind of, you know, the side I run venture fund.[00:39:14] swyx: It's amazing. Like five years ago you would not have done any of this. And it brought you back[00:39:19] Martin Casado: the act, the Activ energy, you're still back. Energy was so high because you had to learn all the framework b******t.[00:39:23] Man, I f*****g used to hate that. And so like, now I don't have to deal with that. I can like focus on the algorithmics so I can focus on the scaling and I,[00:39:29] swyx: yeah. Yeah.[00:39:29] LLMs vs Spatial Intelligence + How to Value World Labs' 3D Foundation Model[00:39:29] swyx: And then, uh, I'll observe one irony and then I'll ask a serious investor question, uh, which is like, the irony is FFE actually doesn't believe that LMS can lead us to spatial intelligence.[00:39:37] And here you are using LMS to like help like achieve spatial intelligence. I just see, I see some like disconnect in there.[00:39:45] Martin Casado: Yeah. Yeah. So I think, I think, you know, I think, I think what she would say is LLMs are great to help with coding.[00:39:51] swyx: Yes.[00:39:51] Martin Casado: But like, that's very different than a model that actually like provides, they, they'll never have the[00:39:56] swyx: spatial inte[00:39:56] Martin Casado: issues.[00:39:56] And listen, our brains clearly listen, our brains, brains clearly have [00:40:00] both our, our brains clearly have a language reasoning section and they clearly have a spatial reasoning section. I mean, it's just, you know, these are two pretty independent problems.[00:40:07] swyx: Okay. And you, you, like, I, I would say that the, the one data point I recently had, uh, against it is the DeepMind, uh, IMO Gold, where, so, uh, typically the, the typical answer is that this is where you start going down the neuros symbolic path, right?[00:40:21] Like one, uh, sort of very sort of abstract reasoning thing and one form, formal thing. Um, and that's what. DeepMind had in 2024 with alpha proof, alpha geometry, and now they just use deep think and just extended thinking tokens. And it's one model and it's, and it's in LM.[00:40:36] Martin Casado: Yeah, yeah, yeah, yeah, yeah.[00:40:37] swyx: And so that, that was my indication of like, maybe you don't need a separate system.[00:40:42] Martin Casado: Yeah. So, so let me step back. I mean, at the end of the day, at the end of the day, these things are like nodes in a graph with weights on them. Right. You know, like it can be modeled like if you, if you distill it down. But let me just talk about the two different substrates. Let's, let me put you in a dark room.[00:40:56] Like totally black room. And then let me just [00:41:00] describe how you exit it. Like to your left, there's a table like duck below this thing, right? I mean like the chances that you're gonna like not run into something are very low. Now let me like turn on the light and you actually see, and you can do distance and you know how far something away is and like where it is or whatever.[00:41:17] Then you can do it, right? Like language is not the right primitives to describe. The universe because it's not exact enough. So that's all Faye, Faye is talking about. When it comes to like spatial reasoning, it's like you actually have to know that this is three feet far, like that far away. It is curved.[00:41:37] You have to understand, you know, the, like the actual movement through space.[00:41:40] swyx: Yeah.[00:41:40] Martin Casado: So I do, I listen, I do think at the end of these models are definitely converging as far as models, but there's, there's, there's different representations of problems you're solving. One is language. Which, you know, that would be like describing to somebody like what to do.[00:41:51] And the other one is actually just showing them and the space reasoning is just showing them.[00:41:55] swyx: Yeah, yeah, yeah. Right. Got it, got it. Uh, the, in the investor question was on, on, well labs [00:42:00] is, well, like, how do I value something like this? What, what, what work does the, do you do? I'm just like, Fefe is awesome.[00:42:07] Justin's awesome. And you know, the other two co-founder, co-founders, but like the, the, the tech, everyone's building cool tech. But like, what's the value of the tech? And this is the fundamental question[00:42:16] Martin Casado: of, well, let, let, just like these, let me just maybe give you a rough sketch on the diffusion models. I actually love to hear Sarah because I'm a venture for, you know, so like, ventures always, always like kind of wild west type[00:42:24] swyx: stuff.[00:42:24] You, you, you, you paid a dream and she has to like, actually[00:42:28] Martin Casado: I'm gonna say I'm gonna mar to reality, so I'm gonna say the venture for you. And she can be like, okay, you a little kid. Yeah. So like, so, so these diffusion models literally. Create something for, for almost nothing. And something that the, the world has found to be very valuable in the past, in our real markets, right?[00:42:45] Like, like a 2D image. I mean, that's been an entire market. People value them. It takes a human being a long time to create it, right? I mean, to create a, you know, a, to turn me into a whatever, like an image would cost a hundred bucks in an hour. The inference cost [00:43:00] us a hundredth of a penny, right? So we've seen this with speech in very successful companies.[00:43:03] We've seen this with 2D image. We've seen this with movies. Right? Now, think about 3D scene. I mean, I mean, when's Grand Theft Auto coming out? It's been six, what? It's been 10 years. I mean, how, how like, but hasn't been 10 years.[00:43:14] Alessio: Yeah.[00:43:15] Martin Casado: How much would it cost to like, to reproduce this room in 3D? Right. If you, if you, if you hired somebody on fiber, like in, in any sort of quality, probably 4,000 to $10,000.[00:43:24] And then if you had a professional, probably $30,000. So if you could generate the exact same thing from a 2D image, and we know that these are used and they're using Unreal and they're using Blend, or they're using movies and they're using video games and they're using all. So if you could do that for.[00:43:36] You know, less than a dollar, that's four or five orders of magnitude cheaper. So you're bringing the marginal cost of something that's useful down by three orders of magnitude, which historically have created very large companies. So that would be like the venture kind of strategic dreaming map.[00:43:49] swyx: Yeah.[00:43:50] And, and for listeners, uh, you can do this yourself on your, on your own phone with like. Uh, the marble.[00:43:55] Martin Casado: Yeah. Marble.[00:43:55] swyx: Uh, or but also there's many Nerf apps where you just go on your iPhone and, and do this.[00:43:59] Martin Casado: Yeah. Yeah. [00:44:00] Yeah. And, and in the case of marble though, it would, what you do is you literally give it in.[00:44:03] So most Nerf apps you like kind of run around and take a whole bunch of pictures and then you kind of reconstruct it.[00:44:08] swyx: Yeah.[00:44:08] Martin Casado: Um, things like marble, just that the whole generative 3D space will just take a 2D image and it'll reconstruct all the like, like[00:44:16] swyx: meaning it has to fill in. Uh,[00:44:18] Martin Casado: stuff at the back of the table, under the table, the back, like, like the images, it doesn't see.[00:44:22] So the generator stuff is very different than reconstruction that it fills in the things that you can't see.[00:44:26] swyx: Yeah. Okay.[00:44:26] Sarah Wang: So,[00:44:27] Martin Casado: all right. So now the,[00:44:28] Sarah Wang: no, no. I mean I love that[00:44:29] Martin Casado: the adult[00:44:29] Sarah Wang: perspective. Um, well, no, I was gonna say these are very much a tag team. So we, we started this pod with that, um, premise. And I think this is a perfect question to even build on that further.[00:44:36] ‘cause it truly is, I mean, we're tag teaming all of these together.[00:44:39] Investing in Model Labs, Media Rumors, and the Cursor Playbook (Margins & Going Down-Stack)[00:44:39] Sarah Wang: Um, but I think every investment fundamentally starts with the same. Maybe the same two premises. One is, at this point in time, we actually believe that there are. And of one founders for their particular craft, and they have to be demonstrated in their prior careers, right?[00:44:56] So, uh, we're not investing in every, you know, now the term is NEO [00:45:00] lab, but every foundation model, uh, any, any company, any founder trying to build a foundation model, we're not, um, contrary to popular opinion, we're

In this episode of Run the Numbers, CJ sits down with Varsha Udayabhanu of Invisible to unpack what enterprise AI adoption actually looks like beyond the hype. They cover forward deployed engineers, eight-week solution sprints, value-based pricing when outcomes are hard to meter, ARR vs. services revenue, and why “momentum” beats traditional SaaS metrics. A tactical look at trust, expansion, and building durable AI revenue.—SPONSORS:Brex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsMetronome is real-time billing built for modern software companies. Metronome turns raw usage events into accurate invoices, gives customers bills they actually understand, and keeps finance, product, and engineering perfectly in sync. That's why category-defining companies like OpenAI and Anthropic trust Metronome to power usage-based pricing and enterprise contracts at scale. Focus on your product — not your billing. Learn more and get started at https://www.metronome.comRightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjTabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/runAbacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.ai—LINKS: Varsha: https://www.linkedin.com/in/varshaudayabhanu/Company: https://invisibletech.ai/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:Marketing as a Form of Capital Allocation With Carta's Head of Growth Angela Winegarhttps://youtu.be/rG09ehsrWv8—TIMESTAMPS:00:00 Intro03:21 What Invisible Technologies Does05:34 Enterprise AI Adoption Gap07:38 Forward Deployed Engineers09:44 Evolving GTM in AI Services10:36 Solution Sprints12:37 Sponsor — Brex | Metronome | RightRev15:56 Upfront Investment vs. Upside18:38 Bespoke Deals20:37 Value-Based Pricing in Enterprise AI22:12 Value Sold vs. Value Delivered23:31 Enterprise Revenue as a Portfolio of Bets24:53 Time-Bound Solution Sprints27:06 Sponsor — Rillet | Tabs | Abacum30:32 Humans in the Loop & Expert Incentives33:38 Niche Human Expertise34:10 Rethinking KPIs Beyond ARR35:44 Momentum Metrics39:00 Evaluating GenAI Financial Profiles40:47 Expansion as the Atomic Unit42:19 AdTech Lessons on Distribution & Brand43:23 Why Brand Matters for Enterprise47:10 Commoditization Risk48:31 Long-Ass Lightning Round53:20 Credits

How AI Agents are Disrupting the AdTech Landscape Semantic content classification driven by AI agents is currently transforming digital advertising and B2B content monetization as we know it. When leveraged the right way, marketers can classify B2B content into actionable signals and find the most relevant content across the open web. This shift toward AI-native advertising allows for a more sophisticated approach to targeting that moves beyond traditional cookies. So, how can brands strategically implement these tools to generate impactful results, and what does the rise of autonomous agents mean for the future of your digital marketing strategy? That's why we're talking to Brendan Norman (Co-Founder and CEO, Classify), who shares his expertise and experience on how AI agents are disrupting the AdTech landscape. During our conversation, Brendan discussed the evolution of digital advertising and the critical integration of AI and cloud-based tools to automate manual tasks and improve campaign optimization. He also elaborated on the massive shift from human-centric to agent-centric traffic, predicting that agent traffic will surpass human traffic within 18-24 months. Brendan also explained why he believes that the future belongs to marketers who can blend audience and contextual signals to monetize human and agent attention. He highlighted how new AI-native tools are democratizing advanced ad tech, significantly reducing costs and improving efficiency for large and small advertisers. https://youtu.be/yVobWZTmwco Topics discussed in episode: [03:01] Beyond Keywords: How semantic understanding allows advertisers to target the nuance of a page (like “snow removal” vs. just “winter”) rather than broad categories.  [06:46] Optimizing for AI Agents: Why “Generative Engine Optimization” (GEO) complements traditional SEO, and how brands must prepare for agents retrieving information instead of humans.  [12:34] The Shift in Web Traffic: The prediction that agent traffic will surpass human traffic on the web in the next 6 to 24 months.  [15:50] The Power of Context + Audience: Why the best advertising strategy combines who the user is (audience) with what they are consuming in the moment (context).  [20:47] Democratizing Ad Tech: How AI agents and new frameworks will allow smaller brands with smaller budgets to access sophisticated programmatic advertising tools.  [26:54] High-Fidelity Curation at Scale: How AI reduces the cost of processing massive data sets, making real-time optimization and curation accessible and sustainable.  [33:44] The “Middleman Tax”: A look at the inefficiency of current ad tech where only 35 cents of every dollar reaches the publisher, and how AI can fix this.  Companies and links mentioned: Brendan Norman on LinkedIn  Classify  Bluefish AI Agentic Advertising Org  IAB Tech Lab Transcript Brendan Norman – Classify, Christian Klepp Brendan Norman – Classify  00:00 I think overall, jobs will change. I think that people will have to spend a lot less time doing a lot of the manual, rote tasks that they’re doing today. You know, kind of in parallel with what we’re seeing in terms of vibe coding and people’s ability to build product really quickly, design new web pages really quickly, like get ship things out quickly. I think a lot of the infrastructure layer tools, or just call them like, like, chatGPT style, cloud based tools, LLMs (Large Language Models), we’ll see a lot deeper integration into existing advertising product. And what that does is it helps democratize the whole ecosystem. So I think it frees up people’s time, you know, to not have to do a lot of the basic administrative, you know, reporting, manual, campaign, optimization type stuff, and it will help service a lot better insights. Ultimately, I think the industry grows, and I think it scales even faster and cautiously, optimistically. I think that we, we will have back to building on the curation piece, and, you know, the advertiser, outcomes piece, publisher monetization piece, user experience piece, I think that all those things will increase. Christian Klepp  01:07 When done the right way and leveraging the right approach and technology, you can classify B2B content into actionable insights and find the most similar content across the open web. So how can this be done the right way, and what role do B2B Marketers play? Welcome to this episode of the B2B Marketers in the Mission podcast, and I’m your host, Christian Klepp. Today, I’ll be talking to Brendan Norman about this. He’s the Co-Founder and CEO of Classify, a software that organizes the world’s digital content, making a privacy, safe, searchable and monetizable. Tune in to find out more about what this B2B Marketers Mission is, and off we go. I’m gonna say Mr. Brendan Norman, welcome to the show. Brendan Norman – Classify  01:49 Thanks for having me, Christian. Christian Klepp  01:51 Great to have you on. I’m really looking for this conversation because, man, like you know, in our previous discussion, besides talking about snow and bad weather, we did have, we did have we did have some interesting discussions around, I’m going to say, AI machine learning, and how that all has some kind of like strong correlation to content. So let’s just dive in. I’m going to start with the first question here. So you’re on a mission to help publishers increase monetization potential and advertisers target the most relevant, curated inventory. So for this conversation, I’m going to focus on the following topic, and we can unpack it from there. So how B2B brands can optimize their own content. And you know, let’s be honest. Brendan, who the heck doesn’t want to do that, right? So your company classify, if I remember correctly. It’s a software that organizes the world’s digital content, making it privacy, safe, searchable and monetizable. So here’s the two-pronged question I’m happy to repeat. So first one is, walk us through how your software does that and B, how does this approach benefit? B2B companies looking to optimize their own content? Brendan Norman – Classify  03:01 Historically, how a lot of content gets categorized, classified, organized, it’s fairly unsophisticated, and it’s been fairly unsophisticated for a long time, just because, you know, the technology is difficult to do, and we haven’t really had the foundational ability to understand it in a way like a human understands it until fairly recently, and do it at Deep scale. So good analogy for this question is like, if you were having a we were having a conversation just a minute ago about the snow, you know, happening in Canada, and how cold it was and how much snow you got, and, you know, also around the fact that, like you had to shovel your driveway, you have a snow blower you were putting the snow. There’s a lot of different nuance to that conversation. I as a human, and most humans, are able to interpret all of that nuance and kind of positively negatively, understand that there’s a snow blower involved in that snow blower was used to remove the snow historically that conversation, you know, if it was just a blob of text, or if it were a web page, the the basic technology to understand it would have reduced it down to a category like snow or maybe winter, and that’s it, and that’s all the targeting that would have happened to that page. So our conversation, you know, gets transcribed. It gets put on a blog, or it gets put on a news site. The only thing that a machine could understand about it was, you know, snow and then potentially a keyword, tagged snow blower. And that’s all so we took a very different one. One of the reasons why you know that that makes it challenging for advertisers and also for publishers. If you’re the publisher of that content, you’re not able to help advertisers really understand the nuance to like, what are we talking about here? Because maybe an advertiser wants to sell snow blowers for that specific site. Maybe they’re looking to sell ski and since we were talking about removing snow from a driveway, probably not the best application to go sell skis on. What is helpful is to deeply understand all the nuance to like we were talking about a driveway. We were talking about removing snow from that driveway. So we invented, you know, a much better, more sophisticated way to scrape content, classify it according to all of the different, you know, nuances semantic understanding much more like a human would, and then embed all of those different, you know, semantic understandings into, you know, this, this, this file, and then we organize that in a way that makes it searchable and kind of understands all the relationships very quickly. And what that does is it helps advertisers, like if you know, I’m Honda selling snow blowers, which they make, arguably the best snow blower in the market, if they’re looking to reach people that are talking about snow removal from the driveway, they can very quickly see the list of all the different URLs across the internet, and they can build, you know, a deal ID, or they can build a targeting, contextual targeting segment to specifically pinpoint those very specific web pages. And that’s kind of how the technology works, and then also, also why it’s relevant to advertisers. Christian Klepp  06:21 Thanks so much for sharing that Brendan that definitely helps us give, you know, some perspective into, like, what your software does. And you know, just, I’m asking you this from, from somebody who probably has learned to write one or two lines of code, and that’s as far as my dev skills go. But like, how, how is your software different from like GEO (Generative Engine Optimization), or is there some kind of overlap? Brendan Norman – Classify  06:46 It’s fairly complementary. I mean, the problem that GEO, you know, is trying to solve, and we’ve got good friends, advisors, you know, like at Blue Fish AI and like, a really cool company, Andre, I worked with him at live rail. He was the co-founder back then, before we got acquired by Facebook, you know. And I think that the problem that they’re trying to solve is going back to that it was just stay on Honda snowblowers. They’re trying to help Honda understand how they’re represented inside of, inside of an LLM or inside of a chat bot. And what they also do is they help these companies restructure their pages for, you know, better representation inside of the other end of like a chatGPT or a cloud answer. So it is kind of SEO (Search Engine Optimization), but for the generative world where we sit on is kind of on a different side of that. It’s very complimentary, though, and we’re deeply understanding content at scale, and that’s helping, you know, the advertiser understand where to position their ad. We’re also just, you know, very quickly, moving into this new space of, traditionally, advertising technology is focused on a human going to a web page, reading that content, reading the article, watching a video, you know, whatever that content looks like, and then helping the right advertisers show up in a contextually relevant way, so that the human will click on that ad, and they’ll go to another web page, they’ll buy the thing, whatever somebody wants to sell. A very recent development, so back up a year or so, you know, chatGPT Claude when they’re out and their agents and their bots are scraping like going out to the web and they’re retrieving information. They’re doing it to train their models to make their models better at answering questions. But now, you know, fast forward to today. They’re actually spending more time just going to content and then using that content to answer a specific question. So like, what’s the best recipe for, you know, creating soft shell craps. It’ll query a couple different web pages. It’ll find that, it’ll retrieve that information and bring it back that that is not being monetized today. And there’s a really interesting thing that we’re, you know, we’re starting to work on, which is monetizing the attention of an agent. And, you know, it’s, there’s a lot to figure out, but it’s kind of like the early days of a web browser, and like early days of search, when humans would go, you know, to a search engine, they would pop in some keywords, or, like, right out of search, and then, you know, Google would look at their entire index of the web, which was an algorithm that was weighted based on the number of different contextual relevancy plus the number of connections between web pages. So a web page that I might have published in geocities.com that nobody else would link to, Christian Klepp  09:50 wow, GeoCities like… Brendan Norman – Classify  09:54 Throwing way back remember the days of like writing like HTML and you know, creating that, you know, looping in some type of image because nobody else had linked to that, like personalized page that you built, it would never get shown up. And, you know, the top 20 or 30 or probably even couple 1000, or maybe even 100,000 search results. So their algorithm was about contextual relevancy, plus the number of links that other pages that had to your page. And then they started to include advertising in that. So early days of ads in search were literally anything, you know, it’s any advertiser that wanted to advertise to you, and they were just kind of choosing the highest price, trying to figure out, you know, how do we make money? And then it evolved into much more contextually relevant ads and sponsored post or sponsored advertisements. So now you know, if you’re searching for, like, what’s the best, you know, LLM or chat bot, you’re probably going to see a sponsored ad from, you know, Claude and Perplexity and chatGPT. Now you’re also going to see the search results underneath those. What’s changing about that kind of rapidly is how we’re influencing because humans are spending less time going there and doing that, and also within Google, Gemini is also surfacing some AI summary quickly and kind of superseding that, creating a chatGPT experience inside of Google, which is a brilliant way to do it also. But a lot of human interaction with the web now is humans going to chatGPT going to cloud asking questions and kind of treating it like we used to treat search back in the day. So influencing that, influencing that agent, going out to the web and sitting in between. That is another really interesting way that you can help an advertiser tell that story, not necessarily to a human but to the agent who’s retrieving the information and then bringing it back to the human, Christian Klepp  11:56 Right, right, right? And if we’re talking about content, it’s, you know, doing it in such a way that the content shows up in the AI search. Brendan Norman – Classify  12:04 Exactly. Christian Klepp  12:05 Because everybody, everybody’s got those now, right, like Google or Bing, or whatever, they’ve got the, they’ve got the AI summary at the at the very top of the page, right when you, when you, when you key in something. Brendan Norman – Classify  12:17 Yeah. Christian Klepp  12:18 Okay, fantastic. I’m gonna move us on to the next question about because we’re on the topic of optimizing content. So what are some of the key pitfalls that like B2B Marketers and their content teams? What should they be mindful of, and what should they be doing instead? Brendan Norman – Classify  12:34 That would be actually a better question for some of the GEO companies and something like more SEO focused companies about how to specifically optimize like your content. It’s a great question. I haven’t spent as much time, you know, deeply thinking through that. And the problem that we’re trying to solve is more of, you know, at scale, what is the semantic understanding of like, how somebody has built their page and or construct the video, as opposed to advising them on what they should do? You know, to think about it in a way that’s either more engaging. I would pivot that question more to the Geo and SEO focused folks, yeah, but super high level. I mean realizing that now web has two primary users of traffic. There’s humans who are bouncing or reading a, you know, web page or watching a video. But there’s also agents. And now the scale is like, changing very, very quickly. So you know, in the next year, two years, everybody will have lots of agents, kind of doing things on the back end for them. And, you know, we believe that, you know, in the next what, 6,12,18,24 months, Agent traffic will surpass human traffic on the web. So realizing that there’s these kind of two layers that one, humans see a web page and nice pretty pictures, and, you know, they see the layout great, but also having a web page that’s optimized in HTML, markdown, JSON, in ways that agents consume that, and then also knowing the different types of agents. So the cool thing that we’re building right now, in addition to this content graph of all the content, which is effectively like a understanding all the context between the content. It’s a mouthful, an agent graph that helps to inform this is an agent coming to my site. So in a lot of ways, it’s very similar to the folks who over the last decade or so, have built these identity graphs or audience graphs, and they know that like you, Christian versus me, Brendan, they’ve got some profiling on us. They understand our search history, our retargeting, our purchase intent, a lot of things that they’re appending to like you as a specific profile or an IP address. The rapid evolution of all this is mapping out the land. Landscape of different agents, where they come from, and then the personalization of these agents, and basically applying a lot of the similar logic that we’ve used for identity graphs and for audience graphs towards agents to help understand, how do you modify the content on the back end that humans never see, so that when they’re retrieving information, interacting with the content they’re doing it, you’re presenting in a really thoughtful way that drives like the answers and the results that you want to Christian Klepp  15:33 right, right? No, absolutely, absolutely. And in our previous conversation, you talked a little bit about contextual versus audience targeting. So and I mean, I’ve asked you this back then, but do you think one is better than the other, or do you think that they can work together? Brendan Norman – Classify  15:50 They should absolutely work together. Christian Klepp  15:52 And why? Brendan Norman – Classify  15:54 The reason, the reason is, you know, knowing who you are is a very important piece to the puzzle. Like, and if you even take a step back, like, what’s the whole point of advertising? Like, the whole point of advertising is storytelling, so that a brand or a service or a company can help market their brand service to the right person they’re trying to sell them something. The cool thing about the internet is we all now have this, you know, basic shared awareness that, like, there are certain things that are paid for on the internet, certain types of content that are gated. I might buy a subscription to The Economist, you know, I pay Claude a certain amount of money, a lot to be able to use it, you know, a lot and chatGPT, and then a lot of the web is free. Facebook is free, Tiktok is free, Instagram is free, LinkedIn is free. But the economics, it’s very expensive to run these businesses, so they have to, you know, support it through advertising. Ideally, you know, there’s a couple of ways to think about it, and there’s one camp of people on the internet who think that advertising is a necessary evil or a last resort, you know, we just cram it in there and make some money. There’s another camper of folks who actually think that it can be additive to the experience. And one of the reasons why, you know, it’s kind of a meme, and you always hear people talking about, you know, I didn’t need this thing, but I saw an ad for it on Instagram, and just had to buy it because it was really cool. The reason why that exists is that their advertising is phenomenal, and the targeting and optimization is phenomenal. And why it’s phenomenal on the back end is it knows a lot about you know me, who I am, what I’m interested in, based on my history, what I’ve been engaging with, where I’m spending time, you know, what I’m looking at, but it also knows specifically when I’m looking at that thing, you know, it might have a framework of saying, Brendan, really, you know, likes these types of skis, you know, he’s interested in, You know, a couple other, couple other interesting products, but the best time to serve each one of those products might be different, and it’s different depending on what I’m looking at, what I’m thinking about in that exact moment. And to kind of align these, these different graphs, graphs of intent, contextual understanding, and then audience, you know, the best time to serve me an ad for a new pair of skis is when I’m reading an article about skiing or something about the mountains. You know, it’s not necessarily when I’m reading about the Warriors, because I’m not really thinking about skiing when I’m reading about basketball. So to your point, the most effective ads are when you’re combining those two sets. It’s great for the advertiser, because I’m much more likely to click on it and go check out the skis. It’s also giving me a better experience, because it feels more native to the overall content that I’m reading. And that’s why it’s so important. It shouldn’t be an afterthought or a necessary evil or a last resort. It should be something that is intentionally thought about the entire design, because it can, it can actually be a cool experience. Christian Klepp  19:06 Absolutely, absolutely. I mean, you know, you’re talking to somebody that started his career in the in the advertising industry, so, yeah, I’ve heard that one before, and what you’ve been describing in the past couple of minutes sounds to me a little bit like time of day marketing too, right? Because you’re you know, are you the had a guest on, like, a year ago who talked about this? Right? Is, is Brendan, the same guy at eight in the morning and one one in the afternoon and seven in the evening? Right? There’s different different times of the day, different mindset, different motivation, different reason for being on your device or looking at, looking at specific type of content, right? But it is interesting, right? And it’s interesting and sometimes a little bit scary, how, um, how quickly the algorithm picks, picks this stuff up, right? Like, for example, last year, I was researching a lot on Japan, because we went there, right? Family trip and whatnot. And. And that’s what I kept seeing on Instagram, right? Like, because I was looking up specific temples and whatnot and and today I got another push. Like, would you like to invest in a temple that’s an on island in the Sea of Japan, right? Brendan Norman – Classify  20:12 Like, sorry, did you invest? Christian Klepp  20:17 No, I did not. But it was just, it was just funny that I got that ad right, like, it’s, like, Okay, interesting, but like, it’s so like it not, was not on my radar at all, right, Brendan Norman – Classify  20:29 Yeah, Christian Klepp  20:29 Okay, great. From your experience, and you talked a little bit about it now in the past couple of minutes, but like, from your experience, how can leveraging AI agents improve efficiency and save marketing leaders time? Brendan Norman – Classify  20:47 Ooh, there’s a couple different ways to think about that. So you know, part of it is this new agentic framework for how existing tools, you know, advertising and marketing tools, will communicate with each other today. You know, it’s fairly complex. You know, if I wanted to go build a contextual targeting segment to help one of our brands that we work with find the right contextual or inventory to target contextually, I would have to work with them. We build a targeting segment. We would upload that into our one of our SSPs, we would build a deal ID, you know, they would connect it back. And there’s a lot of different pieces that happen along the way. And each one of those pieces you have to go to, you know, a UI, I’ve got to go to a dashboard, I’ve got to push that thing in. Some of it happens through an API, but a lot of it happens like going to a whole bunch of different web pages to make sure this stuff all works. So stuff all works. What’s cool about agents? And I’ll unpack this, and then I’ll go to the more of the consumer focus side too. But what’s really cool about agents using, you know, things like the ACP framework from the Agentic Advertising Org., the ARTF (Agentic Real Time Framework) from IAB Tech Lab is they’re kind of built on some of the existing frameworks that allow humans to use natural language to communicate between these different systems. So there’s still the back end pipes of API pushing data or pulling data from one system to another. But on top of that is more of an agentic framework that allows, you know, a human just to use some prompting, like in chatGPT, to make a request, you know, that talks to a back end system. So that’s one part of the agentic framework for like, you know, how to think about this through the lens of advertising and marketing. And then the other side is, you know, more of the consumer focused. There are so many interesting and very quickly growing tools you know, that you can start to plug in, into Cloud, into Cloud code, and to building things that just rapidly accelerate development of different products and your ability to analyze data quickly. I think in the next, you know, 6 to 12 months, we’re going to have a totally different landscape for how people are buying like trading media also, you know, one more final thought about all of this is that a lot of the sophisticated tooling and pipes that we have are only accessible towards the largest advertisers today. And I think that you’ll pretty quickly see a democratization of the ability for anybody to just buy programmatic ads, whether you’ve got a $20 a month budget or a $20 million a month budget. Now, the ability to similar types of tools to access the right content across the web will start to be available towards a lot more folks outside of the existing, you know, kind of ad tech ecosystem. Christian Klepp  23:55 And I might be stating the obvious when I say this here, but that’s a good thing, isn’t it, because, I mean, I, again, I came out of this industry, and I know that, like, you know, if you wanted to advertise in the New York Times, for example, right? Like, how expensive that would be, or, or anything that was print, right? And then they migrated all that to digital, and then it still wasn’t, it still wasn’t affordable. It was, it was cheaper than print, but still not like, exactly like, you know, yeah, I wonder, wonder if they’ll be worth the investment or not. And then now you have this, this push towards the democratization of all of this through AI and machine learning and, and I do think that you know, for all the the scare mongering that you know people are doing now with, with, oh, you know, all this stuff around AI, I do think that that part certainly will be advantageous to to B2B companies and to marketing in general. Brendan Norman – Classify  24:49 Great. I mean, yeah, optimistically, I think I’m excited about the entire landscape changing because it does a couple things. It allows for much more contextually relevant ads. I know right now there’s only, let’s call it to the magnitude of like, 1000s, 10s of 1000s, maybe hundreds of 1000s, of campaigns and or brands that are able to use these pipes to reach the largest publishers. And all of a sudden you expand that out. You know, I think between meta and Google, they each have somewhere between 15 to 20 million unique advertisers on their platforms, and what that means is, you get really hyper specific ads. And it also means that, like, I might get a local ad for my hometown here for some restaurant that’s launching a promotion that I might only get here, and I might only get to your point, maybe not in the morning, but I’ll get in the evening. There’s a lot of different data sets around my identity, you know, the psychographic profile, contextual understanding of what I’m reading at that exact moment. And what it does a lot of things. It helps smaller brands get more traction, get more visibility. It also just helps improve the publisher experience, and like publishers, make more money. And then the user who’s consuming that content, reading the web page, watching a video, also has just a better experience. And then the other layer of that will continue to just go on, this narrative of agentic, tension, but the agents who are reading that content, watching that video for an end user. On the other side, are also able to interact with advertising content that’s very contextually relevant to the content that they’re consuming again, and it’s good for the storytelling of the advertiser and good for monetization of that publisher too. Christian Klepp  26:38 Absolutely, absolutely. Okay. So how can high fidelity curation? This is the next question, right? How can high fidelity curation make B2B companies more sustainable? And if you can just provide an example, Brendan Norman – Classify  26:54 Curations like, it’s such an interesting term, but you know, effectively, it’s just, it’s helping to use the word and the definition, the definition in the word, curate the right inventory to run an ad campaign on, and curate the right inventory and audiences. So it’s a really important part of the business. I think it involves a couple things. It involves front end targeting, of knowing who’s the back to that question, who’s the audience, and then what’s the right content, and then it also involves a lot of ongoing optimization. And I’ll say that there are some some interesting companies that that are really good at curation, who are building out the right automatic tools to think about more real time optimization, and it’s something that the really big social media companies do very well, like they’re constantly looking at lots and lots of signals when they’re running a campaign, and they’re looking at inventory and stitching together based on the signals that they’re acquiring around. Why certain campaigns do well, to your point, you know, when we’re testing that, selling that pair of skis to Christian, we’re testing a lot of things. We’re testing what he’s reading, you know, we’re testing maybe time of day. We’re testing, you know, where he is. There’s a lot of different elements on the back end that they will ingest and understand and then refeed into that targeting and optimization algorithm. And I think that that is one of the cool things that AI to use, like the air quotes, AI will help enable the processing of a lot of this data to just be a lot faster, be a lot more cost effective, and a lot of these systems that you know previously have been not accessible to the ad tech ecosystem, just because we we operate at such a crazy scale of 10s, hundreds of billions of requests and impressions and transactions that happen every single day. It’s very cost expensive if you’re processing all of that data and all these different signals, with the advancement of how the model cost is getting a lot less expensive, very quickly, not just from an LLM perspective, but then the foundational layers and the infrastructure layers, like we’re doing contextual intelligence as an infrastructure layer. There are inference layers that all kind of sit underneath the LLM and help inform an LLM understanding of that content. As those costs start to decrease, you’ll start to see a lot better performance from curation, just because, you know, it’s not as cost prohibitive, and we’ll be able to find that balance in terms of economics. Christian Klepp  29:45 Yeah, yeah, you hit the nail on the head there. Because, you know, I was just writing this down. You said faster, more cost effective and in my head, and you said it, it’s like, and at scale, like, you can scale this stuff faster, like, when I when I think back, like, years ago, when we, when we launched an ad campaign, and, you know, just the amount of effort, like, for the print and then the cost into, you know, the media placements and all of that and and just alone for like, one city, just just the amount of investment that was involved in all of that, right? Just think, thinking about that. It’s like, gosh, and then now you can scale all of that, like, even faster, because it’s because it’s digital, right? So it’s just such an incredible evolution. Like, I’m getting just as excited as you are man, I’m like, for this next question. Brendan, I’m not sure if you’re the type that likes to do this, but I need you to look into the crystal ball for a second here, right? Because we’re looking at, like, stuff that is, you know, the events that are yet to come, if I’m gonna that, make it sound a little bit suspenseful, but, um, the future of digital advertising, like, how do you think that could become less fragmented and more optimized with everything that we’ve talked about in this conversation. Brendan Norman – Classify  31:04 Yeah, I caution against, like, having any, any specific predictions, and more of, like, a framework for, I mean, for me, at least, yeah, more of a framework for how I think overall, jobs will change. I think that people will have to spend a lot less time doing a lot of the manual, rote tasks that they’re doing today. And, you know, kind of in parallel with what we’re seeing in terms of vibe coding and people’s ability to build product really quickly, design new web pages really quickly. Like, get ship things out quickly. I think a lot of the the infrastructure layer tools, or just call them like, you know, the like, chatGPT style, cloud-based tools, LLMs, we’ll see a lot deeper integration into existing advertising product. And what that does is it helps democratize the whole ecosystem. So I think it frees up people’s time to not have to do a lot of the basic administrative, reporting, manual, campaign, optimization type stuff, and it will help service a lot better insights. Ultimately, I think the industry grows, and I think it scales even faster. And, you know, cautiously, optimistically, I think that we, we will have back to building on the curation piece, and, you know, the advertiser, outcomes piece, publisher, monetization piece, user experience piece, I think that all those things will increase, and I I’m hopeful that with the integration of just better technology, embedding AI into a lot of these systems, it’s going to help steer us towards having better experiences across any type of Publisher content. I think that the advertisers will see better outcomes. I think that the people that are in this industry will get to think more creatively about how they’re, you know, building better creative storytelling, better reaching the right people with those stories. And my hope is that it just continues to expedite and grow the overall industry. Brendan Norman – Classify  33:17 That will be my hope as well. All right, get up on your soapbox here for a little bit. What is a status quo in your area of expertise? So anything that we’ve talked about now in this conversation, what’s the status quo that you passionately disagree with and why? Oh, you must have a ton. Brendan Norman – Classify  33:44 I definitely do. I mean, you know, Christian Klepp  33:48 just name one, just one, Brendan Norman – Classify  33:50 Like in any industry, you know, there’s always, there’s always the early adopters, you know, there’s always the kind of like the middle stack, you know, there’s always, like, the laggards. There’s definitely, you know, a smaller, but growing quickly, minority of folks who are really leaning into, you know, I’ll just call it AI, and then the agentic web, and there’s a lot of discussion right now in ad tech around like, what that means? I’m still hearing that. There’s a lot of skeptics who are kind of making fun of it, or, you know, trash talking about different protocols. Fine, like those are the folks that are absolutely going to get left behind. And I think a lot of those folks on the soapbox in the next 6 to 12 months will look back at, you know what they said, and we’ll all kind of say that didn’t age well, and you were not building this stuff. You weren’t fingers on keyboard or hands on keyboard. Vibe marketing, vibe targeting, building stuff like shipping new product and testing and iterating. What I what I don’t think, is that the really big platforms are just able to be super nimble and adapt to a lot of these new frameworks quickly, totally like the pipes will continue to stay there. I think that there will be startups that are more nimble, that can build and ship things, you know, proof of concepts, prototypes, get things out, learn from them, fail, iterate, and then start to scale meaningful businesses without having to rely on a lot of the existing infrastructure that exists today. Do I think the trade desk is, you know, going anywhere? No, do I think that they will, like, continue to be a valuable piece in this ecosystem, absolutely. And I think that they will ship things. I think that they’ll enable the industry like to build on top of of the pipes that they’ve already built. And at the same time, I think a lot of that rapid advancement will come from startups who are kind of proving that, like they don’t necessarily need the existing pipes and channels to be able to at the end of the day, you know, this whole ecosystem is about helping an advertiser surface their ad against the right content for a human or for an agent. And there have been a lot of folks kind of sitting in the middle for that space for a long time. One of my favorite stats, soapboxy stats, is that if an advertiser puts $1 in to the open web with a programmatic web, 35 cents comes out to a publisher, so 65 cents is being taken by some combination of middlemen, you know, who are collecting a margin for, you know, different services, also some version of fraud. There’s a lot of things that happen in between that and what I’m again, cautiously optimistic about, you know, like the big picture, AI, of facilitating, is the ability to reduce that margin so that, you know, advertiser puts $1 in. A lot more of that dollar comes out towards the publisher, I think big social media, you know, it’s around 70 cents comes out. So they take, you know, somewhere between 25 to 30 cents, which is kind of the value exchange of providing the services, all the targeting, all the technology that goes into supporting that, you know, as a more fair exchange. So I think what a lot of the folks on more of the startup on more of like the front end of the frontier tech in the space we’re excited about is getting to reduce a lot of that inefficiency and a lot of that margin in the middle, and helping more of that dollar show up towards the publisher where it should. Christian Klepp  37:34 Boom and there you have it. Man Brendan, this has been awesome conversation, so thanks again for your time, please. Quick intro to yourself and how folks out there can get in touch with you. Brendan Norman – Classify  37:45 Yeah. Brendan Norman, CEO co-founder at Classify, please. You know, hit me up on LinkedIn or shoot me an email. Check out our website, which is, you know, www.tryclassify.com. I’m happy to connect. You know, if you have questions about advertising from a publisher side, from an advertiser side. Love to chat about it. Christian Klepp  38:06 Sounds good. Sounds good once again. Brendan, thanks for your time. Take care, stay safe and talk to you soon. Brendan Norman – Classify  38:13 Cool. Thanks, Christian. Christian Klepp  38:14 All right. Bye for now.

In episode #354, Ben shares the results from his 7th Annual SaaS Tech Stack Survey and reveals the top accounting solutions used by software, SaaS, and AI companies today. With participation across 22 software categories, this year's survey highlights both the consistent market leaders and the rise of newer, AI-first ERP platforms. While legacy players continue to dominate, new entrants are gaining meaningful traction. Ben breaks down the “Power Six” accounting platforms and what their market concentration tells us about the current state of financial systems in tech companies. Resources Mentioned 7th Annual SaaS Tech Stack Survey: https://www.thesaascfo.com/surveys/finance-accounting-tech-stack-survey/ Light, sponsor of the core accounting category: https://light.inc/ What You'll Learn The top accounting and ERP systems used by SaaS and AI companies How the “Power Six” now dominate the accounting stack landscape Which newer AI-first ERP platforms are gaining traction How concentrated is the accounting software market among SaaS companies Why accounting system selection matters as companies scale ARR Why It Matters Your accounting system is the foundation of your financial reporting, SaaS metrics, and KPI tracking Poor financial systems limit your ability to calculate ARR, revenue retention, and other recurring revenue metrics As revenue grows, moving from SMB accounting tools to more robust ERP and financial systems becomes critical Investors and auditors expect scalable accounting infrastructure as companies mature Understanding market trends helps founders and CFOs evaluate whether their current financial systems can support growth

Venture capital investment in New York City is increasingly concentrated in fewer, larger and more mature artificial intelligence companies, a shift that is reshaping Manhattan's office market. To discuss this situation, Ben Bass, vice chairman in JLL's national brokerage division, joins the program to explain the AI forces at play and how it benefits office investors. (02/2026)

Venture capital investment in New York City is increasingly concentrated in fewer, larger and more mature artificial intelligence companies, a shift that is reshaping Manhattan's office market. To discuss this situation, Ben Bass, vice chairman in JLL's national brokerage division, joins the program to explain the AI forces at play and how it benefits office investors. (02/2026)

Send a textIn this special February compilation episode of AI and the Future of Work, we explore what it truly takes to build AI companies designed to last.While AI innovation moves fast, enduring companies are built on fundamentals. Clear problem selection. Thoughtful product design. Ethical intent. Leadership under uncertainty. And the resilience required to keep going when the market pushes back.This episode brings together insights from founders and operators who have built, scaled, and sustained AI-driven companies across different stages and industries. Their stories reveal a shared truth. Long-term success depends less on hype and more on discipline, courage, and trust.Featured GuestsEric Olson, CEO and Co-founder of Consensus - Listen to the full conversation here: https://www.buzzsprout.com/520474/episodes/11574063 Rich White, Founder of UserVoice and CEO of Fathom - Listen to the full conversation here: https://www.buzzsprout.com/520474/episodes/11911533 Dmitry Shapiro, CEO of MindStudio - Listen to the full conversation here: https://www.buzzsprout.com/520474/episodes/14866979 Daniel Marcous, Founder and CTO of April, former CTO of Waze - Listen to the full conversation here: https://www.buzzsprout.com/520474/episodes/12679210 George Sivulka, CEO of Hebbia - Listen to the full conversation here: https://www.buzzsprout.com/520474/episodes/16572788 What You'll LearnWhy founders must act before certainty appearsHow solving real pain leads to stronger, longer-lasting companiesWhat ethical intent looks like in practical AI system designWhy trust, accuracy, and discipline matter more than speedHow resilience shapes leadership through uncertaintyWhat separates durable AI companies from short-lived experimentsInspired by something you heard in this episode?Share your favorite insight on social and tag us. We'd love to hear what resonated with you. And don't forget to subscribe to AI and the Future of Work for more conversations with the founders and leaders shaping what comes next.Other special episodes: Lessons from Four Unicorn CEOs Disrupting Massive Markets with AI (Special Episode)Artificial General Intelligence: Can Machines Really Think Like Us? (Special Episode)Ethical AI in Hiring: How to Stay Compliant While Building a Fairer Future of Work (HR Day Special Episode)AI and the Law: How AI Will Change Legal Careers (Special Episode)AI and Safety: How Responsible Tech Leaders Build Trustworthy Systems (National Safety Month Special)Lessons from Leaders: How AI Is Redefining Work and the Human Experience (Labor Day Special Episode)365: What We've Learned from 364 Expert Conversations (Special Episode)

What Kind of Business Should I Buy? If you're thinking about buying a business, this is the question that determines everything. Not how to fund it. Not how to structure it. Not even how to find it. But what kind of business should you buy? In this week's episode, Jonathan Jay answers the foundational question every serious dealmaker must get right and explains why choosing the wrong business is the fastest way to sabotage your future success  . Start With the End in Mind Jonathan opens with a principle borrowed from Stephen Covey: Begin with the end in mind. Before you even look at sectors or valuations, you need clarity on your outcome. Are you: Escaping corporate life? Growing your existing business? Building a group to sell for seven or eight figures? Each goal demands a completely different acquisition strategy. If you want to replace your salary, Jonathan challenges you to aim higher than feels comfortable. If you want to scale your current company, acquisition is the fastest way to move the needle. If you want generational wealth, buy-and-build might be your path. But the type of business you buy must match the outcome you want. Why Most First-Time Buyers Aim Too Low One of the most controversial sections of this episode? Size. Jonathan argues that most first-time buyers go too small — and pay the price. Businesses making under £100,000 net profit often: Depend too heavily on the owner Lack proper management accounts Have fragile teams Leave no room for post-acquisition wobble Instead, he shares what he looks for: At least £1m revenue At least £200k net profit Stable margins (15–25%+) Strong management in place Recurring or repeat revenue The effort required to buy a £200k profit business is not ten times harder than buying a £20k one. But the impact on your life absolutely is. The Three Core Acquisition Paths Jonathan breaks down three common strategies: Escape the Day Job: Buy a business that produces serious income — ideally 10x your salary. Grow an Existing Business: Acquire competitors, suppliers, complementary businesses, or geographic expansions. Buy-and-Build: Acquire smaller businesses at lower multiples, combine them, and sell the larger group at a higher multiple. He explains: What fragmented markets are Why M&A activity above you matters How multiple arbitrage works Why strong management becomes critical at scale And importantly — why one deal can change your life. The Worst Types of Businesses to Buy Jonathan doesn't hold back here. Avoid: Owner-dependent businesses Fad businesses Highly volatile or "spiky" profit businesses Overleveraged acquisitions Companies reliant on family members Businesses where relationships walk out the door with the seller He also explains why buying too small can mean buying yourself a job. And that's not what this is about. Funding, Risk and Structure This episode also covers: Why over-leveraging kills deals Why working capital matters more than most buyers realise Why your first deal is the most important Why corporate structure must be set up properly Why personal guarantees should be limited and contained Jonathan's position is clear: Deal number one sets the foundation for everything that follows. Get it right, and you build momentum. Get it wrong, and you may never do deal two. The Big Takeaway Buying a business isn't just about buying something. It's about buying the right thing. With: The right margins The right management The right structure The right funding And the right strategic fit for your long-term goal Clarity at the beginning prevents regret later. Listen Now If you're serious about buying a business in 2026 — or even just thinking about it — this episode gives you the strategic filter you need before you start looking at opportunities. Listen now and make sure your first deal is the right one.   If you're serious about buying a business – and avoiding the mistakes Jonathan outlines – book a free Clarity Call with one of his team:

As artificial intelligence companies roll out more sophisticated agents, many analysts and investors raised concerns that AI could replace traditional software. Some are dubbing this the “SaaSpocolypse.”New AI tools allow users to “vibe code,” or describe what you'd like to create in plain language and have the AI generate the code for you. This could make some software easier for companies to create themselves.Marketplace's Stephanie Hughes spoke with Daniel Newman, CEO of The Futurum Group, a technology research firm, to learn more.

As artificial intelligence companies roll out more sophisticated agents, many analysts and investors raised concerns that AI could replace traditional software. Some are dubbing this the “SaaSpocolypse.”New AI tools allow users to “vibe code,” or describe what you'd like to create in plain language and have the AI generate the code for you. This could make some software easier for companies to create themselves.Marketplace's Stephanie Hughes spoke with Daniel Newman, CEO of The Futurum Group, a technology research firm, to learn more.

The run is hot economy is here! Today we talk markets, and debunk alarmist headlines about rising Japanese bond yields. We also talk about a significant market rotation: expensive mega-cap tech stocks are faltering while capital flows into "boring" sectors like staples, industrials, energy, healthcare, and utilities, with international markets also outperforming. Watch out about chasing falling tech names or trying to pick bottoms in areas like crypto. Diversification is always the way to go so understand sentiment cycles and focus on where money is flowing rather than where it has already been. Successful investing is about discipline, context, and avoiding emotional decisions. We discuss... Japan's 10-year government bond yield rising from near 0% to over 2%, which has sparked global concern. Because most Japanese government debt is owned domestically—by the central bank and pensions—the systemic risk narrative may be exaggerated. Market headlines often amplify short-term moves without proper historical framing. A large percentage of U.S. stocks are trading at very high price-to-sales ratios, exceeding even dot-com-era levels in some measures. Companies like Apple have high valuations despite limited recent earnings growth, raising questions about sustainability. Rotations are normal cycles in markets, where leadership shifts rather than the entire market collapsing. Utilities and staples—traditionally "boring" sectors—have recently outperformed while software and high-beta tech stocks have sold off sharply. International markets, particularly emerging markets and Europe, have outperformed the U.S. year-to-date. Heavy AI-related capital expenditures announced by large tech firms may have contributed to investor concerns. We compare crypto cycles to past tech bubbles, noting that true bottoms often occur when sentiment disappears and investors stop paying attention. Focus on where capital is flowing now rather than chasing sectors based on past performance. Diversification, patience, and understanding market cycles are essential for long-term investing success. Today's Panelists: Kirk Chisholm | Innovative Wealth Phil Weiss | Apprise Wealth Management Follow on Facebook: https://www.facebook.com/moneytreepodcast Follow LinkedIn: https://www.linkedin.com/showcase/money-tree-investing-podcast Follow on Twitter/X: https://x.com/MTIPodcast For more information, visit the show notes at https://moneytreepodcast.com/run-it-hot-economy-is-here-791 

Here is a FAQ Video on the Courses: https://youtu.be/0F7imrzjXWs Here is a deep dive into which course is best for you: https://youtu.be/JM_jgS8M-iU https://www.b2bRevenue.com - Get Your Free E-Book on How Companies make Decisions. FAQ: 1 YEAR ACCESS, PAY MONTHLY OR ANNUALLY NOT A SUBSCRIPTION OFFICE HOURS EVERY  OTHER WEEK VIA ZOOM. 1 HOUR GROUP Q&A. UNLIMITED 1-ON-1'S  ARE FREE AS LONG AS THEY CAN BE SHARED IN THE COURSE. 1-ON-1 ARE FULL ACCESS ON DAY ONE - NOTHING IS GATED OR TIME RELEASED. ALL CONTENT IS VIDEO BASED AND SELF PACED I RECOMMEND TAKE COURSE ONCE WITHOUT NOTES OR APPLYING IT SO YOU UNDERSTAND THE BIG PICTURE FIRST. THEN TAKE AND APPLY IT STEP BY STEP. YOU START WHEN YOU WANT AND GO AS FAST OR SLOW AS NEEDED.   Email me additional questions: briangburns@me.com     — SAMPLE EMAIL TO EXPENSE THE COURSE MGR,   I have been listening to the brutal truth about sales podcast for X months and it speaks to the issues we face.   They currently offer a course that includes video instruction, group Q&A and One-on-One coaching. I'm committed to my own personal development and would like your help in expensing the course.   It would pay for itself if I closed only one new deal of $X value.   Please let me know by Friday if I can move forward with this 1 year course.   Thanks, ME Here are some student interviews from the courses:      ———————————————————————————————————— Audible 30 day Free Trial: http://www.audibletrial.com/BrutalTruth  

Aaron and Brian review some of the latest AI model releases and discuss how they would evaluate them through the lens of an Enterprise AI Architect. SHOW: 1003SHOW TRANSCRIPT: The Cloudcast #1003 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS" SHOW NOTES:Last Week in AI Podcast #234Artificial Analysis.AIOpus 4.6 ReleaseGPT Codex 5.3 ReleaseGLM-5 ReleaseOpenAI Preparedness FrameworkSam's Tweet that 5.3 Codex hit “high” ranking for cybersecurityFortune Article on 5.3 high rankingTAKEAWAYSThe frequency of AI model releases can lead to numbness among users.Evaluating AI models requires understanding their specific use cases and benchmarks.Enterprises must consider the compatibility and integration of new models with existing systems.Benchmarks are becoming more accessible but still require careful interpretation.The rapid pace of AI development creates challenges for enterprise adoption and integration.Companies need to be proactive in managing the versioning of AI models.The industry may need to establish clearer standards for evaluating AI performance.Efficiency and cost-effectiveness are becoming critical metrics for AI adoption.The timing of model releases can impact their market reception and user adoption.Businesses must adapt to the fast-paced changes in AI technology to remain competitive.FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

Liz Peek discusses the market's current drift and the continued dominance of Artificial Intelligence, arguing AI is not a bubble but a rapidly adopted technology transforming productivity, with companies underhiring as they assess impact and investors needing exposure to this dominant sector.1900 BRUSSELS

In today's episode of Motley Fool Money, host Emily Flippen is joined by analysts Sanmeet Deo and Dan Caplinger as each gives a stock pick they think can outperform in a “worst case” economic environment of rising inflation, lower-than-expected rate cuts, and slowing economic growth. - Dan argues that Dollar General can keep delivering value to consumers - Sanmeet introduces us to a company that is “fitting” into the mold - Emily wraps up with a pitch for a pest-control parent company Companies discussed: PLNT, DG, ROL Host: Emily Flippen, Dan Caplinger, Sanmeet Deo Producer: Anand Chokkavelu Engineer: Dan Boyd Disclosure: Advertisements are sponsored content and provided for informational purposes only. The Motley Fool and its affiliates (collectively, “TMF”) do not endorse, recommend, or verify the accuracy or completeness of the statements made within advertisements. TMF is not involved in the offer, sale, or solicitation of any securities advertised herein and makes no representations regarding the suitability, or risks associated with any investment opportunity presented. Investors should conduct their own due diligence and consult with legal, tax, and financial advisors before making any investment decisions. TMF assumes no responsibility for any losses or damages arising from this advertisement. We're committed to transparency: All personal opinions in advertisements from Fools are their own. The product advertised in this episode was loaned to TMF and was returned after a test period or the product advertised in this episode was purchased by TMF. Advertiser has paid for the sponsorship of this episode. Learn more about your ad choices. Visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠megaphone.fm/adchoices Learn more about your ad choices. Visit megaphone.fm/adchoices

Our Head of U.S. Internet Research Brian Nowak joins U.S. Small and Mid-Cap Internet Analyst Nathan Feather to explain why the future of agentic commerce is closer than you think.Read more insights from Morgan Stanley.----- Transcript -----Brian Nowak: Welcome to Thoughts on the Market. I'm Brian Nowak, Morgan Stanley's Head of U.S. Internet ResearchNathan Feather: And I'm Nathan Feather, U.S. Small and Mid-Cap Internet Analyst.Brian Nowak: Today, how AI-powered shopping assistants are set to revolutionize the e-commerce experience.It's Tuesday, February 17th at 8am in New York.Nathan, let's talk a little bit about agentic commerce. When was the last time you reordered groceries? Or bought household packaged goods? Or compared prices for items you [b]ought online and said, ‘Boy, I wish there was an easier way to do this. I wish technology could solve this for me.'Nathan Feather: Yeah. Yesterday, about 24 hours ago.Brian Nowak: Well, our work on agentic commerce shows a lot of these capabilities could be [coming] sooner than a lot of people appreciate. We believe that agentic commerce could grow to be 10 to 20 percent of overall U.S. e-commerce by 2030, and potentially add 100 to 300 basis points of overall growth to e-commerce.There are certain categories of spend we think are going to be particularly large unlocks for agentic commerce. I mentioned grocery, I mentioned household essentials. We think these are some of the items that agentic commerce is really going to drive a further digitization of over the next five years.So maybe Nathan, let's start at the very top. Our work we did together shows that 40 to 50 percent of consumers in the U.S. already use different AI tools for product research, but only a mid single digit percentage of them are actually really starting their shopping journey or buying things today. What does that gap tell you about the agentic opportunity and some of the hurdles we have to overcome to close that gap from research to actual purchasing?Nathan Feather: Well, I think what it shows is that clearly there is demand from consumers for these products. We think agentic opens up both evolutionary and revolutionary ways to shop online for consumers. But at the moment, the tools aren't fully developed and the consumer behavior isn't yet there. And so, we think it'll take time for these tools to develop. But once they do, it's clear that the consumer use case is there and you'll start to see adoption.And building on that, Brian, on the large cap side, you've done a lot of work here on how the shopping funnel itself could evolve. Traditionally discovery has flowed through search, social or direct traffic. Now we're seeing agents begin to sit in the start of the funnel acting as the gatekeeper to the transaction. For the biggest platforms with massive reach, how meaningful is that shift?Brian Nowak: It is very meaningful. And I think that this agentic shift in how people research products, price compare products, purchase products, is going to lead to even more advertis[ing] and value creation opportunity for the big social media platforms, for the big video platforms. Because essentially these big platforms that have large corpuses of users, spending a lot of time on them are going to be more important than ever for companies that want to launch new products. Companies that want to introduce their products to new customers.People that want to start new businesses entirely, it's going to be harder to reach new potential customers in an agentic world. So, I think some of these leading social and reach based video platforms are going to go up in value and you'll see more spend on those for people to build awareness around new and existing products.On this point of the products, you know, our work shows that grocery and consumer packaged goods are probably going to be one of the largest category unlocks. You know, we already know that over 50 percent of incremental e-commerce growth in the U.S. is going to come from grocery and CPG. And we think agentic is going to be a similar dynamic where grocery and CPG is going to drive a lot of agentic spend.Why do you think that is? And sort of walk us through, what has to happen in your mind for people to really pivot and start using agents to shop for their weekly grocery basket?Nathan Feather: I think one of the key things about the grocery category is it's a very high friction category online. You have to go through and select each individual ingredient you want [in] the order, ensure that you have the right brand, the right number of units, and ensure that the substitutions – when somebody actually gets to the store – are correct.And so for a user, it just takes a substantial amount of time to build a basket for online grocery. We think agentic can change that by becoming your personal digital shopper. You can say something as simple as, ‘I want to make steak tacos for dinner.' And it can add all of the ingredients you want to your order. Go from the grocery store you like. And hey, it'll know your preferences. It'll know you already like a certain brand of tortillas, and it'll add those to the cart. And so it just dramatically reduces the friction.Now, that will take time to build the tools. The tools aren't there today, but we think that can come sooner than people expect. Even over the next one to two years that you start to get this revolutionary grocery experience.And so, it's coming. And from your perspective, Brian, once agentic grocery shopping does start to work, how does that impact the broader e-commerce adoption curve? Does it pull forward agentic behavior in other categories as well?Brian Nowak: I think it does. I think it does lead to more durable multi-year, overall e-commerce growth. And potentially in some of our more bull case scenarios, we've built out – even an acceleration in e-commerce growth, even though the numbers and the dollars added are getting larger. But there is some tension around profitability.We are in a world where a lot of e-commerce companies, they generate an outsized percentage of their profit from advertising and retail media that is attached to current transactions. Agentic commerce and agents wedging themself between the consumer and these platforms potentially put some of these high-margin retail media ad dollars at risk.So talk us through some of the math that we've run on that potential risk to any of the companies that are feeding into these agents for people to shop through.Nathan Feather: Well, in our work for most e-commerce companies, a majority – or sometimes even all – of their e-commerce profitability comes from the advertising side. And so this is the key profit pool for e-commerce. To the extent that goes away, there is one potential offset here, which is the lower fee that agentic offers for companies that currently have high marketing spend. To the extent that agentic offers a lower take rate, that could be an offset.But we think it's going to be very important for companies to monitor the retail media landscape and ensure they can try to keep direct traffic as best as possible. And things like onsite agents could be really important to making sure you're staying top of mind and owning that customer relationship.Now, on the platform side, search today captures an implied take rates that are 5-10 times higher than what we're seeing in the early agentic transaction fees. If this model does shift from CPC – or cost per click – towards a more commission based model, Brian, how do you think search platforms respond?Brian Nowak: I think the punchline is the percentage of traffic and transactions that retailers or brands or companies selling their items online that's paid is going to go up. You know, while search is a relatively more expensive channel on a per transaction basis, search works because there's a very large amount of unpaid and direct traffic that retailers benefit from post the first time they spend on search.Just some math on this. We're still at a situation where 80 percent of retailers' online traffic is free. Or direct. And so if we do get into a situation where there's a transition from a higher monetizing per transaction search to a lower monetizing per transaction agent, I would expect the search platforms to react by essentially making it more challenging to get free and direct and unpaid traffic. And we'll have that transition from more transactions at a lower rate; as opposed to fewer transactions at a higher rate, which is what we have now,Nathan, in our work, we also talked about a Five I's framework. We talked about inventory, infrastructure, innovation, incrementality and income statement, sort of a retailer framework to assess positioning within the agentic transition. Maybe walk us through what your big takeaways were from the Five I's framework and what it means that retailers need to be mindful of throughout this agentic transition.Nathan Feather: Well, for retailers, I think it's going to be very important that you're winning by differentiation. Having unique, competitively priced inventory with infrastructure that can fulfill that quickly to the consumer and critically staying on the leading edge of innovation.It's one thing to have the inventory. It's another thing to be able to be actively plugged into these agentic tools and make sure you're developing good experiences for your customers that actually are on this cutting edge. In addition, it's one thing to have all of that, but you want to make sure there's also incrementality opportunity.So [the] ability to go out, expand the TAM and gain market share. And of course what we just talked about with the margin risk, I think all of those are going to be very important. And so on balance for retailers, we do see a lot of opportunity. That's balanced with a lot of risk. But this is one of those key transition moments that we think companies that really execute and perform well should be able to perform nicely.Now finally, Brian, over the next five years, how do you think agent commerce reshapes competitive dynamics across the internet ecosystem?Brian Nowak: I think over the next few years, we're going to realize that agentic commerce is no longer a fringe experiment or a concept. It's a reality. And we may get to the point where we don't even talk about agentic commerce or agentic shopping. We just say, “‘This cool thing I did through my browser.' Or, ‘Look at what my search portal can do. Look at how my search portal found me this product. Look at how my groceries got delivered.' And it'll become part of recurring life. It'll become normal.So right now we say it's agentic, it's far off. It's going to take time to develop. But I would argue that every year that goes by, it's going to be becoming more part of normal life. And we'll just say, ‘This is how I shop online.'Nathan, thanks for taking the time todayNathan Feather: It was great speaking with you, Brian.Brian Nowak: And thanks for listening. If you enjoy Thoughts on the Market, please leave us a review wherever you listen. And share the podcast with a friend or colleague today.

Why do companies with strong consumer appeal tend to outperform? The team breaks down the elements of a resilient brand and then uses that lends to discuss recent financial results from Unity Software (NYSEL U) and Zillow Group (NASDAQ: Z). Alicia Alfiere, Rick Munarriz, and Tim Beyers discuss: - The thinking behind David Gardner's fifth trait of a Rule Breaker: strong consumer appeal. - The world's most valuable brands and what makes the best brands resilient. - What fresh results from Unity Software and Zillow say about the resiliency of their brands. Don't wait! Be sure to get to your local bookstore and pick up a copy of David's Gardner's new book — Rule Breaker Investing: How to Pick the Best Stocks of the Future and Build Lasting Wealth. It's on shelves now; get it before it's gone! Companies discussed: AMZN, MSFT, AAPL, U, Z Host: Tim Beyers Guests: Alicia Alfiere, Rick Munarriz Producer: Anand Chokkavelu Engineer: Dan Boyd Disclosure: Advertisements are sponsored content and provided for informational purposes only. The Motley Fool and its affiliates (collectively, “TMF”) do not endorse, recommend, or verify the accuracy or completeness of the statements made within advertisements. TMF is not involved in the offer, sale, or solicitation of any securities advertised herein and makes no representations regarding the suitability, or risks associated with any investment opportunity presented. Investors should conduct their own due diligence and consult with legal, tax, and financial advisors before making any investment decisions. TMF assumes no responsibility for any losses or damages arising from this advertisement. We're committed to transparency: All personal opinions in advertisements from Fools are their own. The product advertised in this episode was loaned to TMF and was returned after a test period or the product advertised in this episode was purchased by TMF. Advertiser has paid for the sponsorship of this episode. Learn more about your ad choices. Visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠megaphone.fm/adchoices Learn more about your ad choices. Visit megaphone.fm/adchoices