Podcasts about b2b saas

Explore how transparency and accountability can transform PPC campaigns and client relationships. Simran Harichand shares lessons from her experiences, including a major underspend mistake that ultimately strengthened client trust.Main Topics:Accountability and transparency in PPCRebuilding client trust after budget overspendFoundational best practices in PPCEthical and effective use of AI toolsCommunication and relationship building in advertisingChapters:00:00 Welcome, introductions, and Simran's journey from Pakistan to UK paid media04:17 A €30,000 underspend on a major B2B SaaS account06:17 How a Target CPA change quietly tanked spend08:42 Owning the mistake and facing the client10:11 Rebuilding trust after a costly PPC error12:30 Why underspending can create serious business problems16:48 Breaking into PPC, internships, and hiring junior talent20:27 Why brilliant basics matter more than shiny tactics25:03 Advice for marketers who discover a major mistake26:27 Building client relationships before things go wrong28:46 The worst GA4 and conversion tracking mistakes in account audits33:20 AI Max, Performance Max, and testing new Google features37:54 Where marketers are getting AI wrong41:41 "Nobody Told Me This Was a Career"42:53 Closing thoughts and where to find SimranSimran Harichand - LinkedInPPC Live The Podcast features weekly conversations with paid search experts sharing their experiences, challenges, and triumphs in the ever-changing digital marketing landscape.Thanks to our sponsor ⁠Adsquire,⁠ a small team of passionate and focused legal marketers that do what it takes to get law firms spectacular results! With the landscape always changing they stay on top of the trends and are first to find and use new strategies to accomplish this for our clients - for example they are the FIRST to serve a lawyer ad on ChatGPT.Join the next ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠PPC Live ⁠⁠⁠eventFollow us on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow us on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Twitter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Slack Group⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe to our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Newsletter⁠⁠

What does it actually take to build a go-to-market strategy for a category that barely existed 18 months ago?In this episode of The Growth Leaders Series, Charlie Marchant sits down with Nick Lafferty, Founding Marketing Engineer at Profound, the AI Search tracking platform helping major brands understand how they show up in ChatGPT, Gemini, Perplexity, and other LLMs.Nick brings serious growth experience to this role. Before Profound, he drove millions in B2B SaaS pipeline at Loom and Mailgun, then spent two years running a solo consulting agency before joining Profound. In this episode, Nick Lafferty covers:Why velocity is a moat in AI SearchWhat a modern, lean marketing team actually looks like, why Nick hires for a generative marketer mindset, and his advice for showcasing this online The growth strategy behind Profound, centred on sharing data and insightsThe go-to-market motion behind Profound's Zero Click events, scaling from 400 to 800+ attendees across New York and LondonThe layered mentality around AI Search for different business sizesWhy FAQ content and FAQ schema is the lowest-hanging fruit most big brands are leaving on the tableHow to make the internal case for AI Search investment when leadership is still thinking in Google termsThe career advice he'd send back to his first dayRead the full show notes: https://exposureninja.com/podcast/growth-leader-series-nick-lafferty/Follow Nick Lafferty on LinkedIn: https://www.linkedin.com/in/nicklafferty/New episode launches every Wednesday throughout June 2026, so stay tuned to hear from growth leaders from leading brands like McKinsey and Company, Wise, and AirOps! Book a consultation to get a live review of your website and marketing

In this episode, recorded out in the New Mexico desert at ChiliPalooza, Jordan Crawford makes a blunt case to B2B SaaS: the methodologies you built your career on are about to age out, and the only way through is to get your hands on Claude Code.Jordan's spent his whole job lately doing one thing: teaching clients to work with AI. And what he's found cuts against almost everything sales and marketing teams currently do.What this episode covers:Why the constraint on building things isn't budget or headcount anymore, it's imaginationThe SDR question every revenue leader is asking today: we went all-in, we see the volume, and we don't know what's working...so now what?How Jordan rebuilds prospecting strategies from what customers actually did, not what a rep thinks they wantWhy being wrong fast and cheap beats being right slowly: "you can beat any grandmaster if you get two moves to their one"The truth about a sloppier world, and why polish is no longer the pointWhy the gap between people who are great at this and people who are bad at it comes down to how you think, not skillWhy the "graybeards" built on ten-year-old playbooks are going away, and what replaces themThe people who get in the tool will build things the graybeards can't imagine. The ones who don't will spend the next few years explaining a methodology nobody's buying.-----------------------------------------------------

"A CRO told us the word our customers cared about most was extensibility. I'm a marketer — I've never used that word in my life."When was the last time marketing had a real say in what got built? In most B2B SaaS companies, engineers and founders own the product, and marketers inherit whatever comes out the other end. Matt Sciannella and Liam Moroney explore what it would look like for marketers to genuinely influence product direction — not by taking over, but by asking the questions nobody else is asking. Keywords: B2B marketing strategy, product marketing, market research, customer discovery, SaaS go-to-market.

Most founders think they have a sales problem. According to Lou Shipley, they usually have a customer understanding problem.Lou is a 3x CEO, Senior Lecturer at Harvard Business School, former CEO of Black Duck Software, and co-author of Unlikely Entrepreneurs.During his time at Black Duck, Lou repositioned the company from open-source compliance to open-source security, quadrupled revenue, and helped lead the company to a $565 million acquisition by Synopsys.In this conversation, we discuss: Why founders should not hand off sales too early  The real purpose of your first 100 customer conversations  How to know if you're solving a painful enough problem  Why competitive markets can be better than new markets  The go-to-market framework that helped scale Black Duck  How to identify product-market fit before building too much  What causes churn and how to spot it before it happens  Why most founders misunderstand scaling a sales team  The reality of AI and what founders should pay attention to  Lessons from six startups, multiple exits, and decades of leadership This is a practical conversation about sales, positioning, product-market fit, scaling teams, and building companies that customers actually want.00:00 Introduction to Lou Shipley and Black Duck Software02:00 The Black Duck acquisition story and repositioning strategy04:30 Why founders should own sales longer than they think09:10 Learning from customers before chasing revenue12:00 Why competitive markets are often better opportunities15:00 The myth of the young founder and why experience matters18:40 Understanding customer pain deeply enough to build a company21:20 Signs you're building a solution nobody truly needs22:45 Building software for yourself vs guessing what customers want25:00 How Lou repositioned Black Duck around security27:30 Managing vs leading as your company scales31:00 Escaping the weeds and thinking like an investor33:10 The sales framework behind Black Duck's growth39:00 Churn, product-market fit, and customer retention43:30 AI, software startups, and what founders should watch51:30 What Lou learned after running multiple companies57:20 The one message every founder needs to hearUnlikely Entrepreneurs: Wins, Losses, and Crucial Lessons on Building Great Companies: https://a.co/d/0fPfhi1D

In this episode, we're joined by George Storm, CRO at N.rich, for a conversation about why traditional B2B SaaS forecasting is no longer good enough in today's market. George shares how N.rich, the European ABM platform, helps sales-led companies influence complex buying committees, warm up priority accounts, and progress accounts before sales ever reaches out.  We spoke with George about why forecasting can't be treated as a static quarterly exercise anymore, why revenue leaders need to account for macro signals like layoffs, budget freezes, acquisitions, interest rates, and market turbulence, and how to move from fixed-number forecasting to ranges, probabilities, and continuous forecast loops. He explains why CROs should think in “regimes” like calm, turbulent, and stormy markets, and how that changes the way you model win rates, sales cycles, ACV, and pipeline coverage. Here are some of the key questions we address: Why is traditional SaaS forecasting broken? Why should forecasts be modeled as ranges instead of fixed numbers? How do macro signals like layoffs, acquisitions, and budget freezes impact pipeline confidence? Why can historical win rates be misleading in today's market? What does it mean to forecast in calm, turbulent, or stormy weather? How can CROs build a continuous forecasting loop instead of relying on quarterly updates? What should revenue leaders monitor weekly to avoid surprise misses?

Host: Annik Sobing Guest: Niki McKinnell Published: May 2026 Length: ~22 minutes Presented by: Global Training Center Niki McKinnell on Sales, Marketing, and the Story Behind Supply Chain Growth Annik Sobing welcomes Niki McKinnell to the Simply Trade Roundup for a conversation about what happens when sales and marketing break down in B2B SaaS supply chain companies. Niki shares how her career began in public sector communications and crisis press offices, how she learned to build a story with limited resources, and how that foundation shaped the way she approaches marketing, messaging, and go-to-market strategy today. What You'll Learn in This Episode How Niki built a career around storytelling Niki explains how her path started in government communications, where she worked in press offices and crisis environments. She talks about how those early experiences taught her to think strategically about messaging, audience, and impact. Why sales and marketing break down The episode explores the most common reasons sales and marketing teams lose alignment in supply chain SaaS companies. Niki describes how different definitions, assumptions, and metrics can create friction even when everyone is working toward the same goal. What makes supply chain different Niki breaks down why supply chain has its own flavor when it comes to go-to-market strategy. Buyers are focused on their operations, not your product, which means credibility, timing, and intentional messaging matter more than ever. How to bring teams back into alignment One of the most useful parts of the conversation is Niki's framework for stronger execution: alignment, coordination, and visibility. She explains how teams can work more intentionally before, during, and after GTM activity so they are moving with the same goals in mind. Why long sales cycles need a different approach Niki and Annik discuss how complex buying committees, long sales cycles, and deeply rooted habits make this industry especially challenging. Niki shares how companies need to adapt their strategy to meet buyers where they are. What to do when pipeline stalls Niki offers advice for founders and leaders who are struggling with pipeline. Her recommendation is to focus on the brand, demand, expand framework, with brand awareness, demand generation, and customer growth all working together to support revenue. Who this episode is for This episode is especially valuable for marketing leaders, sales teams, founders, and GTM professionals working in supply chain or B2B SaaS. It is also a great listen for anyone trying to understand how strategy, communication, and alignment shape growth in a complex industry. This podcast is presented by Global Training Center.  Subscribe & Follow Stay connected with the Simply Trade community and never miss an episode that helps you trade smarter.

From years in the SEO trenches, today's guest knows what it takes to run successful strategies. Adrian Dahlin is the Founder & CEO of Search to Sale, an SEO analytics SaaS company providing automatic content intelligence for B2B, SaaS and marketing agencies.Adrian Dahlin is the Founder & CEO of Search to Sale, an SEO analytics SaaS company providing automatic content intelligence for B2B SaaS and marketing agencies. He began his entrepreneurial journey in 2020 after leaving corporate marketing to launch a startup consultancy, later evolving it into Search to Sale in 2023. Previously, Adrian worked in data science and marketing analytics after earning a Master's in Applied Data Science from NYU, and earlier in his career founded and led sustainability-focused ventures. CONTACT DETAILS:Email: gerardo@searchtosale.io Business: Search to SaleWebsite: https://www.searchtosale.io/ Social Media:LinkedIN: https://www.linkedin.com/in/adriandahlin/ LinkedIN Company: https://www.linkedin.com/company/search-to-sale-seo-revenue-generation-software/ Remember to SUBSCRIBE so you don't miss "Information That You Can Use." Share Just Minding My Business with your family, friends, and colleagues. Engage with us by leaving a review or comment. https://g.page/r/CVKSq-IsFaY9EBM/review Your support keeps this podcast going and growing.Visit Just Minding My Business Media™ LLC at https://jmmbmediallc.com/ to learn how we can help you get more visibility on your products and services. 

Send us Fan MailIn this episode of Sidecar Sync, Amith Nagarajan and Mallory Mejias unpack Google I/O 2026 and what it signals for the future of AI-powered work, search, and member engagement. They explore Google's push toward proactive, agentic AI across Gemini, Workspace, Search, and new infrastructure like Antigravity and TPU chips, while digging into what these changes mean for associations trying to protect their content, improve digital experiences, and stay relevant as members increasingly expect voice, multimodal interaction, intelligent search, and personalized service. The conversation also covers AI's impact on career advice, leadership, web traffic, SEO, smart glasses, privacy, and why associations may need to double down on trust, niche expertise, and human connection in an increasingly agent-driven world.

Dan Balcauski is the founder of Product Tranquility, where he helps B2B SaaS companies improve pricing, packaging, and monetization strategy.  In this episode, Dan breaks down the uncomfortable reality behind today's AI gold rush: buyers are tired of "AI-powered" hype, SaaS companies are struggling to monetize features nobody uses, and pricing teams are rewriting their strategies in real time. If your company is trying to monetize AI without becoming another forgettable AI feature, this episode will change how you think about pricing, adoption, and customer value.   Why You Have to Listen: Learn why AI features alone don't drive purchases — and how to position AI around customer outcomes people actually value. Discover the biggest AI pricing mistake SaaS companies are making — charging for features before customers build adoption habits. See how smart SaaS companies roll out AI strategically — using adoption-first pricing, early access models, and workflow-driven product design.   "Prove value with your new AI features before you throw a paywall in front of it." — Dan Balcauski    Topics Covered: 02:10 - "Freemium Is a Terrible Idea for Most SaaS Companies". Why most freemium models fail before companies fully understand the real costs behind them. 06:48 - Why AI Can't Automatically Set Your SaaS Prices. Dan explains where AI can help pricing teams and where human judgment still matters most. 09:53 - The Dangerous Truth About AI Pricing Advice. Most LLMs learned pricing strategy from bad SEO content and outdated thinking. 13:35 - The Adoption vs. Monetization Framework. The simple 2x2 model every SaaS company should use before pricing AI features. 17:34 - Margin Percentage vs. Margin Dollars. A smarter way for CFOs and SaaS leaders to think about AI profitability. 18:32 - "Buyers Don't Care That Your Product Uses AI". Why customers care more about outcomes and workflows than your AI technology. 24:31 - Why SaaS Companies Keep Changing AI Pricing. Most AI pricing models don't survive their first 18 months. 26:07 - The "Early Access" AI Pricing Strategy. How smart SaaS companies introduce AI features without hurting adoption. 29:24 - "Earn the Right to Monetize". Why proving customer value should happen before putting up a paywall.   Key Takeaways: "We need to prove our value first before we can monetize it." – Dan Balcauski   People / Resources Mentioned: Steven Forth — Mentioned as a trusted source of pricing expertise and strategic thinking. Anthropic Claude Code — Dan's primary AI workspace for research synthesis and pricing analysis. Readwise — Tool Dan uses to ground AI outputs using trusted expert highlights and notes. Salesforce — Referenced as an example of rapidly evolving AI pricing strategies. Pragmatic Institute — Mentioned during the discussion on product adoption and feature prioritization.   Connect with Dan Balcauski: Website: https://www.producttranquility.com/ LinkedIn: https://www.linkedin.com/in/balcauski/ X: https://x.com/dan_balcauski Podcast: https://podcasts.apple.com/us/podcast/saas-scaling-secrets/id1682338188   Connect with Mark Stiving: LinkedIn: https://www.linkedin.com/in/stiving/ Email: mark@impactpricing.com  

Most founders treat 'scale' like a switch you flip after raising a round: hire 14 reps, 10x the ad spend, and pray. About half scale too early and burn the runway, while the other half scale too late and get caught by a more aggressive competitor. Almost nobody can tell you, in measurable terms, when they're actually ready.In this episode, Yaniv Bernstein is joined by Mark Roberge - founding CRO at HubSpot (where he scaled the company from $0 to $100M ARR), senior lecturer at Harvard Business School, cofounder of Stage 2 Capital, and author of the new book 'The Science of Scaling'. Mark walks Yaniv through his impressive data-driven framework for scaling that he's spent a decade refining, covering how to objectively define product-market fit, why customer retention is the only honest measure of PMF, and how to instrument a Leading Indicator of Retention you can act on in week one.In this episode, you will:Learn why retention is the only honest measure of product-market fit, and why most founders are flying blind without itDiscover Mark's framework for building a Leading Indicator of Retention (LIR) you can measure in week one, using Slack, HubSpot, and Facebook as worked examplesHear Mark coach Yaniv through Vera's LIR in real time, and pick up a repeatable method for designing one for your own businessLearn the 'Stay/Go/Slow' model for pacing hires and spend post-raise, and why startups should reassess monthly or quarterly rather than locking in an annual planGet Mark's take on why 'paranoid optimism' is the trait that correlates most strongly with founder success, and the link between that trait and founder mental healthTimestamps00:00 Coming Up00:26 On Today's Show: The Science of Scaling01:47 Guest Intro: Mark Roberge02:31 Why Scaling Needs Data04:20 Eric Ries and Product Market Fit06:56 Retention as a North Star10:15 What Makes a Good Leading Indicator?15:00 Case Study: Vera (Yaniv's Startup)17:41 Choosing Frequency and Event23:55 Instrumenting and Unique Value31:12 Blitzscaling and Defining PET34:41 ICP Denominator Rules37:28 Segmenting By Product40:40 Go To Market Fit45:25 Dealing with Revenue-Focused Investor Pressure50:33 The Pace of Scaling56:07 About the Book, The Science of Scaling57:45 Founder Mental Health01:02:28 Closing ThoughtsResources in this episode:Mark Roberge on LinkedIn: https://www.linkedin.com/in/markroberge/‘The Science of Scaling: Using Data to Decide When — and How Fast — to Scale Revenue' by Mark Roberge: https://www.amazon.com/Science-Scaling-Revenue-Mark-Roberge/dp/1394319428Stage 2 Capital (Mark's B2B SaaS-focused venture firm): https://www.stage2.capital/Vera (Yaniv's startup): https://vera.guide/The PactHonor the Startup Podcast Pact! If you have listened to TSP and gotten value from it, please:Follow, rate, and review us in your listening appFollow us on YouTube for full-video episodes: https://www.youtube.com/@startup-podcastGive us a public shout-out on LinkedIn or anywhere you have a social media followingKey linksThis episode of the Startup Podcast is sponsored by .tech domains. Forget weird prefixes and creative misspellings; the availability for .tech domains is simply way better than .com. For a clean and memorable name, go to https://⁠get.tech/tspThis episode of the Startup Podcast is sponsored by Vanta. Vanta helps businesses get and stay compliant by automating up to 90% of the work for the most in demand compliance frameworks. With over 200 integrations, you can easily monitor and secure the tools your business relies on. For a limited time offer of US$1,000 off, go to ⁠⁠⁠⁠https://⁠www.vanta.com/tsp⁠⁠⁠⁠⁠ The Startup Podcast website: https://www.tsp.show/episodes/Follow Yaniv on Linkedin: https://www.linkedin.com/in/ybernstein/Producer: Justin McArthur https://www.linkedin.com/in/justin-mcarthurAssistant Producer: Steph Hefferan https://www.linkedin.com/in/steph-heff/Intro Voice: Jeremiah Owyang https://web-strategist.com/

Consumption pricing puts pressure on the forecast in places traditional SaaS models rarely exposed. Total usage may be easier to model from the CFO's seat, but the field still has to answer harder questions: which customer, which channel, which rep, and when. In this replay segment, Devavrat Shah explains how AI can help teams learn across cohorts, spot patterns in uneven data, and create more trust in a forecast that would otherwise depend on isolated judgment calls.  Devavrat Shah is an MIT professor, director of MIT's Statistics and Data Science Center, and co-founder and CEO of Ikigai Labs. He brings a data science and operator's perspective to forecasting, consumption pricing, and enterprise AI. Connect with Devavrat: LinkedIn Listen to the full episode here: Understanding AI Through History and Practical Application with Devavrat Shah Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

Get the 3 prompts (research + classify, gift ideas, note writer) in my paid newsletter here.---In this episode, I break down the 2-step ABM gifting system you can run on your top 200 accounts. How to find the clue. How to classify the prospect into one of four buckets (Identity, Passion, Milestone, or No Gift). How to pick a gift that maps to the specific niche. And how to write the note that proves the research wasn't AI-generated.--If you're new here, I'm Elric Legloire, founder of Outbound Kitchen. I help B2B SaaS companies between $2M and $50M ARR fix and scale their outbound system. My view: in 2026, productivity is the multiplier to scale outbound teams.Menu:- Why a $300 researched gift is cheaper than the cold email sequence you'd run into a $200K to $1M ARR account- The 4-bucket prospect classification: Identity, Passion, Milestone, No Gift (and why ~70% land in No Gift)- The specificity ladder: vague vs. niche vs. Passion vs. Signature clues, and which two qualify for a gift- Where to find Signature clues: LinkedIn About sections, podcast appearances, keynotes, blogs, book forewords- A worked example on Kyle Norton (CRO at Owner.com). From clue ("former MMA gym co-owner, black belt") to gift (personalized oak belt display from Etsy with his "Slow is smooth. Smooth is fast." quote)- The 5-part note template: research proof, rabbit hole, gift bridge, pitch + proof, soft ask- Why Perplexity beat ChatGPT for clue research this round, and why you should keep benchmarking AI modelsReferenced:- Stevie Case (CRO, Vanta), Quake rocket launcher gift, sent by Brennan- Tom (CMO, Incident.io), signed vinyl gift- Newsletter with the 3 prompts (research + classify, gift ideas, note writer): https://newsletter.outbound.kitchen/p/abm-how-to-gift-your-top-200-accountsChapters(00:00) Why Gifting Works(01:37) ABM Outbound Fit(02:41) Step One Find Clues(03:29) Clue Buckets Framework(04:41) Avoid Creepy Research(05:38) Make Clues Specific(06:55) Where To Research(07:17) Kyle Norton Example(09:51) Decision Tree Choices(10:40) Step Two Gift Ideas(11:56) Personalized Gift Build(13:24) Write The Note(14:58) Note Template--When you're ready⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Want to work with me? Send me a DM⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ---Connect with me⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Send us Fan MailIn this episode of Sidecar Sync, Amith Nagarajan and Mallory Mejias dig into two major shifts happening beneath the surface of AI: how enterprise software vendors are responding to the rise of AI agents, and why diffusion language models may be moving from research curiosity to real-world infrastructure faster than expected. They unpack Salesforce's open, agent-friendly “Headless 360” strategy, SAP's more restrictive API stance, and what these moves mean for associations trying to maintain control over their data. Then, they revisit diffusion LLMs through the lens of Inception Labs' Mercury 2, exploring why faster, cheaper models could matter for voice agents, enterprise search, taxonomy work, content classification, and the future of model flexibility.  

Side hustles used to be the vibe… but are they still “allowed” now?

For Mother's Day, Tye DeGrange hands the mic to his mom — Ginger DeGrange. Rodeo queen. Horse trainer. Summer camp founder. 36-year instructor who's taught 10,000+ students the art of horsemanship at Santa Rosa Junior College. This one's full of great stories: the OJ Simpson trail ride, a student who went on to dine with the Queen of England, competing at the Grand National, and the old reinsman who taught Ginger that quiet confidence beats loud energy every time. Plus real lessons on building confidence, earning trust, and leading with feel — on and off the horse.

Andrej Persolja built a product with a 4.9-star rating and real clinical proof it worked. He launched in one market and it took off. He then launched to the US and nothing happened. No customers. No conversions.Four years and tens of thousands in ad spend with almost nothing to show for it. He eventually figured out why. Then he ran a structured test. One thing changed. Revenue went up 200%. His cost to acquire a customer dropped by more than half. He left the startup and built a consulting practice around what he learned.He then went on to take another company from $200K to $2M in annual revenue in five months. And in this episode we cover the startup growth playbook, From research to market positioning to client acquisition.This conversation covers:what he learned about why products stop sellinghow he identifies growth levers most teams misswhat customer research actually looks like when it worksand what he did when things were at their worstEnjoy!

This week's throwback episode guest studied Sociology, Cultural Relations and Global Politics at University of Montana before taking the jump into B2B Sales and Marketing where she has spent most of her career. She has been a Sales Director, Head of Sales, Employee #1 to CRO all leading up to the work she does now as the Founder of Sales-Led GTM Agency. At Sales-Led GTM Agency, she focuses on building the outbound sales strategy, processes & skill sets your sales-led organization needs to thrive, and provides B2B Sales Training & GTM Consulting for B2B SaaS & Service orgs between 15 - 50 M in annual revenue. Last time we spoke, she just left corporate, but since then has been building in public, and now we are 2 years in and will be talking about her journey today! Please join me in welcoming Leslie Venetz to The 20% Podcast. In this week's episode, we discussed:Trusting Your Gut Why Become An EntrepreneurA New Wave of EntrepreneursGet Clear On WorkGetting Specific With Your AsksMuch MorePlease enjoy this week's episode with Leslie VenetzI am now in the early stages of writing my first book! It will cover my journey into sales, the lessons learned, and include stories and advice from top sales professionals around the world. I'm excited to share these interviews and bring you along on this journey!Like the show? Subscribe to the email: Subscribe HereI want your feedback! Reach out at 20percentpodcastquestions@gmail.com or connect with me on LinkedIn.If you know anyone who would benefit from this show, please share it! If you have suggestions for guests, let me know!Enjoy the show!

Stuart Clout founded Thedocyard at 3am on the floor of a Sydney law firm boardroom, listed it on the ASX the week before COVID locked down Australia, merged with Ansarada, and rode the combined business through to a $236m acquisition by Datasite. He joins Don and Aaron to unpack twelve years of B2B SaaS lessons, the deal mechanics, and what he wishes he had known sooner. Intro and origin story The 3am post-it note moment and the birth of Closing Rooms Top three lessons: founder problem fit, demos vs sales, the Mom Test The Gail Goodman talk that kept Stuart in the game $500 of coffees: the lead-gen tactic that beat everything else Anti-sales is the new sales Apathy, durability and becoming a verb The 2020 ASX listing one week before COVID The Ansarada merger: how the deal got done The Datasite acquisition: $236m, $2.50 per share, nine months of process The ACCC and policy frustrations Where Australian B2B SaaS should focus Founder mindset, fitness, family and "you can have everything, just not at the same time" Find Stuart hello@tribeglobal.vc   

Dave "CAC" Kellogg and Ray "Growth" Rike dig into the Redpoint Ventures 2026 Software and AI Market Update - a 69-page report built on proprietary CIO survey data from 141 respondents, plus public market data from Qatalyst, Pitchbook, Goldman Sachs, RBC, and McKinsey. Big report with even bigger implications. Ray and Dave unpack the data that matter most for B2B SaaS and AI-native software operators.WHAT WE COVER IN THIS EPISODEThe AI Build-Out Is Real and It's Not the Dot-Com BubbleHyperscaler CapEx is projected to hit $765B in 2026, up nearly 50% year over year. More than 90% of new data center capacity is already pre-committed. Compare that to the dot-com era when fiber utilization was under 3%. The other critical difference: today's infrastructure spend is funded primarily by free cash flow, not debt. The more important signal is demand. AI has reached 1 billion monthly active users in four years. The internet took far longer to reach 70 million. The demand is real. The risk of speculative overbuild is also real.The Agent Maturity Curve and Why Most of the Value Is Still AheadPage 7 of the report maps the four phases of agent maturity by runtime: co-pilots (seconds), task agents (minutes), workflow agents (hours), autonomous agents (days). Co-pilots represent roughly $500B in software spend. Task agents, where coding tools live today, push that to $1.2T. Workflow agents expand the TAM to $2.8T. Autonomous agents take it to $6.1T. Coding has been the beachhead use case for good reasons: structured training data, instant verification, self-improving feedback loops. The real enterprise revenue opportunity is still in phases three and four.What the CIO Survey Actually Says This is the buried lead of the report. 54% of CIOs are actively consolidating vendors. 45% of AI budgets are coming from existing software budgets, not net-new spend. 58% say AI feature additions are the top driver of incremental software spend. 54% prefer to stay with incumbent vendors if they deliver on AI. Only 13% have a strong preference for AI-native software. The 33% who are neutral are the swing vote. Incumbents are winning the preference battle but losing the execution battle — the CIO feedback on Agentforce, Copilot, and ServiceNow AI in the survey is not flattering.Terminal Value Is the Real SaaS Valuation StoryThe public SaaS median NTM revenue multiple sits at 4.1x (Meritech says 3.1x), the lowest since the global financial crisis. In a SaaS DCF, 85 to 95% of enterprise value comes from terminal value, not the five-year forecast. The implied long-term growth rate embedded in current SaaS valuations has collapsed from 4.7% to 1.1%. Short-term beats like ServiceNow's recent quarter do almost nothing to move the stock because the market's concern is not next year. It's year ten and beyond. That is a terminal value story, not a growth story.ARR Per Employee - The Benchmark EvolvesCursor and Anthropic hit $100M ARR in roughly two years. Slack took three. Salesforce and Adobe took four to five. ServiceNow took seven to eight. AI-native companies have made $1M revenue per FTE the new floor. The P&L transformation model in slide 39 projects R&D costs down 15 to 20%, sales costs down 15 to 20%, COGS increasing due to inference spend but offset by reductions in customer support and customer success. Net result: potential EBITDA expansion of 100 to 250% on the same revenue base over three to five years.Private Markets Are in an AI Love FestAI-native deals represent nearly 100% of new VC activity in Q1 2026. Deal concentration is accelerating: the top 20 deals captured 44% of total funding in 2025, up from 31% in 2024 and 7% in 2022. At the model layer, dollars and valuations are concentrated while deal volume belongs to the application layer (61% of deals). The model competition is effectively over. The only question is rank order. The application layer is where the volume plays out, and AI-native vendors are winning that battle.Redpoint 2026 Software and AI Market Update: https://www.redpoint.com/reports/2026-market-updateABOUT THE METRICS BROTHERS Ray Rike is the Founder and CEO of Benchmarkit, the leading B2B SaaS and AI-native software benchmarking company. Dave Kellogg is an EIR at Balderton Capital, independent consultant, and author of Kellblog. Together they bring a CFO-meets-GTM lens to the metrics and benchmarks that drive efficient revenue growth and enterprise value.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Is the Marketing Qualified Lead (MQL) dead, or are marketing teams stuck in a cycle of high-volume, low-return efforts?Marketing economics have undergone significant structural shifts in recent years. With the disappearance of global labor arbitrage and the rise of AI-generated content, the costs of customer acquisition and inbound marketing have skyrocketed. Because of these changes, the once-dominant metric of the MQL is rapidly losing its relevance in today's B2B SaaS environment.In this episode of B2B SaaS Marketing Snacks, Brian Graf, Executive CMO of Kalungi, sits down with Stijn Hendrikse, Kalungi's co-founder and ex-Microsoft product marketing leader, to unpack the risks of over-reliance on MQLs. They talk through why marketing teams can no longer win on sheer quantity and speed alone, and how AI and globalization have completely changed the playing field.You'll hear why focusing on "big plays"—low volume, high depth strategies like flagship events or deep partnerships—is key to sustainable growth. Brian and Stijn also detail practical frameworks for shifting away from the high-volume "MQL trap" and moving toward metrics that actually matter: pipeline value and signal-to-noise ratio. By focusing on these deeper, quality-led strategies, marketing teams can flatten the problem of labor arbitrage and AI ubiquity.In this podcast, you'll learn:Why the once-dominant MQL is losing its relevance in the B2B SaaS environment.How the end of global labor arbitrage and the rise of AI have heavily inflated marketing and customer acquisition costs.The dangers of the "MQL trap," where teams are forced to execute high-volume, high-depth campaigns with diminishing returns.Why shifting to "big plays"—low volume, high depth strategies—is the key to sustainable growth.How to transition your tracking from MQLs to measuring the actual dollar value created in your pipeline.The importance of structuring a leaner marketing team that focuses on signal-to-noise ratio and quality-led strategies.By the end, you'll have a clearer view of why the old inbound playbooks are failing and how to build a quality-led, pipeline-focused go-to-market strategy that cuts through the noise.Chapters:00:00 The Death of the MQL08:20 Shifts in Marketing Economics15:29 The Big Play Quadrant20:49 New Metrics for Success25:44 Team Dynamics and Marketing CostsABOUT B2B SAAS MARKETING SNACKSSince 2020, The B2B SaaS Marketing Snacks Podcast has offered software company founders, investors and leadership a fresh source of insights into building a complete and efficient engine for growth.Meet our Marketing Snacks Podcast Hosts:  Stijn Hendrikse: Author of T2D3 Masterclass & Book, Founder of KalungiAs a serial entrepreneur and marketing leader, Stijn has contributed to the success of 20+ startups as a C-level executive, including Chief Revenue Officer of Acumatica, CEO of MightyCall, a SaaS contact center solution, and leading the initial global Go-to-Market for Atera, a B2B SaaS Unicorn. Before focusing on startups, Stijn led global SMB Marketing and B2B Product Marketing for Microsoft's Office platform.Brian Graf: Executive CMO at KalungiAs a CMO at Kalungi, Brian provides high-level strategy, tactical execution, and business leadership expertise to drive long-term growth for B2B SaaS. Brian has successfully led clients in all aspects of marketing growth, from positioning and messaging to event support, product announcements, and channel-spend optimizations, generating qualified leads and brand awareness for clients while prioritizing ROI. Before Kalungi, Brian worked in television advertising, specializing in business intelligence and campaign optimization, and earned his MBA at the University of Washington's Foster School of Business with a focus in finance and marketing. Visit Kalungi.com to learn more about growing your B2B SaaS company.

Send us Fan MailIn this episode of Sidecar Sync, Amith Nagarajan and Mallory Mejias dig into the hidden world of “dark databases”—messy, undocumented systems quietly powering many associations—and explore DBAutoDoc, a new open-source tool from Amith and Thomas Altman that uses AI to map database structure, infer relationships, and generate documentation at scale. Amith & Mallory also unpack the state of AI video after OpenAI's Sora shutdown, explaining why the category is not cooling off, which models are leading the race, and where associations might find real value in video generation, personalized learning, website assistants, and member communications. Along the way, they touch on data readiness, AI audio versus video, governance, privacy, and why focus may be the most important leadership skill in the AI era.

In this podcast episode, Diane Wiredu, Founder and Messaging Strategist for Lion Works, underscores the significance of this key element. Diane breaks down a step by step guide on effective messaging, while also providing insights on engaging customers and growing products.Our HostsLily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She's currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. She's worked on a diverse range of products – leading the product teams through discovery, prototyping, testing and delivery. Lily also founded ProductTank Bristol and runs ProductCamp in Bristol and Bath.Randy Silver is a Leadership & Product Coach and Consultant. He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury's. He participated in Silicon Valley Product Group's Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs (CPO Circles), Product Managers (Product In the {A}ether) and Product Coaches. He's the author of What Do We Do Now? A Product Manager's Guide to Strategy in the Time of COVID-19. A recovering music journalist and editor, Randy also launched Amazon's music stores in the US & UK.

Stage 2 Capital General Partner Liz Christo joins the show to discuss the disconnect between venture expectations and reality in the software market. The conversation covers the hidden costs of the new build versus buy debate, the structural changes happening within modern sales organizations, and whether traditional B2B SaaS go-to-market strategies and moats still matter when AI coding tools make software replication cheaper than ever. Key Takeaways: -The shift toward building internal AI tools instead of buying SaaS products overlooks long-term technical debt, as Liz Christo points out that "there's like a huge amount of cost buried behind the scenes that we're not really talking about today because it's still like sexy and fun." -Founders are artificially inflating their Total Addressable Market to meet new venture capital baseline expectations, with Liz Christo noting that "pitch decks read like really ridiculous right now where everybody wants to tell the story of like a $10 billion outcome because that's the new milestone that got set." -Revenue Operations is becoming the most direct path to the Chief Revenue Officer seat in AI-first organizations, which Sam Jacobs explains is "because as we use fewer humans and more agents, the sort of the half technical, the semi-technical capabilities of most RevOps people will translate into orchestrating armies of agents." -Delegating analysis and writing to AI risks destroying strategic judgment across go-to-market teams, a trend Liz Christo summarizes by stating, "I think we are producing an incredible amount of content that's not getting consumed... I just think we're like losing the ability to think and we're not teaching junior employees how to do it." Connect with the Hosts & Guests: Host: Sam Jacobs - https://www.linkedin.com/in/samfjacobs/  Host: AJ Bruno - https://www.linkedin.com/in/ajbruno3/  Host: Asad Zaman - https://www.linkedin.com/in/azaman1/  Guest: Liz Christo - https://www.linkedin.com/in/lizchristo/   Topline is more than a YouTube Channel: Subscribe to Topline Newsletter: https://toplinemedia.substack.com/ Tune into Topline Podcast, the #1 podcast for founders, operators, and investors in B2B tech: https://www.joinpavilion.com/topline-podcast Join the free Topline Slack channel to connect with 600+ revenue leaders to keep the conversation going beyond the podcast: https://www.joinpavilion.com/topline-slack   Chapters:  00:00 Intro and Cold Open 02:41 The New Build vs Buy Debate 06:10 Engineers in Every Department 10:48 Pitch Decks and 10B Dollar TAMs 17:53 Venture Capital Funding Quiz 23:43 AI Memos and Critical Thinking 42:41 Software Moats and Switching Costs 47:46 Bulls vs Bears Segment 48:23 RevOps as a Path to CRO 51:25 The Future of SDR Managers 55:14 Is Clay Actually Undervalued 59:12 Odds of Hitting 50M ARR  

With Sohrab Hosseini (Orq.ai) Season #13 Episode #2 | #Marketing_021 Der Podcast über Marketing, Vertrieb, Entrepreneurship und Startups *** www.orq.ai/ www.linkedin.com/in/sohrabhosseini/ *** In the "Marketing From Zero To One" podcast, Sohrab Hosseini, co-founder of Orq.ai, talks about his personal founder journey, from his first startups and a difficult bankruptcy experience to building a new AI infrastructure company in Amsterdam. He explains how Orq.ai helps companies build, manage, and control AI applications and agents on one platform instead of using many disconnected tools. A central topic of the conversation is how the company adapted from prompt management to full agent lifecycle management while working with clients across many industries. The interview also covers fundraising, public speaking, the differences between Europe and the U.S. startup environment, and Sohrab's advice for future founders, and students. *** 01:19 – Sohrab's background and founder journey 03:37 – Personal lessons 05:55 – McKinsey, Tech transformation, and path to Orq.ai 07:28 – From business rules to AI agent infrastructure 08:29 – Early stage of Orq.ai and founding context 10:01 – How past failure shaped later decisions 10:37 – Meeting his co-founder Anthony 11:13 – Starting with uncertainty 12:37 – First product and first customers 14:21 – Customer problem and need for an AI platform 14:53 – How Orq.ai works across models and providers 17:20 – Horizontal platform, different use cases 18:44 – Sales, GTM, and product-led growth 21:26 – Clients, markets, and international reach 21:53 – Pricing logic and usage-based model 24:56 – Public speaking and founder visibility 26:43 – Recent U.S. trip and differences between EU & US 30:30 – Fundraising and the 5 million seed round 32:43 – Why Orq.ai is different from niche AI startups 33:39 – Product experience and developer adoption 36:50 – Core value proposition of the platform 40:17 – Agent trend, fast market changes, and product direction 43:32 – Typical use cases and regulated industries 45:45 – Europe, sovereignty, and strategic advantages 46:53 – Agents, SaaS change, and new user behavior 48:59 – Current product focus and next developments 50:54 – Advice for European AI infrastructure founders 53:37 – Tips for students who want to start a company

Does AI really break B2B positioning, or is it exposing deeper product problems? In this roundtable, Refine Labs' VP of Innovation Matt Sciannella sits down with Fletch PMM founders Anthony Pierri and Rob Kaminski to unpack what's actually happening when companies try to position themselves for the AI era.They cover why AI mandates from VCs create confusion (not clarity), how Intercom, Palantir, Salesforce, and Owner.com handle multi-product positioning, and why delegating positioning to LLMs is a race to mediocrity.What is product positioning in B2B SaaS?Product positioning defines who your product is for, what problem it solves, and why it's different from alternatives. It's the upstream decision that drives homepage messaging, paid media, and GTM clarity.How does AI affect B2B positioning strategy?AI doesn't break positioning fundamentals — it adds market uncertainty and product pressure. Companies still must answer: what problem do you solve, for whom, and better than what?Can AI write your positioning for you?No. LLMs can accelerate research and fill in details, but they can't generate non-obvious strategy from scratch. They're best used when humans provide 80% of the thinking first.Why do multi-product companies struggle with positioning?Most markets are fragmented. Customers think narrowly — they're not shopping for "everything." Leading with one clear use case (like Apple with iPhone, Owner.com with restaurant grading) outperforms breadth.What is a go-to-market positioning framework?A GTM positioning framework defines your category, ideal customer profile (ICP), competitive alternatives, differentiated value, and homepage message — in that order, before messaging or campaigns.#b2bmarketing #ProductPositioning #GTMStrategy #B2BSaaS #DemandGeneration #ProductMarketing #AIMarketing #ContentMarketing #SaaSMarketing #RefineLabsRoundtable #FletchPMM #MarketingStrategy #ICPMessaging #HomepageCopywriting #GoToMarket

Dave "CAC" Kellogg and Ray "Growth" Rike tell the full story of how Intercom, a $400M ARR company that stalled at 4% growth, executed one of the most dramatic AI-first transformations in B2B SaaS. From writing off tens of millions in ARR to building a proprietary vertical AI model, this episode breaks down what it actually took to reinvent a mature SaaS business from the ground up.Topics CoveredFrom 4% to 26% Growth: The Numbers Behind the Turnaround. Intercom hit rock bottom with five straight quarters of declining net new ARR before founder Eoghan McCabe returned and went all in on AI following the ChatGPT launch in November 2022. Ray and Dave walk through the growth trajectory and what made the timing of the reset both urgent and actionable.The "Burn the Ships" Organizational Decision. Intercom rotated roughly 80% of its R&D team onto the new AI product, deliberately wrote off 50 to 60 million in ARR, and created small startup-like teams of 10 to 15 people with directly responsible individuals leading each workstream. Ray and Dave discuss why half-measures fail and how a stuck business actually has an advantage: very little to lose.Board Dynamics and Why Committees Kill Bold Moves. Dave shares a candid take on how PE boards versus VC boards respond differently to dramatic pivots, and why the committee nature of multi-partner VC boards tends to drive toward measured, middle-ground responses that often produce no real outcome.AI Economics: Gross Margins, Inference Costs, and Building Your Own Model. The shift from SaaS to AI-native changes the cost structure fundamentally. Ray puts current gross margin ranges in context (40 to 55% for pure AI-native, 55 to 70% for blended), explains why inference spend is actually rising despite lower per-token costs, and discusses why Intercom built its own vertical customer agent model for both performance and COGS optimization.Outcome-Based Pricing and the 99-Cent Resolution. Customer support is one of the clearest use cases for outcome-based pricing because the natural unit is obvious: a resolved ticket. Ray and Dave break down how Intercom priced Fin at 99 cents per resolution, validated the model against an 81% internal resolution rate, and watched NRR climb from 112% to 146% as adoption scaled across 8,000 customers.Never Waste a Good Crisis. Dave frames the broader lesson for SaaS CEOs: two paths exist now, dramatic AI reinvention or a Rule of 60/70 efficiency play. The Intercom story illustrates what the reinvention path actually demands. Ray adds that many SaaS companies sitting at 10% growth and 25% EBITDA are already in a slow-moving crisis and just haven't admitted it yet.If you lead a B2B SaaS company navigating the shift to AI, this episode is the most concrete case study available on what full commitment actually looks like in practice. Ray and Dave go beyond the headlines to examine the organizational design, board dynamics, cost structure, pricing model, and retention metrics behind Intercom's transformation. Whether you are considering an AI-first pivot or trying to understand why incremental approaches tend to stall, this episode gives you the analytical framework and the real numbers to think it through.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode of Predictable B2B Growth, Javier sits down with Nick Turner, CEO of Dreamdata, to break down what it really takes to build predictable growth in today's B2B landscape.Nick shares lessons from leading Dreamdata through a $55M Series B raise, including why focus—not expansion—is the key to scaling, and the metrics that actually matter to investors: growth rate, gross retention, and burn efficiency.They also dive into the reality behind AI hype, why most companies misunderstand its role in go-to-market, and how businesses should think about delivering real customer value instead of chasing buzzwords.The conversation explores the growing importance of brand, the long B2B buying cycle, and why over-reliance on short-term demand generation can quietly kill pipeline. Nick also challenges how sales and marketing teams use automation, emphasizing that while marketers can scale communication, sales still depends on genuine human interaction.At its core, this episode is about cutting through noise—focusing on the right customers, solving real problems, and building a growth engine that's actually sustainable.Key Topics and TakeawaysFundraising strategies for Series BThe role of AI in SaaS growthImportance of customer feedback and focusPredictability in growth metrics is crucial for Series B success.AI is a tool to deliver value, not a buzzword to chase.Focus on a specific market segment to dominate before expanding.Listening to customers is the most reliable way to build products.Chapters00:00 Introduction to Nick Turner and Dream Data01:51 Fundraising Journey and Predictability Metrics04:47 The Role of AI in Business07:55 Listening to Customers and Market Feedback11:20 Navigating Investor Conversations13:11 Defining Predictable Growth16:27 Focus and Market Positioning20:37 Metrics for Success and Burn Multiple22:44 The 30-Day Blackout Challenge23:45 The Sales Cycle and Brand Awareness26:34 Marketing and Sales Alignment29:43 The Evolving Role of Sales32:43 AI in Marketing vs. Sales39:23 Customer-Centric Growth StrategiesResources & LinksDream Data - https://dreamdata.ioNick Turner LinkedIn - https://linkedin.com/in/nickturnerChet Holmes - The Ultimate Sales Machine - https://www.amazon.com/Ultimate-Sales-Machine-Target-Profits/dp/1591842158Send us Fan Mail Thanks for listening to Predictable B2B Growth.Want predictable pipeline (not random acts of marketing)? Run the Predictable Pipeline Diagnostic (15 min): https://boldermediasolutions.com/pipeline Subscribe to the newsletter: https://boldermediasolutions.com/newsletter Book a strategy call: https://boldermediasolutions.com/strategyMore episodes + show notes: https://boldermediasolutions.com/podcastConnect with Javier:LinkedIn: https://www.linkedin.com/in/javierlozanojr/ Website: https://boldermediasolutions.comIf the show helps, follow + leave a rating/review.

Ask Me Your SEO Questions!What do you do when a brand-new B2B SaaS website launches with zero traffic, zero rankings and zero authority? In this episode, I'm breaking down the exact SEO strategy we used to grow Easy T from scratch.We niched down into specific recruitment sectors, built targeted service pages, created in-depth industry guides to support conversions, and used LinkedIn strategically to amplify what was already gaining traction. I'll walk you through the structure, content decisions, and positioning shifts that helped turn a completely new domain into a growing traffic and leads engine.If you're starting from zero or launching a new brand, this case study will show you what actually works. Grow your business with SEO by using the exact strategy I use with multimillion dollar companies: The Complete Beginner's SEO Course Is Here Enroll Here!Head to www.theplansuccess.com where you can get started on your SEO journey for free with some great free resources like the beginner's small business starter guide!And if you're not already, follow me over on Instagram for easy SEO tips!Website: theplansuccess.comInstagram - @theplansuccess

Evan Spiegel, the co-founder and CEO of Snap, is one of the very few people in the world who has successfully built and scaled a lasting consumer social product. Snapchat has nearly 1 billion MAUs, and Evan and his team invented some of the most important consumer products and features, including Stories, AR glasses, swipe-based navigation, the camera as the primary UX, and a lot more.In our in-depth conversation, we discuss:1. Why distribution is now the biggest challenge for creating a consumer technology business2. How Snap innovates at scale with a 9-to-12-person design team: no titles, no hierarchy, hundreds of ideas reviewed weekly with the CEO3. Why a pure software business is no longer a moat, and what actually creates durable competitive advantages today4. How AI is changing the way designers work and why they're now shipping code5. Why every major Snap feature was copied and how that forced the company to work differently6. Evan's prediction that humanity's comfort with AI will be a bigger bottleneck than the technology itself7. This year's crucible moment for Snap—Brought to you by:WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs: https://workos.com/lennyVanta—Automate compliance, manage risk, and accelerate trust with AI: https://vanta.com/lenny—Episode transcript: https://www.lennysnewsletter.com/p/snapchat-ceo-why-distribution-is—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Evan Spiegel:• X: https://x.com/evanspiegel• Snapchat: https://www.snapchat.com/@evan• LinkedIn: https://www.linkedin.com/in/evan-spiegel• Website: https://www.spiegelfamilyfund.com—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Evan Spiegel(02:28) Why consumer social products are so hard to build(04:31) How Snapchat cracked distribution with close friends, not network size(05:50) Why distribution is the new moat in the AI era(08:39) Snapchat's innovation track record (and why software isn't a moat)(11:39) Why Snap is betting on two of the hardest businesses: consumer social and hardware(16:00) Specs use cases(17:56) The innovation process(21:34) The velocity of design work at Snapchat(25:07) Why Evan says you must talk to customers(26:06) The origin story of Stories(28:25) How screenshot detection saved early Snapchat(31:03) Why they waited to hire PMs—and what role they play now(34:41) How AI is shifting the designer-PM-engineer triad(36:10) Design as an intentional bottleneck for product cohesion(37:24) Why staying close to customers matters for any leader(39:39) What Evan looks for when hiring designers(41:57) How to develop young design talent(44:16) Designers shipping code with AI—and the guardrails needed at scale(47:20) Using jobs-to-be-done to organize AI transformation(48:50) How the CEO job has changed over 15 years(51:30) Learning to communicate(54:08) Why this year is Snapchat's “crucible moment”(56:22) Being the “middle child” in tech(57:51) Screen-time philosophy with four kids (ages 2 to 15)(1:01:08) AI Corner(1:04:02) Contrarian Corner(1:06:04) Lightning round and final thoughts—References: https://www.lennysnewsletter.com/p/snapchat-ceo-why-distribution-is—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

⁠⁠⁠⁠Subscribe to the Outbound Kitchen newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠---This episode was originally recorded in French for Cognism's Prospect podcast with Laetitia Fall. The English audio you're hearing is an AI-translated dub generated with ElevenLabs. Original French version: https://youtu.be/DMRO_GGp-1A--If you're new here, I'm Elric Legloire, founder of Outbound Kitchen. I help B2B SaaS companies between $2M and $50M ARR boost their outbound results. My view: in 2026, productivity is the multiplier, not headcount. --We discuss:- Why outbound isn't dead, but pipeline got roughly 10x more expensive (1% conversion in 2015 to needing 1,000 emails per opportunity in 2023, source: Winning By Design)- Why global SDR headcount grew over 20% in a year while public layoffs dominated the LinkedIn feed- The Owner.com benchmark: 40 appointments per BDR per month, 25% close rate, $70-80K sourced revenue per BDR per month- When to hire experienced SDRs vs juniors, and the 12-meetings-per-month threshold that signals your playbook is ready- Why ICP work beats tool selection (Tier 1 closes at 25%, Tier 2 at 5%, you need 5x the volume to compensate)- Snowflake's 140 ICP data points and why 10-15 is the realistic target for most teams- Multichannel orchestration when only 30% of your prospects are reachable on LinkedIn- The data-quality test most teams skip: coverage rate per persona, per market- AI in outbound (January 2026): what actually works, why most LinkedIn AI messages fail, and the foundation AI needs (market, CRM, message principles, signals)Referenced:Outbound Kitchen newsletter: https://newsletter.outbound.kitchenElric Legloire on LinkedIn: https://www.linkedin.com/in/elriclegloire/Cognism's original French episode: https://youtu.be/DMRO_GGp-1AHost: Laetitia Fall Sources cited in episode: Winning By Design, Insight Partners (Jeremy Donovan's portfolio data), Owner.com BDR benchmarks, Snowflake ICP methodology----When you're ready⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Want to work with me? Send me a DM⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠---Connect with me⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Rigvi breaks down what truly useful AI looks like, grounded in outcomes like spend optimization, revenue protection, and predictive maintenance, not just convenience features or marketing buzz. He also makes the case that AI won't replace FM professionals, it will finally let them do the job they were always meant to do. Welcome to Elevating Brick and Mortar. A podcast about how operations and facilities drive brand performance. On today's episode, we talk with Rigvi Chevala, Chief Product and Technology Officer at ServiceChannel. With over 20 years in B2B SaaS across industries ranging from local marketing to trucking to real estate, Rigvi brings a uniquely cross-industry lens to the challenges and opportunities facing facilities management today. Guest Bio: Rigvi is an experienced management executive with strong leadership skills and over 20 years of experience in software and product development and has led multiple product lines with >$200M in ARR. He manages and and executes product roadmaps and organizational strategy with experience in evolving B2B SaaS products and reusable digital platforms. TIMESTAMPS: 00:52 - About ServiceChannel 04:19 - What surprised Rigvi about facilities 08:47 - Key unsolved challenges in the industry 11:37 - Defining useful AI vs. marketing noise 15:08 - Breaking down AI types (generative, agentic, computer vision) 28:33 - Why 90% of AI initiatives fail 33:54 - Will AI replace FM roles? 39:37 - Advice for leaders evaluating AI tools SPONSOR: ServiceChannel brings you peace of mind through peak facilities performance. Rest easy knowing your locations are: Offering the best possible guest experience Living up to brand standards Operating with minimal downtime ServiceChannel partners with more than 500 leading brands globally to provide visibility across operations, the flexibility to grow and adapt to consumer expectations, and accelerated performance from their asset fleet and service providers. LINKS: Connect with Rigvi on LinkedIn Connect with Sid Shetty on Linkedin Check out the ServiceChannel Website Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Cat Wu is Head of Product for Claude Code and Cowork at Anthropic, building one of the most important AI products of this generation. Before joining Anthropic, Cat spent years as an engineer and briefly worked in VC. Today, she's interviewing hundreds of product managers who are trying to break into AI—and seeing firsthand what separates those who thrive from those who fall behind.We discuss:1. How Anthropic's shipping cadence went from months to weeks to days2. The emerging skills PMs need to develop right now3. Why you need to build products that don't yet fully work, so you're ready when the next model closes the gap4. Cat's most underrated AI skill: asking the model to introspect on its own mistakes5. Why Claude's personality is core to its success6. Why Anthropic's mission alignment eliminates the friction that slows most large organizations7. Why “just do things” is the most important principle for working at AI-native companies—Brought to you by:WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUsVanta—Automate compliance, manage risk, and accelerate trust with AI—Episode transcript: https://www.lennysnewsletter.com/p/why-half-of-product-managers-are-in-trouble—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Cat Wu:• X: https://x.com/_catwu• LinkedIn: linkedin.com/in/cat-wu• Newsletter: https://catwu.substack.com—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Cat Wu(01:29) Working with Boris Cherny(04:29) What Anthropic looks for when hiring PMs(06:18) How to help your teams move fast(08:58) How PRDs and roadmaps have evolved at Anthropic(10:28) The Mythos model and Anthropic's shipping velocity(11:54) What happened with the Claude Code source code leak(12:53) Integrating with OpenClaw(14:19) How the PM team is structured at Anthropic(15:42) How engineer and PM roles are merging(17:54) Why product taste is the most valuable skill(20:10) Where human brains will continue to be useful(22:23) How to stay sane in constant chaos(24:16) What gets sacrificed when you ship so fast(27:47) The /powerup command(28:32) Why Anthropic has been so successful(32:28) When to use Claude Code vs. Desktop vs. Cowork(35:58) Tips for getting started with Cowork(38:44) Demo: Using Cowork to build slide decks overnight(41:48) Cat's PM tech stack and internal tools(46:47) Which teams use the most tokens(51:15) The emerging skills PMs need for AI companies(55:00) Why building evals is underappreciated(58:44) Why Claude's character and personality matter so much(1:00:44) How new models force product changes(1:05:11) The vision for Claude Code and Cowork(1:07:22) Advice for thriving in an AI-driven world(1:09:18) Why 95% automation isn't good enough(1:11:58) Build apps you use every day, not prototypes(1:13:41) The divide between AI skeptics and believers(1:15:19) Lightning round—Referenced: https://www.lennysnewsletter.com/p/how-anthropics-product-team-moves—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Today, we check in a year after the first Unsupervised Learning x Latent Space Crossover special to discuss everything that has changed (there is a lot) in the world of AI. This episode was recorded just after AIE Europe, but before the Cursor-xAI deal.Unsupervised Learning is a podcast that interviews the sharpest minds in AI about what's real today, what will be real in the future and what it means for businesses and the world - helping builders, researchers and founders deconstruct and understand the biggest breakthroughs.Thanks to Jacob and the UL production team for hosting and editing this!Jacob Effron* LinkedIn: https://www.linkedin.com/in/jacobeffron/* X: https://x.com/jacobeffronFull Episode on Their YouTubeWe discuss:* swyx's view from the center of the AI engineering zeitgeist: OpenClaw, harness engineering, context engineering, evals, observability, GPUs, multimodality, and why conference tracks now reveal what matters most in AI* Whether AI infrastructure has finally stabilized: why “skills” may be the minimal viable packaging format for agents, why infra companies have had to reinvent themselves every year, and why application companies have had an easier time surviving model volatility* The vertical vs. horizontal AI startup debate: why application companies can act as the outsourced AI team for enterprises, why some horizontal companies still matter, and why sandboxes may be the clearest reinvention of classic cloud infrastructure for the AI era* The “agent lab” playbook: starting with frontier models, specializing for your domain, then training your own models once you have enough data, workload, and user behavior to justify the cost and latency savings* Why domain-specific model training is real, not just marketing: how companies like Cursor and Cognition can get users to choose their in-house models, and why search, domain specialization, and distillation are becoming more important* Open models, custom chips, and alternative inference infrastructure: why swyx has turned more bullish on open source, why non-NVIDIA hardware is suddenly getting real attention, and why every 10x speedup can unlock new product experiences* What it means to sell to agents instead of humans: why agent experience may mostly just be good developer experience by another name, why APIs and docs matter more than ever, and how pretraining-data incumbents are compounding advantages in an agent-first world* Why memory and personalization may become the next big wedge: today's models mostly reward frequency of mentions, but in the future, swyx expects product choice to be shaped much more by personalized memory systems* The state of the AI coding wars: why coding has become one of the largest and fastest-growing categories in AI, how Anthropic, OpenAI, Cursor, and Cognition have all ridden the wave, and why the category may still have more room to run* Capability exploration vs. efficiency: why the industry is still in a token-maxing, experiment-heavy phase where people are rewarded for spending more rather than less* Claude Code vs. Codex and the strange stickiness of coding products: why first magical product experiences may matter more than expected, and why the bigger mystery may be why only a few names have emerged as real winners so far* What the end state of the coding market might look like: two major players, a longer tail of niche products, and possible disruption if Microsoft, Mistral, xAI, or the Chinese labs push harder into coding* Where application companies still have room against the labs: why frontier labs are trying to expand into verticals like finance and healthcare, but still leave space for focused companies that own the workflow and the last mile* Why coding may be a preview of every other AI market: the first category to truly go parabolic, the clearest example of foundation model companies colliding with application companies, and a template for how future vertical AI markets may develop* Why AI valuations now feel unbounded: from billion-dollar ARR products built in a year to trillion-dollar market caps, swyx and Jacob unpack how the AI market has broken traditional startup intuitions about scale and durability* Consumer AI vs. coding AI: why ChatGPT's consumer category may have plateaued on frequency and product design, while coding continues to feel like a daily-use category with real momentum* The next product frontier beyond coding: consumer agents, computer use, and “coding agents breaking containment,” with swyx's thesis that 2025 was the year of coding agents and 2026 may be the year they begin to do everything else* Whether foundation models are really killing startup categories: why swyx is less worried for early founders, more worried for mid-size startups and traditional SaaS, and why building something ambitious may now be the best job interview for a frontier lab* AI vs. SaaS and the internal culture war around adoption: the tension between AI-native employees who want to rip out expensive software and skeptics who think quick AI-built replacements create fragile systems* Why traditional SaaS may be under real pressure: swyx's own experience spending six figures on event and sponsor management software, the temptation to rebuild it cheaply with AI, and the broader question of whether teams will trust custom AI-native replacements* Biosafety, security, and frontier model access: why swyx raised biosafety at a dinner with Anthropic's Mike Krieger, why Krieger argued security is the bigger issue, and what restricted model releases reveal about Anthropic vs. OpenAI* The era of giant models: why 10T+ parameter systems may only be a temporary rationing phase before bigger clusters arrive, why labs may increasingly keep their most powerful models private for distillation, and why scale alone no longer feels like a complete answer* Memory as the slowest scaling factor in AI: why context windows have improved far more slowly than people hoped, why million-token context still has not changed most real workflows, and why memory may be the key bottleneck for the next generation of systems* What swyx changed his mind on in the past year: becoming more bullish on open models, more convinced that the top tier of agent startups behaves very differently from the median AI company, and more optimistic about fine-tuning and specialized model adaptation* “Dark factories” and zero-human-review coding: the next frontier after zero human-written code, where models not only write the code but ship it without human review, forcing companies to rethink testing and verification from first principles* Why RL and post-training may matter more than people assumed: even if the resulting models get thrown out every few months, the data, workflows, and domain-specific improvements persist* Synthetic rubrics, Doctor GRPO, and multi-turn RL: why reinforcement learning is becoming much more domain-specific and multi-step than many people realize, opening the door to much deeper customization* The next frontier after coding: memory, personalization, and world models, including why swyx thinks world models matter not just for robotics or gaming, but for giving AI something closer to lived understanding* Fei-Fei Li, spatial intelligence, and the Good Will Hunting analogy: the idea that today's LLMs may know everything by reading it all, but still lack the lived experience that turns knowledge into a deeper kind of intelligenceTimestamps* 00:00:00 Intro preview: AI coding wars, startup pressure, and market structure* 00:00:28 Welcome to the Latent Space × Unsupervised Learning crossover* 00:01:17 What AI builders are focused on now: OpenClaw, harnesses, and infra* 00:04:33 Why AI infra is harder than apps, and where startups can still win* 00:06:39 Should companies train their own models?* 00:09:28 Open models, custom chips, and the new inference race* 00:11:25 Designing products for agents, not just humans* 00:16:49 The state of the AI coding wars in 2026* 00:19:27 Capability exploration, token-maxing, and why coding is going parabolic* 00:21:41 What the end state of the coding market could look like* 00:23:50 Where app companies still have room against the labs* 00:27:02 Why AI valuations and market swings feel unprecedented* 00:28:56 Consumer AI vs. coding AI, and why sticky products still matter* 00:32:28 What the next breakthrough product experience might be* 00:32:53 2026 thesis: coding agents break containment and eat the world* 00:35:27 Are foundation models wiping out startup categories?* 00:37:33 AI vs. SaaS, vibe coding, and internal team tensions* 00:40:01 Biosafety, security, and the politics of restricted model releases* 00:42:19 Giant models, compute constraints, and the limits of scale* 00:44:30 Memory as the real bottleneck in AI* 00:44:57 Why swyx changed his mind on open models* 00:47:44 Dark factories and the future of zero-human-review coding* 00:49:36 Why post-training and RL may matter more than people think* 00:51:50 Memory, world models, and the next frontier of intelligence* 00:53:54 The Good Will Hunting analogy for LLMs* 00:54:21 OutroTranscript[00:00:00] swyx: Isn't that crazy? That number is just mind boggling.[00:00:03] Jacob Effron: What is the state of the AI coding wars today?[00:00:05] swyx: We're in a phase of sort of like capability exploration. The general thesis that I have been pursuing now is that the same way that 2025 was a year coding agents 2026 is coding agents breaking containments to do everything else.[00:00:16] Jacob Effron: Do you worry about the foundation models just getting into a bunch of these startup categories?[00:00:21] swyx: Mid-size startups. Yes.[00:00:23] Jacob Effron: What do you think the end state of this market is[00:00:25] swyx: for the market structure to, to significantly change? There would be[00:00:28] Jacob Effron: today on unsupervised learning. We had a, a fun episode and what's really become an annual tradition, a crossover episode with our friends at Latent space.Swix and I sat down and we talked about everything happening in the AI ecosystem today. What we thought of the various changes at the model layer, what's happening in the infra world, the coding wars, and a bunch of other things. It's a ton of fun to do this with someone I really respect and another great podcaster in the game.Without further ado, here's our episode. Well switch. This is, uh, super fun to be back with another unsupervised learning, uh, latent space crossover episode.[00:01:02] swyx: Yeah,[00:01:02] Jacob Effron: I feel like a lot of places we could start, but you know, one thing I always find fascinating, uh, about the way you spend your time is you obviously are like at the epicenter of this engineering movement and community, and you run these events and conferences and put on these.Awesome talks and, and I think just have a great pulse on the zeitgeist of what's going on.[00:01:16] swyx: Yeah.[00:01:17] Jacob Effron: Maybe to, to start just what are the biggest topics people are thinking about right now?[00:01:21] swyx: Yeah, so I just came back from London, uh, where we did a IE Europe and we're doing roughly one per quarter now, which Yeah, you've[00:01:27] Jacob Effron: really up[00:01:27] swyx: the, hopefully[00:01:28] Jacob Effron: up the, up the pace.[00:01:29] swyx: It's trying. We're trying to match AI speed, youknow?[00:01:30] Jacob Effron: Yeah, exactly. The tops would be completely different, I imagine. Uh,[00:01:33] swyx: yeah. You know, I definitely curate the tracks, like you can see what I think. When you see the track list and the, the speakers that I invite, obviously Open Claw is like the story of the last four or five months, and then be, be just below that.I would consider harness engineering, context engineering to be two related topics in agents and rag. And then there's a long tail of Evergreen stuff like evals, observability, GPUs, uh, and uh, LM infra and just general, just in general. We also have other updates on like multimodality and, uh, generative media, let's call it.Um, but I definitely, the, the first three that I mentioned are top of mind people. Yeah.[00:02:13] Jacob Effron: I think harness is particular like, so interesting. Um, you know, there was this tweet from Harrison Chase, the, the lane chain, CEO, that, that caught my eye recently where he said, you know, it finally feels like we have stability, uh, around the infrastructure for, uh, you know, around ai.And I think what. He basically was implying his like, look over the past two, three years as a company at the epicenter of AI infrastructure, it was a bit like playing whack-a-mole, right? You were constantly moving around with, however, the building patterns were evolving[00:02:36] swyx: for Harrison for sure. Right? Like he's basically had to reinvent the company every year since he started Lang Chain.Right? It was Lang chain, Ang graph and LP agents and like, uh, I think he's like one of the most nimble, adept sharp people about this. Yeah. Yeah.[00:02:49] Jacob Effron: Saying now, now is finally the time stability[00:02:51] swyx: this. Yeah.[00:02:52] Jacob Effron: Yeah. Um, do you buy that or what have you kind of make of that take?[00:02:56] swyx: I think that. It, it's very expensive to say this Time is different sometimes, but when you're just writing code, like it's actually okay to just like try to make a call and I think it may not even matter if this call is right or not.Like I just don't even care that much because you can be right on a thesis, but if you don't, you don't figure out how to monetize the thesis, then who cares if you said something first that said, um, it does feel like, for example. Uh, we went through a lot of different ways of passion packaging integrations up with, uh, with agents.And it feels like we've landed at skills, which is like the minimal viable format. Yeah. Which is just a markdown file, uh, with some scripts attached to it, and I don't see how it can be more simple than that. And so there is some justification for. The stability around harnesses. I feel like there may be more adaptation with regards to maybe like the real time elements or subagents or memory or any of those like agent disciplines, let's call it in, in agent engineering.Uh, but if, if the thesis is that, okay, you just want agents are LMS with tools in the loop with a file system, what they can do. Retrieval with, with skills and all these like standard tooling that now seems to be relatively consensus then probably. That makes sense. Um, I just think like there's no point trying to stake your reputation on this thesis that we're there because if it changes again, just change with it.It's fine.[00:04:33] Jacob Effron: Yeah. It's always, you know, I've always been struck by how that is. Much more challenging for infrastructure companies and application companies. Like obviously I think, yeah. You know, on the application side you've seen, you know, Brett Taylor from Sierra Max, from Lara. Like, they're like, look, we build, you know, what's ahead of the models and we're willing to throw everything out every three months, you know, as the models get better and better.Exactly. Yeah. But the thing you at least have there is you have. Uh, you have an end customer, right? That's like decently sticky. Um, you know, they will mostly stick, you know, they'll, they'll give you a shot at least of, of building these things. What I've always found more challenging, uh, at, at the kind of like, you know, reinvent yourself every three months of the infrastructure layer, it's like, you know, developers are definitely a, a pickier audience maybe than an accounting firm or, uh, you know, a bank.Yeah. And so it's definitely a, a, a more challenging position to be in to, to have to constantly reinvent yourself.[00:05:17] swyx: Yeah. Yeah. Yeah. And, and like when they turn, it's like. Very complete. Like, they'll leave to like the, the hot new thing, uh, because there's like no defensibility, I guess. Like e even, even if you are a database, like, uh, people can migrate workloads off databases.Like it's, it's a, it's a known thing. Uh, so I think like basically what we're talking about is the vertical versus horizontal, uh, debate in, in AI startups. And uh, the way I think about it also is just that like when you are. Um, Lara, when you are a bridge, like you are the outsource AI team, right? You, you are, your job is to apply whatever state ofthe art AI methods.[00:05:55] Jacob Effron: Yeah. Like this translation layer between model capabilities and your[00:05:57] swyx: own customers. Yeah. To, to the end customers and like, well, if they didn't have you, they would've to hire in house and they're not gonna hire in house so they have you. And like, I think that's like a reasonable, like very robust to any whatever trends and, and discoveries that people make in, in the engineering layer.I do think like there is, um. It like sort of useful horizontal companies being built, but they're all. Very much like, sort of like the reinventions of classic cloud in the AI era and the, the primary one being sandboxes. Yeah. Um, which like, it's another form of compute guys, like, let's not get too excited about it.But I mean, like the, the workloads are enormous.[00:06:38] Jacob Effron: Right.[00:06:38] swyx: Yeah.[00:06:39] Jacob Effron: It's interesting, and I feel like as, as part of this, you know, the questions that folks are asking around infrastructure, there's a lot around, you know, the extent to which companies should have their own AI teams and what they should be doing in-house.And, you know, uh, I think there's questions around should people be training their own models? Should people be doing, you know, rl, uh, in-house based on the data they have? I feel like, you know, one has to evolve their takes on this every, every three months with paces. But where, where are you at on this today?[00:07:00] swyx: I think, well, I mean actually all models have gone up. Um, and obviously I'm involved in cognition and also cursors doing, doing, uh, a lot of own model training. And I think that that is some part of the, what I've been calling the agent lab playbook, where you start off with the state of the art models from, uh, from the big labs and you, uh, specialize for your domain.But once you have enough workload and enough high quality data from your users, then you can obviously train your own models and like save a lot on cost and latency and all that, all that good stuff. Um, you also get like a marketing bonus of like calling it some fancy name and putting out some research[00:07:38] Jacob Effron: from my seat.I can't tell how much of it is like actual, you know, value that's provided to the end user. And how much of it is that marketing bonus? Right. It seems some combination of the[00:07:45] swyx: I think it's both.[00:07:46] Jacob Effron: Yeah.[00:07:46] swyx: Um, no, no. There, there actually is real value. Um, and you, you know that for a number of reasons. Like one, even when it's not subsidized, people do choose it as like one of the top four or five.This is both composer two and, uh, suite 1.6 I one of the top five models. Like in a, in a fair market? In a free market, yeah. In a, in a, in a model switch. Or people do choose it and like, it's not subsidized. Like, so that's as good as it gets. Uh, but beyond that, like domain specific models, for example. For search with, with both, which both companies have absolutely makes, makes a ton of sense.Everyone says like, yeah, we should always, always do this. And honestly like, I think the infrastructure for that is becoming easier with, um, like thinking machines tinker thing as well as primary like, uh, lab stuff. Yeah, I mean like, this is one of those like reversal of the, the bitter lesson where you first bootstrap on the large models and the general purpose models to get big.And as you get very well-defined workloads that are just high quantity but not high variance, um, then you just distill down to a smaller model and run that on your own. Right. Which like totally makes sense.[00:08:50] Jacob Effron: What I'm less clear on is the kind of DIY RL use case, which I think is really mostly around, you know, improved, uh, quality for, for different things.Obviously there's probably like more efficient ways to, you know, get a smaller model that's that's faster and cheaper. And it'll be interesting to see whether. You know, obviously you had, you know, uh, two, three years ago this whole case of companies that were, you know, pre-training and claiming better outcomes in, in their domains than getting kind of cooked as each model iteration improved.You know, I wonder whether that's a, a similar story plays out in the, uh, in, in the, our all space. Yeah, for the focus on, on on pure outcomes and quality, not the cost side, which clearly your own models for cost at scale makes a ton of sense.[00:09:28] swyx: I think there are this, there are two sides of the same coin.Like you basically always want to hold, uh, quality constant or trade off a little bit of quality for a drastic decreasing cost. And that's true for everyone. Uh, one element I wanted to bring out, which is very much in favor of open models, is custom chips. So this would be cereus, but also talu. And then there's a huge range of stuff in between.This has been a huge story this past year on just like everything non Nvidia is getting bid up, including like freaking MatX is working for, which is very, which is very rewarding for me, but I think one of those things where like, oh, like the suddenly, because the number of alternative. Hard, uh, hardware is increasing and the inference that you can get is insanely high.Like, um, we're talking thousands of tokens per second instead of less than a hundred. So the trade off for qua quality doesn't hold as much anymore because the speed is so high.[00:10:24] Jacob Effron: Have you seen a lot of companies go all in on the alternative chip?[00:10:26] swyx: So cognition has Yeah. On Cerebras, uh, and, and so has OpenAIUm, uh, and so no, I don't think so beyond that, uh, and that, do you think that's like a, that's mostly, that's foreshadowing of, that's, yeah. I used to be kind of a skeptic in terms of like, okay, so what if I get my inference at a hundred to a hundred tokens per second sped up to 200 tokens per second. It's only two X faster.It's not that big a deal. Um, but when you, uh, I think every 10 x does unlock a different usage pattern. Um, and you, we have proof in Talas and, and some of the others. That you can actually, um, drastically imp improve inference speed and what happens from there? I don't even really know, like it's, it's so hard to predict when entire applications just appear at once.Yeah. Uh, and it also isn't that expensive, right? So like, um, this is one of those things where like, I, I think the, the investment cycle is gonna be multi-year. Um, and I. Would caution people to not dismiss it too, too quickly.[00:11:25] Jacob Effron: Yeah. I mean, one other like infra question I was curious to get your thoughts on is obviously it seems increasingly a lot of the cutting edge infra companies are building for agents as the buyers of their product or users of their product, right?[00:11:35] swyx: Ooh,[00:11:36] Jacob Effron: and[00:11:37] swyx: another huge theme. Yeah. Yeah.[00:11:38] Jacob Effron: And I'm trying to figure out like what. What, what do you have to do differently about selling into agents? Um, are they just the ultimate rational developers? Uh, or is there, you know,[00:11:46] swyx: no, absolutely not. Um, I think they are easily prompt, injected and, uh, very tuned towards like, basically com compounding existing winners.[00:11:57] Jacob Effron: Yeah,[00:11:57] swyx: so like if, like, congrats if you won the lottery for getting into the training data right before 2023, because now you're like installed in there for the foreseeable future. But yeah. Uh, you know, one stat that Versal, uh, CTO Malta dropped at my conference was that there are now, uh, 60% of traffic to Elle's, um, like app arch, like admin app architecture for like configuring versal applications, uh, is bought.It's not, it's not human. Uh, so like your primary customer is agents now. Um, and it's mostly co like mostly coding agents, mostly people using CLI on CP or whatever. But yeah, I mean, I think. More. I, I think step one, if it doesn't exist as an API that agents can use, it doesn't exist. Right, right. Which I think is like, uh, it's a good hygiene thing anyway, to, to make everything API available, but not as like an extra, um.Push on like products, people to not only work on the ui, um, you should probably work on the on SCLI stuff. Beyond that, I think honestly there is like, so I, I come from the sensibility of, I think everything that you are trying to do for agents experience now, which is the term that Matt Bowman and Nullify is trying to coin, is the same thing that you should have been doing for developer experience.That you should have had good docs, you should have had a consistent API, uh, that is. Mostly stateless. Um, you should have, I guess, discoverable or progressive disclosure or like search or like whatever. And so now that people have energy in like finding these customers to do that, that's great. Um, do I believe in.Extending beyond that into something like a EO, um, for gaming The chatbots? Not necessarily, but obviously there's gonna be huge advantages when people who figure out the short term wins. Yeah. And short term wins can compound.[00:13:43] Jacob Effron: Do you think these compounding advantages to like the, the pre-training data cutoff companies, like, you know, obviously over some period of time, I imagine that doesn't persist.And so as you think about like. I dunno, three, four years from now what the, you know, selection criteria end up being. Do you think it still mirrors exactly what you were saying before? Like it's exactly what you should have been doing all along to sell a good product to developers?[00:14:01] swyx: It could be, except that I think in three, four years we'll probably have much better memory and personalization.So then general a EO or GEO doesn't really matter as much. So I think whatever memory or personalization system we end up with will probably d determine what you end up choosing much more. Than, than what is currently the case, which is just frequency of mentions, let's call it. Yeah,[00:14:26] Jacob Effron: yeah.[00:14:26] swyx: Uh, so you just spa quantity and I think that's, I mean, that's something I'm looking forward to.I do think, like, like, you know, I, I think that the fundamental exercise to work through for yourself is if you start a new, um, sort of. Uh, disruptor company. Now there's a, there's a big incumbent that everyone knows, like, like superb base. Super base is like, kind of like the Postgres, like database, uh, incumbent.If you wanna start like new superb base, how would you compete with them? And I don't necessarily have the answer, but I, I, I do think like people, like resend like relatively new. I think they would start like 20, 23 and still there was, there was a recent survey where like, people. Checked what Claude recommends by default.If you just don't prompt it with anything, just say, gimme an email provider and says, resent as in like 70, 70% of each cases. Like the fact that you can get in there with like such a relatively short existence, I think is, is encouraging.[00:15:14] Jacob Effron: Yeah.[00:15:14] swyx: I do think like. Um, you do want to do whatever it is to, to like to, to get in that Very short mentions this because, um, it's not gonna be 20 of them, it's gonna be like three.[00:15:26] Jacob Effron: No, definitely. It feels like, uh, you know, probably more, more consolidation than ever. Uh, or, or kind of like, you know, uh, a winner take most market than maybe the, the, the physics of go-to market in the past. Yeah. Might have, uh, enabled.[00:15:38] swyx: The other thing also is like, semantic association is gonna be very important, uh, in the sense that like, you want to do like the combo articles where you're like, use my thing with for sale, with blah, blah.And like that all gets picked up in a, in a corpus. And so that's. Probably one thing that you, you wanna do? Well, I don't know what else. Uh, it's, it's, it's, it's one of those things where like, I think I feel, I feel I'm behind, uh, I don't know how you feel about this, but like,[00:16:04] Jacob Effron: I think AI is just everyone constantly feeling like they're behind some, uh,[00:16:08] swyx: yeah.With,[00:16:09] Jacob Effron: I wanna meet the person that doesn't feel behind,[00:16:11] swyx: but like with, with ax, right? Like, so, so like, my, my stance was that exactly what I said before, like everything that you, that you should do for agents is something that you should have done for humans anyway. Yeah. And so. To the extent that you're just getting it more energy to, to do things for agents, great.But like, uh, it's hard to articulate what new thing apart from just like more spam, um, that you should be doing. Anyway, that would be my take right now. Um, I I, I do think like there, there will be more turns at this. I think the personalization turn that is coming, um, will be big. And I don't know what that looks like because like basically we're kind of, we feel kind of tapped out on the memory side of things.[00:16:49] Jacob Effron: Yeah. I, I guess since we last chatted, you know, you, you took this role over at cognition, um, and you've obviously have a, have a front row seat to the AI coding space today. You know, I feel like coding in many ways. You know, people view it as this, like, I mean, besides being like the, the mother of all markets and this massive opportunity, I think it's kinda a preview of like, what's to come for many other spaces.Both. Yeah. You know, I feel like agents are most advanced in coding. I also feel like the, you know, competition between foundation models and application companies, you know, and, uh, mirrors what we may see in other spaces. And so maybe for our listeners, can you just lay out like what is the state of the AI coding wars today?[00:17:25] swyx: Um, it is massive, right? Like, uh, and I don't think necessarily, last time we talked about this, we appreciated the size of what[00:17:32] Jacob Effron: No, I wish we did.[00:17:33] swyx: I state of AI coding wars today, um, both opening eye philanthropic have made it their p serials to competing coding. Um, and. Tropic is like 2.5 billion in a RR just from Cloud Code.The way they recognize a RR is. Opt for debate, uh, open ai. I don't think the, a public number is known, but let's call it 2 billion as well. And then cursor is like, rumored to be 2 billion, you know? And, and those, those are like the public numbers that are known? Yeah. Um, so like huge markets that have just been created in the past one year.Like, like anthropic, just like Claude Code just recently celebrated their one year anniversary, which is, yeah, pretty nice. Um, so, and then I think, like the other thing that I see is there's, there's some other people who are like, oh, here's like the, the sort of relative penetration of, uh, Claude use cases, right?Like, and it's like coding 50% and then legal, whatever. Health, uh, it's like the, the remaining ones. And there was a very popular tweet that was like, okay, I'll look at the, the empty space and all these other use cases. If you are a new founder today, you should be betting on the other stuff because on, on a sort of catch up Yeah.Theory and my. Consider my, my pushback is the same pushback that, uh, I had on app over Google, which is like, well, well why is this time different? Like, why, if it went from let's say 10 to 50% in the past year, why can't I keep going? Uh, and like getting that wrong is actually a very painful one because you could have just did, did the momentum bet.Instead of the mean reversion bed. So I, I, I think that that is the, the state of things now that people are very, very much into psychosis. Um, they're are getting rewarded for spending more rather than spending less. And I think we're not in that phase of efficiency. We're in a phase of sort of like capability exploration.So I think people who are more crazy, who are more. Uh, creative, um, get rewarded comparatively. Yeah.[00:19:27] Jacob Effron: Well, it's interesting. I mean, it feels like behind these like token maxing, leaderboards and whatnot is this, it's like the first phase of this transition from a workforce perspective is you just gotta show your employer like, Hey, I, I use these tools.[00:19:37] swyx: Here's my nu number of tokens I cost, and that's it. They don't care about the quality. Right. It is, uh, maybe distasteful to someone who cares about the craft and, and all that. Um, but directionally everyone just wants you to go up regardless. And so, um, there it is not very discerning. It's, and it's probably very sloppy, but I think it's net fine because we're still probably underusing ai just in generally.Yeah. Um, and so I think that's like very interesting. Like we had on the podcast, uh, Ryan La Poplar from OBI, who spends a billion tokens a day. Yeah. Um, and that's for those county home, it's like something like 10,000 worth, $10,000 worth a day of API tokens. If they, they did market rates, um, and like most of us can't afford that.Yeah. But like. And, and, and probably a lot of what he does is slop.[00:20:25] Jacob Effron: Right.[00:20:25] swyx: But like, he's going to dis, he's like, if there were a new capability, he would discover it first before you because he was, he was trying and you were not trying. Right. And like, you only do things that work like, well, good for you.But like the, the people who are going to discover the next hot thing are living at the edge.[00:20:42] Jacob Effron: Right and increase in living at the edge of just having the compute budget to like run these experiments. I mean, kind of similar to what living at the edge on the research side has always been. You know, it was constrained in many ways by the amount of compute you had to run these experiments.It feels similarly on the, almost on the builder or like actualizing these tools now.[00:20:56] swyx: Yeah. The other thing that's, I mean, very obvious is philanthropic is kind of like the high price premium player. Um, that where, you know. Restricting limits or restricting model releases even is like the name of the game.Whereas Codex is like, come on in guys, use our SDK, use our login and we don't care. We're gonna reset limits. Whatever you do want to try to exploit the subsidies where you can get it. And definitely Codex is super subsidized right now. Gemini also very subsidized. Um, and. Comparatively, like, I think you should make, Hey, I guess while, while that's going on, it's not that bad to be a capabilities explorer on just the $200 a month plan from Cloud Code or from OpenAI.Um, and, uh, I I, I, my sense is that people aren't even there yet.[00:21:41] Jacob Effron: How do you think this, like, market ultimately plays? I mean, it's obviously such a big market that, you know, any slice of that market is interesting for, for anyone going after it. But I think what, what makes people so interesting in the coding market particularly is it feels like it's kind of this.Foreshadowing of what will happen in other, you know, any other kind of application market that the foundation models eventually turn to and are all their models against and gather data around. And so how do you think, you know, like does there end up being room for lots of different kinds of players or like, what do you think the end state of this market is and is that, do you think that's applicable to other markets?[00:22:10] swyx: I feel like there will be, I mean. Status quo is probably the most likely outcome, which is there are two big players and there's a small range of longer tail people that, um, fit other use cases that the, the two big players don't. That feels right to me. I think that, um, for it to, for the market structure to, to significantly change there would be, there needs to be significant change in like the economics or like the, the brand building or like the, the, the, the value propositions of the, of the companies involved and I.Haven't seen any in the last six months that, that have really changed the stories materially. So I feel like they would just keep going until something, something else happens. Something else happens, meaning like Microsoft wakes up and like goes like. Guys, we have GitHub, we have, uh, you know, we, we, we'll, we'll do something much bigger here than other, other than just copilot.Um, and, uh, that would be a big change. Um, MSL has put out a model now, and I was in a breakfast with, uh, Alex Wang, where they were like, yeah, like, we, we really, really want to go after the coding use case. We haven't done anything yet, but like, don't underestimate them. Right. Um, and, and similarly for the Chinese labs.Um, I think they're trying to go after it. Like ZAI is doing stuff. GLM uh, ZI and GLM is same thing. Um, uh, and, and so it's, so like everyone's trying to get a piece of that pie. I, I feel like the, the status quo has been pretty stable for the past, like almost a year I'll say.[00:23:39] Jacob Effron: Yeah. And is the room for the, not like, you know, for, for the application companies more on like the enterprise side or like where do the, where do the, like what surface area do the model companies leave for application companies?[00:23:50] swyx: Yeah, that's a good one. Um. It's very much evolving. Um, it, I, I, I will say because opening I did not have this, the, this level of attention on coding. Yeah. Uh, a year ago. We just don't have that much history. Right. Um, and it seems like, for example, so the big push at Open I now is the Super app. Um, is that a consumer thing?Is that like a products like. Portfolio rationalization thing, how much is that gonna take away attention from coding at the time when they actually do want to put more coding? I think it's, it's very unclear. So I do think like there's, there's all these, like in both big labs, there's. Uh, sorry. Both of the, and, and drop and, and deep minus and XAI are are separate cases.Um, they are trying to see the other time expansion areas. So cloud code for finance. Yeah. Um, uh, cloud cowork, all those, all those things. Whereas I think cursor and cognition are like comparatively just focused on coding and so I, I do think they leave space and I do think for the other verticals that also means the same thing.Right. That, uh, that they're not gonna be that. Um, intensely focused on, on, on that domain. Except for, I, I think I would mark out finance and healthcare as like the next ones, um, that they're clearly going after. Uh, I, I would say comparatively, healthcare seems more thorny. There, there, there've been some announcements about it, but like, I would respect the, the finance work a lot more just because like the, the path to money is a lot clearer.[00:25:12] Jacob Effron: Yeah, no, I mean, obviously like, I, I think, you know, maybe similar to, to the space that's being left in these other domains, you know, there's obviously. Uh, a lot that's required to actually implement these tools in enterprises, uh, versus, you know, maybe just giving them, uh, giving model access to, to folks outta the box.[00:25:27] swyx: Yeah, yeah. Yeah. So the, the agent lab thing is like, we'll do the last mile for you. Whereas I think the model labs tend to just trust the model and, and be minimalist about it. Both of them work.[00:25:38] Jacob Effron: Yeah.[00:25:38] swyx: I, I don't, I don't necessarily think one, uh, beats the other, uh, for every, for every use case. Um, all I, all I do know is that it does seem like.Uh, the large enterprises do want a dedicated partner that isn't just the model labs, which is kind of interesting.[00:25:55] Jacob Effron: We, we've been in this phase of, of pure capability exploration. And so I think nothing has been, you know, better for the large labs, right? I mean, they're always gonna be, uh, uh, the frontier of, of capability exploration.And so I think have a very good relationship with a lot of these enterprises. But ultimately over time, like. The, uh, the incentive structure of these labs is always gonna be maximal, you know, token consumption for, uh, for the end customers they work with. And there's just, I think, so few companies that have actually gotten to massive scale.Maybe coding again is the most interesting. So it's the first space that really is just completely gone, you know? Yeah. You must love it every day. Like absolutely insane. And. I think it[00:26:32] swyx: gets even. Okay. I mean, like, I think we, we say good things about crystal cognition, but the sheer liftoff of like both end UPIC and open ai.‘cause they, they, they have independent valuations. I mean, let's throw an XEI in there because it's now I ping at 1.2 trillion. That number is just mind boggling. Like I, I feel like in normal investing or normal startups, there's kind of like a ceiling market cap or valuation. Totally. That, that like you, you reach and you go like, all right, let's, it's gonna be chiller from now on.And these guys are not slow down. No.[00:27:02] Jacob Effron: Well, I also think the dynamic is fascinating about some of these later stage companies is, is, you know, in the past, I feel like in, in venture world, if you got to a certain level of scale, the question around you was really more a valuation question. And this is like why there was different phase, like, you know, types of venture people did and like the late stage growth people were just incredible at like, you know, a little bit of what's the ultimate market opportunity of this company, but also what's the right way to, to value it.Like we know it's, it's in some bands of an outcome that is like. Sure there's some variance to it, but it's like relatively understood what that bands is and then maybe you get over time surprised to the upside. Whereas any kind of like later, even the labs themselves, any later stage company, the bands of which that company might be worth right now, even in a year or two years are so massive because of how fast the ecosystem changes that it's like.Even for later stage companies, every three months could be an existential level event to the upside to the downside. Yeah. Um, and I think that, like, you are obviously seeing it in the, in the positive with code, which, you know, if you think about a company like philanthropic, you know, that. For a while, it was like unclear if they were going to have access to enough capital, um, to really stay in the, in the race, right?And then coding hit at the exact right time. They had the perfect model for it. They executed brilliantly. Um, and you know, now are, are, you know, uh, you know, one of the most valuable companies in the world.[00:28:13] swyx: Uh, at the same time, I, I don't find, I, I have zero sympathy for opening eye because they're crushing it and they're all rich.You know, this is like a high class champagne problem to have to, uh, to be number two at coding or whatever. Like, who cares? Like, you're, you're doing great.[00:28:27] Jacob Effron: Yeah. It's funny though. I can't even, I mean, you would be closer to this, uh, you know, even that you're in the AI coding space, but it's like a lot of people I talk to think Codex is just as good, if not better than Claude Code.Right. I think one thing that I've been really surprised by, and maybe, maybe Cloud Code is a better product in some ways, I'm curious your thoughts is just in consumer AI with chat GBT. You saw this big first mover advantage, right? Where admittedly today, like, I don't know, Claude Gemini. Great products.Not sure, not abundantly clear chat GBTs any better, but like. People stick with chat, GBT, it's the first thing to introduce them.[00:28:56] swyx: They stay, but they're not growing anymore. I don't know if you've seen[00:28:59] Jacob Effron: Right. But that to me is more of like a, a, a product problem than it is. They're not like, it's not like they've like lost share to someone else.My understanding is the overall problem with consumer AI today is much more of a how do you take this tool and, you know, for, for folks like us, like knowledge workers, it's like this incredible magic tool, but it's not necessarily a daily active use tool for a lot of people around the world today. And what are the like products?It's, it's kind of a category wide problem. Like in coding, for example, like. The entire space has gone parabolic. There may be some relative growth in, uh, in other consumer AI players, but it's not like consumer AI as a category is like going parabolic and they're not capturing most of that thing. I think it's actually the larger problem is much more, hey, the category has kind of hit a bit of a plateau of people haven't figured out how to bring, you know, tons more users on board.Yeah, yeah. Or increase the frequency of those users. And so it seems more of a category wide problem than it is, you know, a massive market share of change. I was gonna draw the comparison to, to the coding space where Claude Co is the first product, obviously, to introduce people to this magical experience.You know, by all accounts, codex is, is pretty damn close to as good, if not better. Um, but like still that first product, you, you would've thought that would not be a super sticky, uh, you know, product surface area. And it actually has, it turns out, I, it feels like the first lab to introduce you and experience really does, uh, keep a lot of, uh, a lot of the focus.[00:30:12] swyx: I, I think. M maybe it's like still, still early days. You know, Chad, BT is like three plus years old and Yeah. Cloud code is only one. Just turned a year. Yeah. So give it time, you know? Yeah. Like, yeah. I mean, definitely sometimes a lot of people have switched from to Codex. Maybe that will keep going. I, it's like really hard to tell.Uh, yeah. I, I, I do, I do think that. Because we are in this like, high volatility, high temperature phase. Um, the loyalty and stickiness to first movers and category creators, I don't think is as high as it might be in some other, uh, areas in our careers that we've looked at.[00:30:47] Jacob Effron: Yeah. Though, I mean, I've been surprised by the cloud code thing.I, I would've thought that, like, in many ways I always worried about the[00:30:52] swyx: enterprise. You think you would've been gone by now?[00:30:53] Jacob Effron: Not gone. But I would've, I I always worried that the, that the consumer business of these companies would be quite sticky. And then the enterprise API business. Uh, was actually like, you know, in some ways like your least loyal buyers, like they would, they would move to,[00:31:05] swyx: right, right.But, but they worked out that it wasn't the enterprise API it was enterprise product.[00:31:09] Jacob Effron: Totally. And maybe that was the, that was the secret that like, but the amount of lock-in or just default behavior that has happened in that space, uh, is, is more than I might've imagined with two products that by all accounts are pretty damn similar.Yeah.[00:31:22] swyx: No fight there. Uh, I will say I do think that Codex is still in like a catch up. Like in terms of personal experience. Um, the only thing I like out of, out of Codex is the, is like Spark and like yeah. Uh, the, I, I feel like the skills integration is a little bit better. I feel like, uh, the, the speed is a bit better.Maybe ‘cause it's in, is written in rust or whatever. Um, very minor things that you like. Almost like telling yourself rather than like objectively assessing between two, two of them. I, I, I do think, like vibes wise, I think that's going on. Um, the, the, you know, I, I feel like the, the missing questions, uh, in, in this whole debate is like, why is this so concentrated in only two names, right?Yeah. Like, um, how, where, like, where is the Gemini? You know, presence, where's the Xai presence? Um, and like they are trying, it's just they haven't made that much progress yet.[00:32:12] Jacob Effron: But what the, what the Claude Co moment does show, and it actually in some ways makes you a little more bullish on the potential for someone else to catch up because it does feel like if you're the first person to introduce some magical net new product experience, that that actually might be stickier than one might have imagined.[00:32:27] swyx: Right, right, right. Okay. Yeah.[00:32:28] Jacob Effron: And so it's, everyone can believe they have shot[00:32:29] swyx: that. What do you think that new product experience might be like? I, I, it's, it's like, and this is a failure of imagination on my part. Like, I always wonder, like, people always say this like, well, the, the thing that will save us is like being first to the next new thing.Like what is it?[00:32:41] Jacob Effron: Yeah.[00:32:42] swyx: It's like,[00:32:45] Jacob Effron: I dunno, something around like, uh, consumer agent, computer use, like hybrid. I think, obviously, I think we're like scratching the surface on the consumer side.[00:32:53] swyx: So my, my current theory is like the. Open claw is like a vision of things to come.[00:32:58] Jacob Effron: Totally.[00:32:58] swyx: Um, and uh, it's good that O open I has like the association with open claw, but by no means do they have the rights to win it.The general thesis that I have been pursuing now is that the year the same way that 2025 was the year of coding agents, 2026 is coding agents breaking containment to do everything else. Um, and so coding agents continue to still win, but because they generate software and software eats the world, so like, it's kind of like the trans.Associated property of like software, eat the world, coding agents, eat software, therefore coding agents eat the world. Um, which is like an interesting,[00:33:30] Jacob Effron: yeah, and breaking containment always an easier phase phrase in the consumer context than the enterprise one. You've seen people run these really cool, uh, experiments in their own personal lives.I think like,[00:33:37] swyx: yes.[00:33:38] Jacob Effron: Figuring out, you know, how you, obviously everyone's focused, you know, on the enterprise side now around how you create these experiences. I feel like the vibes, you know, people love to have these narratives of like, everything is completely shifted. It's like I actually, you know, open AI.Organizationally, uh, you know, volatility aside is, you know, great products, great team, great models like everyone else in the world is incentivized for there to be. Two, three more. Everyone would love more like great model companies. And so I feel like the, the natural forces of the world revolt when any one company, you know, is too much the star of the show, right?There's so many people in the ecosystem that are incentivized for that not to happen. And so I think I'd be shocked if we don't have. Uh, uh, reversion of vibes, not maybe completely the other way, but at least a little bit more equal at some point over the next six, 12 months.[00:34:24] swyx: I, I think there's just a kind of different stages when, when you talk about the world, one wanting more model companies, I talked think about like the neo labs.[00:34:30] Jacob Effron: Yeah.[00:34:31] swyx: And I mean, I don't know, is it fair to say none of them have really broken through in the past year?[00:34:35] Jacob Effron: I think that's totally fair,[00:34:37] swyx: which is rough. Um, and well, how are we gonna, how are we gonna grow that diversity in, in, in choice, like. Um, that's, this is it.[00:34:46] Jacob Effron: Yeah. It'll be really interesting to see what, what, what ends up happening with that.And you've seen, you know, folks like Nvidia, you know, very incentivized to make sure there's, there's a broader platform of, of other model providers.[00:34:57] swyx: I think, uh, I don't know people say this, but I, I, I don't think they try it hard. Nvidia tries harder to build neo clouds[00:35:05] Jacob Effron: Yeah.[00:35:06] swyx: Than neo labs.[00:35:07] Jacob Effron: Well, they try pretty damn hard to build neo Cloud, so[00:35:09] swyx: that's,[00:35:09] Jacob Effron: yeah.[00:35:10] swyx: But like, you know, let's call it like the, the core weaves of the world, much happier place in the, you know, than any neo lab built on top of them.[00:35:18] Jacob Effron: Yeah. That one might argue it's, it's easier to, to enable a neo cloud to be successful than it is. Uh, you can't will a neo lab into existence the same way you, soNvidia[00:35:25] swyx: has more direct control over it.Uh, for sure.[00:35:27] Jacob Effron: What else is kind of catching your eye today on the startup side? I mean, you worry, there's obviously this whole narrative of like, you know, the foundation models, you know, they announced a product and every stock goes down 15%. Like[00:35:36] swyx: Yeah.[00:35:37] Jacob Effron: Do you, do you worry about the foundation models just kind of eating into to a bunch of these startup categories?[00:35:43] swyx: Not really. I, I think actually like. As, uh, there's, there's, okay, there's, there's, there's the, there's the point of view of like being an investor in startups, and there's a point of view of like, do you wanna start something? And I think honestly, like the, the downside for all these is so. Minimal in, in a sense of like, the worst you do is you just get hired into one of these labs anyway.So I, I think the, the market for people who just do things and try things and try to execute in like a competent way, even if like it doesn't work out commercially, even if it just wasn't that great anyway. Like, but like that's your job interview to go into, into one of these things anyway, so, um, I don't feel that.From a, from a very, very small startup perspective, mid-size startups. Yes. Uh, I will say there's been a lot of dead, um, LM Infra, a lot of LM infra consolidation like the, the, uh, lang fuses of the world getting absorbed into, into click house. And I, I think. Like people have maybe worked out the domain specific playbook, uh, and like, I think that's okay.Um, and, and yeah, I'm not that, not that worried about, uh, okay. So, um, I, I would say I'd be more worried about traditional SaaS, like low NPSS. This is the whole AI versus SaaS debate that has, that's been going on. Uh, and, and like literally I'm going through that exact thing in my company where, so I like kind of.Thinking through this on a very visceral, visceral level, right? On one hand you have the people who say you vibe coders don't appreciate the amount of work that goes into A-A-C-R-M and like, yeah, you think you can rip out Salesforce? So did the 30 entrepreneurs before you, right? Like, like, you know, you classically underestimate the things that you don't.Deeply, no. And, and, and target audience is not you. Uh, at the same time, like we have never been able to build software so easily and customize software so easily and like Yeah, you're not gonna use 90% of the things in Salesforce. So like, yeah. What's the typical, so what have you, what[00:37:33] Jacob Effron: have you done internally?[00:37:34] swyx: So we have there the main SaaS that we do for event management and sponsor management. That's, and we paid 200 KA year for that. Not, not huge, but like chunky for, for, for my, my scale. Um, and like, yeah, I could probably spend 2000 and, and build like a custom version of that. Um, the, the, the trick has been dealing with my, the rest of my team and getting them on board.Yeah. ‘cause I'm the most ethical person on my team, but like, I can't make that decision myself. And I think in the same way I've been telling with other CEOs team leaders as well, it's like, well you can be super cloud pilled. You can be super LM psychosis and that you think that's okay, but you like you have to bring your team with you.And I think like there, the sort of widening disparity in LM psychosis in companies is causing real s real riffs because. And on one hand, on one hand, the people who are less AI native are not getting with the picture. They're not, they're actually like behind, they're actually not waking up to the fact that like you, everything you think is necessary is not actually that necessary.And in fact, exactly would be better of you if you just like held your nose and went in and when came out the other side. Yeah, only talking to agents in natural language and like your life would actually be better and you just, you're just like close-minded. There's that perspective. The other perspective is, oh, you vibe coder.You, you did this in a weekend and you got the 80% solution and now the rest of your employees. Have to pick up the rest of your s**t, right, that you, that you thought you were, you were such hot, amazing, uh, uh, at, but like, actually you didn't figure it out. And like, actually LMS are still useless at this and blah, blah, blah.So like, I think there's this huge debate going on in every company right now. Um, and like, um, you know, I have a small microcosm of it, but like, yeah, it, it's making me hesitate to, to pull the trigger. But like I will at some point, it's like maybe I've put it off for one year, but not like five. Yeah, but like, so, so like SaaS is definitely getting squeezed.Um, it does make me wonder, like, I, I do think that there's an opportunity for a more AI native, um, system of record thing that is not just Postgres. Um, or not just MongoDB, although both are very good. Maybe it's like a convex or like people Yeah. Bring up convex a lot. I don't know, like, like, I, I just feel like the sort of quote unquote firebase of, of AI apps isn't really a thing yet.Um, beyond what we have. Uh, which, which is fine. It's, it's, it's just. We could probably start in a more sort of rapid iteration cycle first before scaling up to like a Postgres or MongoDB, which are more sort of old tech. I was at a dinner with, uh, Mike Krieger, the CPO of en philanthropic, and, and he, we were just kind of going around the room going like, what are people most worried about?Yeah. And, uh, for me, uh, I, instead of security, I brought up biosafety. Yeah,[00:40:21] Jacob Effron: classic.[00:40:22] swyx: Um, actually, like I said, it was. Cliche and classic, and the rest of the table were, were like, what do you mean? Someone sitting at home can manufacture a virus that wipes out half of humanity,[00:40:32] Jacob Effron: almost like the OG Jeffrey Hinton.Like, this is why you should be scared.[00:40:35] swyx: I'm like, yeah, like the read the, you know, risk reports. Like this is like the thing. Um, I think, and Mike was just sitting there knowing he was sitting on Mythos and going like, actually it's security. Um, and I think like, um, I think the, there's, there's, part of it is.A very good marketing. Like too good. Yeah, like I would actually advise and topic to tune down the marketing because also it's, it is just a very good model and you don't have to make so many marketing claims around it. At the same time, it is not really a private model. If you give it to 40 companies.Each of whom have like 10,000 employees or whatever. Right. It's not, it's not private, it's, it's like there's bad actors in there.[00:41:18] Jacob Effron: Yeah. Hopefully, hopefully not as, uh, as bad as releasing it widely, but, uh, no, I mean, it's an interesting. You know, it's an interesting case study for how all, I mean, many model releases might, I mean, you know, this might be the first model release that looks like the rest of ‘em from from now on, right?[00:41:31] swyx: It, it, so it's, it's the, there's an overall product strategy, uh, for anthropic of like bundle, uh, you know, restrict access bundle, uh, product with model maybe.Whereas, uh, OpenAI has definitely been a lot more sort of. Philosophically aligned on like, we will just enable access everywhere and we don't know what you, what will come out of it. Right.[00:41:51] Jacob Effron: Right. Though, I mean, this current moment, uh, obviously the cynical take is also just ties to the amount of compute that both companies[00:41:56] swyx: Yeah.Right, right, right. Yeah, I think, I think that's true. I I do think like the, the, this is the, the, the scale, the dawn of like larger than 10 trillion parameter models is very interesting. I don't think it, I think it's a temporary phenomenon because we have much larger compute clusters coming online for everyone over the next like three, five years.It's, and this is like already written in, in the cards.[00:42:18] Jacob Effron: Yeah.[00:42:19] swyx: So to the extent that like, you know, will we have rationing of models, uh, above 10 trillion, uh, in like two years? I don't think so. I think everyone will have no, we'll just[00:42:29] Jacob Effron: have rationing of the next phase.[00:42:30] swyx: Right. Right. But like, that's as it should be almost like, um.My, my classic example, which I, this is just me theorizing, not anything confirmed by Google. When Google announced Gemini, they actually announced three sizes, which was Flash Pro Ultra. They never released Ultra. They only have Pro and Flash. Um, so my theory is they have ultra sitting in a basement and they just could distilling from it for, for flashing pro.Um, which like, yeah, I mean, I, I actually think that's. As it should be for any lab that they, that they do that.[00:43:02] Jacob Effron: Yeah. Just because those are the models that people actually wanna end up using. And it's just like cost prohibit.[00:43:06] swyx: It is more, yeah, it's cost. Yeah. It's, it's not the want, it's just, just, just the cost.Um, I do think, like, uh, it is interesting that, uh, for a while I was, I was considering the theory that models capped out at two, 2 trillion, and I think that's proving to be wrong. And well then if I'm wrong, how wrong? How wrong am I? Do we do 200 trillion? Do we do two quarter trillion, whatever? Um, and I don't think we have the straight answer to that, but like, uh, it's interesting that we are continuing to scale number of pers when everyone kind of assu like can see that we're not going to get like the next thousand or 1 million x from this paradigm.So like the others, like the alias of the world are working on other. Um, model architecture improvements. We need a different scaling law, I guess, because like, we're, I, I feel like people already already feel like we're tapped out on this. Like the, the end, the end state of this is we turn most of the world into data centers and like, I don't know.I don't know if we want that.[00:44:08] Jacob Effron: Yeah, I mean, uh, if the, if, if, if the return of intelligence are there, maybe, uh, maybe not so bad.[00:44:13] swyx: I, I, I think there, there's just a sheer amount of like, like un scalability that like is wrangling people's sensibilities right now. Um, especially in terms of like context lengths.Um, my classic quote is that context length is like the slowest scaling factor in, in lms.[00:44:30] Jacob Effron: Yeah.[00:44:30] swyx: Um, we, like, we took maybe. Three years to go from like 4,000 context length to a million and that's about it. Yeah. Like Gemini has had a million token context length for two years now. Um, and no one's using it.Like, so like yeah, it's memory. Memory is probably gonna be the, the biggest limiting constraint on all these things.[00:44:50] Jacob Effron: Yeah. Certainly seems that way. I guess I'm curious over the last year since you recorded last, like what's one thing you've changed your mind on?[00:44:57] swyx: I feel like I was kind of bearish on open models like last year.Um, in a sense of, like, I, I had just done the podcast with an Al[00:45:07] Jacob Effron: Yeah.[00:45:08] swyx: Of Braintrust where he, and he, I mean, you know, he has a good cross section of all the top AI companies and he says market share of open source is 5% and going down. Um, I think that's changed. I think it's going up. Um, and even if,[00:45:22] Jacob Effron: even though the capability gap does seem to be increasing.Spending on the[00:45:26] swyx: time. It's hard to tell. Yeah, it's, it's really hard to tell. ‘cause like, okay, for, for listeners, capability gap increasing is like on public benchmarks. And let's say you're comparing mythos versus like, I don't know, G-T-O-S-S or like GLM 5.1. And, um, it's, it is really hard to tell. ‘cause even if they were closing, you will also not believe that they were closing that much because it's very easy to gain the benchmarks.Yeah. So you just don't really, really know. Um, all you know is like. Uh, there's somewhat objective open router stats on like what people choose in a free market. And people do choose some of these open models in significant volume, except that a lot of them are heavily discounted. So you need to kind of like price adjust, uh, these things.So even if, even if that were true, which I, I'm not sure, like I, I, I feel like the numbers just up now instead of down. Uh, I think the. Separation between what the top tier agent labs

Dave "CAC" Kellogg and Ray "Growth" Rike discuss the ICONIQ 2026 State of GTM Report, a 32-page benchmark study based on a January 2026 survey of 155+ B2B SaaS executives across CROs, CEOs, and RevOps leaders. The pair digs into what the data says about how high-growth companies go to market differently, how usage-based pricing is reshaping sales compensation, and where AI in the GTM stack is actually delivering results versus falling short.Topics CoveredGTM Motion Mix: Top-Down vs. Bottom-Up vs. Hybrid. The data shows roughly 60% of companies use a hybrid motion, but high-growth companies skew more toward bottom-up and PLG. Ray and Dave unpack the ICONIQ "variable growth bar" definition and what the motion mix signals about the source of growth.Channel and Partnership Revenue Is Bigger Than Expected. ICONIQ reports channel partnerships representing 27-31% of revenue for high-growth companies. That is well above the 11-15% Ray typically sees in comparable reports. Dave calls it the long-awaited comeback of channel in SaaS, and both hosts flag the near-absence of self-serve as a surprise.Quota Setting and Commission Structures in a Usage-Based World. For the first time in a major GTM benchmark, ICONIQ covers how companies set quotas and structure commissions in a consumption and outcome-based pricing environment. 30% of respondents use forecasted consumption to set quota. Commission payout timing is split across four models, signaling how unsettled the go-to-market compensation playbook remains.Clawbacks Are Back. With usage-based and prepaid consumption models on the rise, 45-50% of companies now have clawback provisions in sales compensation. Ray and Dave discuss why clawbacks are a morale killer for sales teams and what the smarter alternative looks like in practice.POC and Free Trial Conversion Rates. POC-to-paid conversion improved from 36% to 50% year over year. Ray and Dave discuss resource allocation for proof-of-concepts, including dedicated versus shared solution architects, and raise the question of where forward-deployed engineers fit into the picture.AI in GTM: Where It Is and Isn't Working. Lead gen and call transcription top the adoption charts, but AI-driven forecasting sits at only 38%. Ray flags the gap between AI-native and traditional SaaS companies in GTM AI adoption. Dave points to slide 30 as a reality check: pipeline efficiency and unit economics are not yet showing meaningful improvement from AI investment.If you are responsible for GTM strategy, sales compensation, or measuring the ROI of AI investments, this episode gives you a practical lens on one of the best benchmark reports published in 2026. Ray and Dave go beyond summarizing the slides. Dave and Ray flag caveats in the methodology, challenge the data where it warrants scrutiny, and connect the findings to real-world operating decisions on quota design, commission structures, channel strategy, and AI adoption. If you only have time for one GTM benchmark deep-dive this year, this is the episode to start with.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Nikhyl Singhal is the founder of The Skip, a community for senior product leaders; a former product exec at Meta, Google, and Credit Karma; and a many-time founder. He's also one of the most honest, unfiltered voices on what's actually happening in product management right now.In our in-depth conversation, we discuss:1. Why the next two years will be the most chaotic period in product management history2. Why half of current product managers are at risk, and what separates those who'll do well3. Why you need to find your “moments of joy” with AI4. The “smiling exhaustion” he's seeing across the product community5. The psychological barriers that prevent people from reinventing themselves6. Why your resume's fancy logos matter less than ever, and what matters now7. His prediction that companies will shed 30,000 people and rehire 8,000—all AI-first—Brought to you by:WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUsVanta—Automate compliance, manage risk, and accelerate trust with AI—Episode transcript: https://www.lennysnewsletter.com/p/why-half-of-product-managers-are-in-trouble—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Nikhyl Singhal:• LinkedIn: https://www.linkedin.com/in/nikhyl• X: https://x.com/nikhyl• Podcast & Newsletter: https://skip.show• Skip Community: https://skip.community• Skip Coach: https://skip.coach• Skip.help: https://skip.help—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Nikhyl Singhal(02:25) The big picture: what's changing for product managers(10:00) Are product leaders doing better than 2-3 years ago?(11:44) What will change in the next couple of years(14:23) How companies are changing the way they build products(15:51) What “judgment” really means for PMs(17:46) Why there won't be any more bad software(20:25) The skills you need to be effective today(23:31) Why there are more PM roles than ever(24:27) The builder versus information-mover divide(30:14) The non-builder problem(30:53) Should PMs code?(34:15) Why experienced leaders still matter(35:44) The diversity setback nobody's talking about(37:21) Why your brand doesn't matter as much anymore(39:54) How valued skills are flipping upside down(40:49) Why change is so hard for humans(43:53) The “equal disappointment” algorithm(46:39) You must cross the threshold(48:37) This chaos will settle(53:19) Finding your moment of joy(58:50) Nikhyl's AI stack and what he's building(1:00:53) The obsolescence mindset(1:05:24) Specific advice for PMs right now(1:08:58) The four jobs that will exist in the future(1:11:59) Why alignment is changing (but not disappearing)(1:15:40) How engineering is changing even more than PM(1:17:04) The surprising design plateau(1:18:49) Finding optimism in the chaos(1:21:12) Lightning round—Referenced:• Building a long and meaningful career | Nikhyl Singhal (Meta, Google): https://www.lennysnewsletter.com/p/building-a-long-and-meaningful-career• COBOL: https://en.wikipedia.org/wiki/COBOL• United Airlines: https://www.united.com• State of the product job market in early 2026: https://www.lennysnewsletter.com/p/state-of-the-product-job-market-in-ee9• Head of Growth (Anthropic): “Claude is growing itself at this point” | Amol Avasare: https://www.lennysnewsletter.com/p/anthropics-1b-to-19b-growth-run• Demis Hassabis on X: https://x.com/demishassabis• Sam Altman on X: https://x.com/sama• Dario Amodei on X: https://x.com/DarioAmodei• Cross on Prime Video: https://www.amazon.com/Cross-Season-1/dp/B0D6X7ZZHC• Jack Ryan on Prime Video: https://www.amazon.com/Tom-Clancys-Jack-Ryan/dp/B0CNDCMN8R• 24 on Prime Video: https://www.amazon.com/24-Season-1/dp/B000HPF85A• Claude Code: https://code.claude.com• Codex: https://chatgpt.com/codex• Lovable: https://lovable.dev• Sonos: https://www.sonos.com• “There are only four jobs” on X: https://x.com/yrechtman/status/2039012253341495462• Paradise on Hulu: https://www.hulu.com/series/paradise-2b4b8988-50c9-4097-bf93-bc34a99a5b4f• Lioness on Paramount+: https://www.paramountplus.com/shows/lioness• Tesla: https://www.tesla.com• Albert Einstein's quote: https://www.goodreads.com/quotes/115696-genius-is-1-talent-and-99-percent-hard-work—Recommended books:• James: https://www.amazon.com/James-Novel-Percival-Everett/dp/0385550367• The Adventures of Huckleberry Finn: https://www.amazon.com/Adventures-Huckleberry-Finn-Unabridged-Uncensored/dp/195483943X—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

In this episode, Jordan Crawford outlines how revenue leaders can build an autonomous pipeline generation engine and transition away from static playbooks. The discussion covers practical methods for extracting competitor usage data, identifying segments of users who are far more likely to buy, and generally how top GTM teams are using GTM Engineering to dramatically improve their results. The hosts also evaluate how AI tools alter revenue operations, shift the traditional B2B SaaS go-to-market strategy, and force a total redesign of traditional AE compensation models. Key Takeaways: Top-down executive mandates fail because leadership lacks hands-on experience with the required technical systems. Jordan Crawford notes the absurdity of this disconnect, stating, "You read these letters from all these CEOs and they're like, 'We need to be an AI first organization and I can't tell you what that means or how to implement it, but goddamn, you need to be able to do it.'" The core function of revenue operations must shift from administrative reporting tasks to running active market tests. Sam Jacobs explains this organizational friction, observing many Rev Ops employees think their job is still to deliver reports to the C-Suite and ensure data accuracy, when today's reality is that "actually your job now is to generate demand and like I need 50 campaigns tested by tomorrow." Identifying high-value target accounts requires prioritizing product telemetry and user actions over static CRM fields. Jordan highlights the power of this approach, explaining that "AI can basically analyze customers' words, actions, and what's in the CRM and say... these accounts are worth 10 times more than these accounts." Connect with the Hosts & Guests: Host: Sam Jacobs - https://www.linkedin.com/in/samfjacobs/  Host: AJ Bruno - https://www.linkedin.com/in/ajbruno3/  Host: Asad Zaman - https://www.linkedin.com/in/azaman1/  Guest: Jordan Crawford - https://www.linkedin.com/in/jordancrawford/ Topline is more than a YouTube Channel: Subscribe to Topline Newsletter: https://www.joinpavilion.com/topline-newsletter  Tune into Topline Podcast, the #1 podcast for founders, operators, and investors in B2B tech: https://www.joinpavilion.com/topline-podcast J Join the free Topline Slack channel to connect with 600+ revenue leaders to keep the conversation going beyond the podcast: https://www.joinpavilion.com/topline-slack Chapters: 00:00 Podcast Episode Introduction  02:52 Reversing GTM Strategy  06:44 RevOps and AI Thought Partners  13:58 Executing Fast Sales Campaigns  18:25 AI For Comp Plan Benchmarks  21:04 Defining Pain Qualified Segments  26:42 Shifting RevOps Priorities  38:41 Unbundling B2B SaaS Jobs  39:21 The Topline Trivia Game  45:11 Top Of Funnel AI Tactics  46:43 Middle Of Funnel Telemetry  47:56 Bottom Of Funnel Contract Review  49:10 Bull/Bear Predictions  

In this episode of The Ross Simmonds Show, Ross drops new research across 8,566 high-priority B2B SaaS keywords that reveals how Reddit is quietly dominating bottom-of-funnel search, including the most expensive CPC terms in your Google Ads account. He breaks down what the data actually means for your pipeline and the exact playbook operators need to respond before the gap widens further. Key Takeaways and Insights: 1. How Reddit Changed the SEO Game -Google elevated user-generated content after seeing demand for people-first answers, and Reddit threads now outrank product and category pages across B2B SaaS. -Traditional SEO playbooks built on volume and backlinks are losing ground. The shift is not theoretical. It is happening in your highest-value queries right now. 2. The Scoreboard: Reddit's Share of the Top 3 -Reddit commands 40 to 45 percent of top-three rankings across most B2B SaaS verticals. In three of four industries analyzed, Reddit consistently beat all competitors. -Even established review sites are losing ground to subreddit threads. The brands still ignoring this are handing over pipeline. 3. Myth: Reddit Only Wins on Review Terms -Reddit wins 94 percent of the time for "best software" queries, but 77 percent of Reddit's wins come from non-review, demand-gen keywords. -Reddit is not just stealing review traffic. It is influencing pipeline at every stage of the buyer journey. 4. The CPC Paradox -At $15 to $20 CPC, Reddit wins 45 percent of the time. At $50-plus CPC, that number hits 67 percent. -You are paying $50 per click while a two-year-old Reddit thread captures the organic click above you. 5. Authority Alone Does Not Win Anymore -High domain authority no longer guarantees rankings. Brands that win build long-tail infrastructure aligned to real customer queries. -Long-tail blog content consistently outperforms thin category pages. The edge goes to operators who build depth, not just links. 6. Subreddits Are the Real Competitors -It is not Reddit as a monolith. It is specific communities. The CRM subreddit showed a 49 percent win rate. r/EmailMarketing hit 68 percent. r/SmallBusiness drove nearly 141K monthly searches across tracked queries. -One subreddit can dominate an entire B2B category. These are your real competitors. 7. Long-Tail Is Where Reddit Dominates -For keywords with six or more words, Reddit's win rate hits 87 percent. Years of user questions created a library of hyper-specific content that is nearly impossible to replicate overnight. -That long-tail depth fuels both Google rankings and LLM citations. If you are not building long-tail assets, you are invisible in AI search. 8. Reddit Now Shapes LLM Visibility -B2B buyers use peers, communities, and LLMs to validate decisions, and LLMs are citing Reddit more than ever. -If you are absent from key subreddits, you likely do not exist in AI-generated answers either. Reddit presence influences both the SERP and the model. 9. The Operator Playbook for Winning on Reddit -Run a keyword gap analysis against reddit.com. Identify three to five subreddits consistently outranking you. Engage with value, not pitches. Earn credibility first. -Invest in high-quality educational content and measure sentiment and LLM visibility as part of your growth system. This is how you build presence that compounds. Resources & Tools:

The CPG Guys are joined in this episode by Christian Hasshold, host of the In/Organic Podcast and Managing Partner of Resilient Edge M&A Advisors.Follow Christian on LinkedIn at: https://www.linkedin.com/in/hassold/Follow the In/Organic podcast on Apple Podcasts at: https://podcasts.apple.com/us/podcast/inorganic-podcast/id1710070954Follow the In/Organic podcast on Spotify at: https://open.spotify.com/show/0oa6R4M37vtys5II6clEfF?si=7fdee69be69747f4Christian answers these questions:How do you distinguish between a “nice-to-have” tuck-in acquisition and a transformational, growth-enabling deal in the lower/middle markets?In the lower-to-middle market, what characteristics make an agency or SAAS target attractive to PE buyers versus strategic acquirers? Are there red flags you consistently see?What valuation levers tend to move the needle most for digital agencies and B2B SaaS in private equity? In what scenarios do earnouts or seller financing play a critical role in aligning incentives post-close? What's a must-have due diligence checklist for an M&A in this space (e.g., tech stack, client concentration, monetization models, recurring revenue quality)? Any surprises you often encounter? After closing, what are your top three priorities to de-risk integration and accelerate value realization in a digital agency or B2B SaaS platform?How do you evaluate and incorporate AI and data-driven product improvements in these companies to unlock scale? Are there specific bets you'd avoid in early-stage growth scenarios?For CPG brands and their agencies, what practical lessons can you share from the M&A and growth playbook that help them become more attractive to PE-backed buyers or to strike better LP-aligned partnerships? CPG Guys Website: http://CPGguys.comFMCG Guys Website: http://FMCGguys.comSheCOMMERCE Website: https://shecommercepodcast.com/Rhea Raj's Website: http://rhearaj.comLara Raj in Katseye: https://www.katseye.world/DISCLAIMER: The content in this podcast episode is provided for general informational purposes only. By listening to our episode, you understand that no information contained in this episode should be construed as advice from CPGGUYS, LLC or the individual author, hosts, or guests, nor is it intended to be a substitute for research on any subject matter. Reference to any specific product or entity does not constitute an endorsement or recommendation by CPGGUYS, LLC. The views expressed by guests are their own and their appearance on the program does not imply an endorsement of them or any entity they represent.CPGGUYS LLC expressly disclaims any and all liability or responsibility for any direct, indirect, incidental, special, consequential or other damages arising out of any individual's use of, reference to, or inability to use this podcast or the information we presented in this podcast.

Lenny's Podcast: Product | Growth | Career ✓ Claim : Read the notes at at podcastnotes.org. Don't forget to subscribe for free to our newsletter, the top 10 ideas of the week, every Monday --------- Keith Rabois was an early executive at PayPal (part of the famous PayPal Mafia), COO at Square, VP of Corporate Development at LinkedIn, and an early investor in Stripe, DoorDash, Airbnb, YouTube, Ramp, and Palantir. Currently he's managing director at Khosla Ventures. Also, he hasn't touched a computer since September 2010 (he does everything from an iPad).In our in-depth conversation, Keith shares:1. The barrels vs. ammunition hiring framework (and how to spot barrels)2. Why talking to customers is actively harmful for consumer products3. How to identify undiscovered talent4. Why the PM role is dying5. The three traits of the best-performing companies right now6. The specific interview question he asks every senior candidate7. Why CMOs (not engineers) are becoming the #1 consumer of tokens—Brought to you by:WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUsVanta—automate compliance, manage risk, and accelerate trust with AI—Episode transcript: https://www.lennysnewsletter.com/p/hard-truths-about-building-in-the-ai-era—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Keith Rabois:• X: https://x.com/rabois• LinkedIn: linkedin.com/in/keith• Website: https://www.khoslaventures.com—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Keith Rabois(01:59) Why Keith hasn't used a computer since 2010(04:52) The team you build is the company you build(07:40) How Keith learned to identify talent at PayPal(10:05) Tactics for getting better at hiring(15:31) The barrels vs. ammunition framework(18:52) What makes someone a barrel(22:36) How to attract the best talent(26:18) Building companies on undiscovered talent(27:53) Why better performance requires more pressure(32:36) Career advice in the age of AI(35:14) The future of the product triad(41:03) Why design and code are merging(49:35) What practicing law taught Keith about entrepreneurship(51:22) Contrarian takes on customer feedback(1:02:33) Identifying great AI opportunities(1:05:13) Advice for evaluating statrups (1:12:36) Criticizing in public vs. private(1:15:05) Failure corner(1:17:29) Lightning round—Referenced:• Square: https://squareup.com• Jack Dorsey on X: https://x.com/jack• Head of Claude Code: What happens after coding is solved | Boris Cherny: https://www.lennysnewsletter.com/p/head-of-claude-code-what-happens• Simon Willison's Weblog: https://simonwillison.net• Vinod Khosla on X: https://x.com/vkhosla• Peter Thiel on X: https://x.com/peterthiel• Max Levchin on X: https://x.com/mlevchin• David Sacks on LinkedIn: https://www.linkedin.com/in/davidoliversacks• Tony Xu on X: https://x.com/t_xu• David Sze on X: https://x.com/davidsze• Faire: https://www.faire.com• Max Rhodes on X: https://x.com/MaxRhodesOK• Jeffrey Kolovson on LinkedIn: https://www.linkedin.com/in/jeffreykolovson• Uncapped | Comparative Advantages w/ Keith Rabois: https://www.khoslaventures.com/posts/uncapped-comparative-advantages-w-keith-rabois• Lattice: https://lattice.com• Taylor Francis on LinkedIn: https://www.linkedin.com/in/taylor-francis-4ba49640• Building product at Stripe: craft, metrics, and customer obsession | Jeff Weinstein (Product lead): https://www.lennysnewsletter.com/p/building-product-at-stripe-jeff-weinstein• The art of hiring: insights from Khosla Ventures, Airbnb, Ramp and Traba: https://ramp.com/velocity/the-art-of-hiring-insights• Eric Glyman: Seek out super individual contributors (ICs): https://ramp.com/velocity/the-art-of-hiring-insights#Eric-Glyman:-Seek-out-super-individual-contributors-(ICs)• Eric Glyman on X: https://x.com/eglyman• Mike Moore on LinkedIn: https://www.linkedin.com/in/mike-moore-802223177• Brian Chesky's new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach• Why you should work much harder RIGHT NOW: https://marginalrevolution.com/marginalrevolution/2026/03/why-you-should-work-much-harder-right-now.html• Opendoor: https://www.opendoor.com• The Craft of Early Stage Venture | Peter Fenton, General Partner at Benchmark | Uncapped with Jack Altman: https://www.youtube.com/watch?v=vRiblwiXt-Q• Lovable: https://lovable.dev• The rise of the professional vibe coder (a new AI-era job) | Lazar Jovanovic (Professional Vibe Coder): https://www.lennysnewsletter.com/p/getting-paid-to-vibe-code• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (co-founder and CEO): https://www.lennysnewsletter.com/p/building-lovable-anton-osika• Marc Andreessen: The real AI boom hasn't even started yet: https://www.lennysnewsletter.com/p/marc-andreessen-the-real-ai-boom• Jeremy Stoppelman on X: https://x.com/jeremys• The design process is dead. Here's what's replacing it. | Jenny Wen (head of design at Claude): https://www.lennysnewsletter.com/p/the-design-process-is-dead• Andy Warhol: https://en.wikipedia.org/wiki/Andy_Warhol• Curation and Algorithms: https://stratechery.com/2015/curation-and-algorithms• Ernest Hemingway: https://en.wikipedia.org/wiki/Ernest_Hemingway• William Shakespeare: https://en.wikipedia.org/wiki/William_Shakespeare• Evan Moore on X: https://x.com/evancharles• Andrew Mason on X: https://x.com/andrewmason• Read Taylor Swift's Full Viral Speech After Record-Breaking Awards Sweep: https://www.newsweek.com/entertainment/read-taylor-swift-full-acceptance-speech-record-breaking-awards-sweep-11745941• The Chainsmokers: Stories Behind the Songs, AI's Impact on Music, and Venture Investing | Uncapped with Jack Altman: https://www.youtube.com/watch?v=9GMSC-2pYnw&list=PLtpH7YnTL8ihy0nR2BV32n5VkRtqlDAS1&index=16• How to spot a top 1% startup early: https://www.lennysnewsletter.com/p/how-to-spot-a-top-1-startup-early• David Weiden on LinkedIn: https://www.linkedin.com/in/davidweiden• Alfred Lin on LinkedIn: https://www.linkedin.com/in/linalfred• Keith's post about vertical integration on X: https://x.com/rabois/status/870673635375104000• Jon Chu on X: https://x.com/jonchu• Kanu Gulati on X: https://x.com/KanuGulati• Rogo: https://rogo.ai• Profound: https://www.tryprofound.com• Basis: https://www.getbasis.ai• Spellbook: https://www.spellbook.legal• Roelof Botha on X: https://x.com/roelofbotha• Delian Asparouhov on LinkedIn: https://www.linkedin.com/in/delian-asparouhov-87447742• Lessons From Keith Rabois, Essay 1: How to become a Venture Capitalist: https://delian.io/lessons-1• Velocity over everything: How Ramp became the fastest-growing SaaS startup of all time | Geoff Charles (VP of Product): https://www.lennysnewsletter.com/p/velocity-over-everything-how-ramp• Nuremberg on AppleTV+: https://tv.apple.com/us/movie/nuremberg/umc.cmc.3sg4y0382byupy76bfy7307k4• Eight Sleep: https://www.eightsleep.com• “NO DAYS OFF”—Bill Belichick on X: https://x.com/SNFonNBC/status/829036279069364224—Recommended books:• Creativity, Inc.: Overcoming the Unseen Forces That Stand in the Way of True Inspiration: https://www.amazon.com/Creativity-Inc-Overcoming-Unseen-Inspiration/dp/0812993012• The Jordan Rules: The Inside Story of One Turbulent Season with Michael Jordan and the Chicago Bulls: https://www.amazon.com/Jordan-Rules-Sam-Smith/dp/0671796666• The Upside of Stress: Why Stress Is Good for You, and How to Get Good at It: https://www.amazon.com/Upside-Stress-Why-Good-You/dp/1101982934—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Matthew Sciannella breaks down what separates high-performing B2B go-to-market teams from the rest — using Ramp and Clay as real-world case studies. From Ramp's Super Bowl surround-sound strategy to Clay's disciplined mid-market segmentation bet, this episode pulls back the curtain on the GTM principles any B2B SaaS team can steal.

In this final bonus episode recorded at the recent Illumia Momentum conference, we talked with Jennifer Chellew from Illumia about their rebrand, how they managed the transition, and the detailed process that went into to everything from choosing a name, choosing colors, to building consensus in a large community of stakeholders. Guest Name: Jennifer Chellew - SVP of Marketing at Illumia Guest Social: LinkedIn Guest Bio: Jennifer Chellew is SVP of Marketing at Illumia, where she leads brand, demand generation, analytics, and marketing operations for a high-growth B2B SaaS company. With more than 25 years of experience across healthcare technology, financial services, and B2B SaaS, Jennifer is known for building marketing organizations that drive pipeline growth and earn executive buy-in through a data centered and operationally sound approach. Her career includes senior marketing leadership roles at Sallie Mae and JP Morgan Chase. Jennifer holds a B.S. in Business Administration from the University of Delaware and is an active community leader in the Delaware area. - - - -Connect With Our Host:Dustin Ramsdellhttps://www.linkedin.com/in/dustinramsdell/About The Enrollify Podcast Network:The Higher Ed Geek is a part of the Enrollify Podcast Network. If you like this podcast, chances are you'll like other Enrollify shows too!Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Keith Rabois was an early executive at PayPal (part of the famous PayPal Mafia), COO at Square, VP of Corporate Development at LinkedIn, and an early investor in Stripe, DoorDash, Airbnb, YouTube, Ramp, and Palantir. Currently he's managing director at Khosla Ventures. Also, he hasn't touched a computer since September 2010 (he does everything from an iPad).In our in-depth conversation, Keith shares:1. The barrels vs. ammunition hiring framework (and how to spot barrels)2. Why talking to customers is actively harmful for consumer products3. How to identify undiscovered talent4. Why the PM role is dying5. The three traits of the best-performing companies right now6. The specific interview question he asks every senior candidate7. Why CMOs (not engineers) are becoming the #1 consumer of tokens—Brought to you by:WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUsVanta—automate compliance, manage risk, and accelerate trust with AI—Episode transcript: https://www.lennysnewsletter.com/p/hard-truths-about-building-in-the-ai-era—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Keith Rabois:• X: https://x.com/rabois• LinkedIn: linkedin.com/in/keith• Website: https://www.khoslaventures.com—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Keith Rabois(01:59) Why Keith hasn't used a computer since 2010(04:52) The team you build is the company you build(07:40) How Keith learned to identify talent at PayPal(10:05) Tactics for getting better at hiring(15:31) The barrels vs. ammunition framework(18:52) What makes someone a barrel(22:36) How to attract the best talent(26:18) Building companies on undiscovered talent(27:53) Why better performance requires more pressure(32:36) Career advice in the age of AI(35:14) The future of the product triad(41:03) Why design and code are merging(49:35) What practicing law taught Keith about entrepreneurship(51:22) Contrarian takes on customer feedback(1:02:33) Identifying great AI opportunities(1:05:13) Advice for evaluating statrups (1:12:36) Criticizing in public vs. private(1:15:05) Failure corner(1:17:29) Lightning round—Referenced:• Square: https://squareup.com• Jack Dorsey on X: https://x.com/jack• Head of Claude Code: What happens after coding is solved | Boris Cherny: https://www.lennysnewsletter.com/p/head-of-claude-code-what-happens• Simon Willison's Weblog: https://simonwillison.net• Vinod Khosla on X: https://x.com/vkhosla• Peter Thiel on X: https://x.com/peterthiel• Max Levchin on X: https://x.com/mlevchin• David Sacks on LinkedIn: https://www.linkedin.com/in/davidoliversacks• Tony Xu on X: https://x.com/t_xu• David Sze on X: https://x.com/davidsze• Faire: https://www.faire.com• Max Rhodes on X: https://x.com/MaxRhodesOK• Jeffrey Kolovson on LinkedIn: https://www.linkedin.com/in/jeffreykolovson• Uncapped | Comparative Advantages w/ Keith Rabois: https://www.khoslaventures.com/posts/uncapped-comparative-advantages-w-keith-rabois• Lattice: https://lattice.com• Taylor Francis on LinkedIn: https://www.linkedin.com/in/taylor-francis-4ba49640• Building product at Stripe: craft, metrics, and customer obsession | Jeff Weinstein (Product lead): https://www.lennysnewsletter.com/p/building-product-at-stripe-jeff-weinstein• The art of hiring: insights from Khosla Ventures, Airbnb, Ramp and Traba: https://ramp.com/velocity/the-art-of-hiring-insights• Eric Glyman: Seek out super individual contributors (ICs): https://ramp.com/velocity/the-art-of-hiring-insights#Eric-Glyman:-Seek-out-super-individual-contributors-(ICs)• Eric Glyman on X: https://x.com/eglyman• Mike Moore on LinkedIn: https://www.linkedin.com/in/mike-moore-802223177• Brian Chesky's new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach• Why you should work much harder RIGHT NOW: https://marginalrevolution.com/marginalrevolution/2026/03/why-you-should-work-much-harder-right-now.html• Opendoor: https://www.opendoor.com• The Craft of Early Stage Venture | Peter Fenton, General Partner at Benchmark | Uncapped with Jack Altman: https://www.youtube.com/watch?v=vRiblwiXt-Q• Lovable: https://lovable.dev• The rise of the professional vibe coder (a new AI-era job) | Lazar Jovanovic (Professional Vibe Coder): https://www.lennysnewsletter.com/p/getting-paid-to-vibe-code• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (co-founder and CEO): https://www.lennysnewsletter.com/p/building-lovable-anton-osika• Marc Andreessen: The real AI boom hasn't even started yet: https://www.lennysnewsletter.com/p/marc-andreessen-the-real-ai-boom• Jeremy Stoppelman on X: https://x.com/jeremys• The design process is dead. Here's what's replacing it. | Jenny Wen (head of design at Claude): https://www.lennysnewsletter.com/p/the-design-process-is-dead• Andy Warhol: https://en.wikipedia.org/wiki/Andy_Warhol• Curation and Algorithms: https://stratechery.com/2015/curation-and-algorithms• Ernest Hemingway: https://en.wikipedia.org/wiki/Ernest_Hemingway• William Shakespeare: https://en.wikipedia.org/wiki/William_Shakespeare• Evan Moore on X: https://x.com/evancharles• Andrew Mason on X: https://x.com/andrewmason• Read Taylor Swift's Full Viral Speech After Record-Breaking Awards Sweep: https://www.newsweek.com/entertainment/read-taylor-swift-full-acceptance-speech-record-breaking-awards-sweep-11745941• The Chainsmokers: Stories Behind the Songs, AI's Impact on Music, and Venture Investing | Uncapped with Jack Altman: https://www.youtube.com/watch?v=9GMSC-2pYnw&list=PLtpH7YnTL8ihy0nR2BV32n5VkRtqlDAS1&index=16• How to spot a top 1% startup early: https://www.lennysnewsletter.com/p/how-to-spot-a-top-1-startup-early• David Weiden on LinkedIn: https://www.linkedin.com/in/davidweiden• Alfred Lin on LinkedIn: https://www.linkedin.com/in/linalfred• Keith's post about vertical integration on X: https://x.com/rabois/status/870673635375104000• Jon Chu on X: https://x.com/jonchu• Kanu Gulati on X: https://x.com/KanuGulati• Rogo: https://rogo.ai• Profound: https://www.tryprofound.com• Basis: https://www.getbasis.ai• Spellbook: https://www.spellbook.legal• Roelof Botha on X: https://x.com/roelofbotha• Delian Asparouhov on LinkedIn: https://www.linkedin.com/in/delian-asparouhov-87447742• Lessons From Keith Rabois, Essay 1: How to become a Venture Capitalist: https://delian.io/lessons-1• Velocity over everything: How Ramp became the fastest-growing SaaS startup of all time | Geoff Charles (VP of Product): https://www.lennysnewsletter.com/p/velocity-over-everything-how-ramp• Nuremberg on AppleTV+: https://tv.apple.com/us/movie/nuremberg/umc.cmc.3sg4y0382byupy76bfy7307k4• Eight Sleep: https://www.eightsleep.com• “NO DAYS OFF”—Bill Belichick on X: https://x.com/SNFonNBC/status/829036279069364224—Recommended books:• Creativity, Inc.: Overcoming the Unseen Forces That Stand in the Way of True Inspiration: https://www.amazon.com/Creativity-Inc-Overcoming-Unseen-Inspiration/dp/0812993012• The Jordan Rules: The Inside Story of One Turbulent Season with Michael Jordan and the Chicago Bulls: https://www.amazon.com/Jordan-Rules-Sam-Smith/dp/0671796666• The Upside of Stress: Why Stress Is Good for You, and How to Get Good at It: https://www.amazon.com/Upside-Stress-Why-Good-You/dp/1101982934—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Cassie Young, Partner at Primary Venture Partners, joins the show to break down their contrarian $625 million seed fund and the reality of the coming gross retention apocalypse for tech companies. The conversation outlines why classical venture logic is breaking and how AI-native CRMs are plotting to steal enterprise market share from legacy giants like Salesforce. The group also details why the $6 trillion white-collar labor pool is the real target for emerging AI workflows rather than traditional software budgets. Expect a direct discussion on the myth of the zero CAC CEO, the evolving definition of the jaw-dropping customer experience, and the specific signals Primary looks for when writing checks to early-stage go-to-market founders building the next wave of B2B SaaS products. Key Takeaways -Primary Ventures just raised a massive $625 million seed fund by challenging traditional venture capital models, as Cassie explains, "The conventional wisdom at seed is to stay small. We fundamentally disagree with that." But don't worry: Cassie explains why they believe their approach is the right one. -Cassie warns that a retention reckoning is inevitable for AI startups noting, "I still very much live in fear of this... it might not be next month, it might not be next year, but it's ultimately going to come." Meanwhile, the hosts wonder: is it truly inevitable? Or just highly likely? -Cassie points out that a "Zero CAC CEO who's not supplemented by amazing technologists, it's just not enough." And that's just one of the many classic competitive moats which Cassie flags as reducing in value. -Asad highlights how rapid AI advancements are forcing companies to continuously rethink their competitive moats and sales motions, stating, "There are no product advantages that are long-lasting anymore." Connect with the Hosts & Guests  Host: Sam Jacobs - https://www.linkedin.com/in/samfjacobs/  Host: AJ Bruno - https://www.linkedin.com/in/ajbruno3/  Host: Asad Zaman - https://www.linkedin.com/in/azaman1/  Guest: Cassie Young - https://www.linkedin.com/in/cassyoung/ Topline is more than a YouTube Channel:  Subscribe to Topline Newsletter: https://www.joinpavilion.com/topline-newsletter  Tune into Topline Podcast, the #1 podcast for founders, operators, and investors in B2B tech: https://www.joinpavilion.com/topline-podcast  Join the free Topline Slack channel to connect with 600+ revenue leaders to keep the conversation going beyond the podcast: https://www.joinpavilion.com/topline-slack Chapters: 00:00 Cassie Young Joins Topline 04:37 Raising a $625M Seed Fund 08:08 How Primary Wins Seed Deals 12:56 The 6 Trillion AI Opportunity 19:04 Can AI CRMs Beat Salesforce 29:14 Gross Retention Apocalypse 40:12 Myth of the Zero CAC CEO 46:58 Eradicating Switching Costs 53:15 Backing Go-to-Market Founders  

Heading to Vegas this May? Join Josh at Pulse 2026 and come say hi—your oversized fluorescent daiquiri is on him. No catch.Grab your ticket at gainsightpulse.com and use code UNCHURNED for a special rate.Watch Eric Gilpin, President of Go-To-Market at G2, reveal how he's building the first-ever unified revenue org in a 210,000-product marketplace. In this episode, he takes us behind the scenes of G2's “zero daylight” alignment strategy and how it led to a game-changing 31% YoY traffic spike. Discover why G2 made the controversial bet to let LLMs scrape their data (and why competitors are now paying the price), how to flip buyer intent into “churn intent” to catch customers before they leave, and how the difference between “freelancers” and “contractors” became worth $600M+ in revenue. This is marketplace strategy 101, told by someone who's spent 25+ years perfecting it. Essential listening for GTM leaders, CS teams, and anyone building in B2B SaaS.---Timestamps0:00 - Preview & introduction1:28 - Meet Eric Gilpin & overview of G26:50 - How Upwork scaled from $30M to $650M in gross sales volume10:00 - Building G2's first President of Go-To-Market role11:13 - Zero daylight: Aligning marketing, BDRs, and all revenue teams16:42 - Pivoting narrative from “SEO review site” to AEO visibility engine18:15 - G2's “Hunter Hunter” post-sales org (60% growth from expansion)21:00 - The highest-value CS activities (hint: it's not quarterly review sends)23:23 - Self-serve + AI automation for the 150,000 SMB products25:23 - The churn intent27:28 - Why G2 allowed LLMs to train on their data31:55 - Nostalgia and denial kill businesses---What You'll Learn- How to eliminate silos by achieving “zero daylight” between marketing, sales, and revenue teams- The whitespace model G2 uses to find expansion revenue- How to build a “churn intent” system using buyer signals to catch churn before it happens- Why allowing LLMs to scrape your data early creates a competitive moat- The subtle power of reframing to unlock enterprise adoption and market perception- Why is constantly challenging your own status quo the only defense against disruption---Want the playbook, not just the conversation? Subscribe for deep-dive, actionable breakdowns from every episode at unchurned.substack.com.---Where to Find Eric GilpinLinkedIn: https://www.linkedin.com/in/ericgilpin/---Where to Find Josh:LinkedIn: https://www.linkedin.com/in/jschachter/Unchurned Substack: https://unchurned.substack.com/

Teju Owoye is a serial entrepreneur, author, and wellness innovator. She began her entrepreneurial journey by founding The Sulte Group, a growth marketing agency serving celebrity clients like Def Leppard and Ice Cube and top B2B SaaS companies alike. After facing her own health challenges, she launched Clean Rebellion, a clean beauty brand voted the Best Multi-Use Body Wash by Byrdie two years in a row. Today, she leads WellZest, a platform that connects individuals to holistic health providers and wellness experiences worldwide. Teju is also the author of The Audacity Journal, a guided workbook for ambitious women reclaiming purpose, confidence, and joy in their next chapter. She also hosts the WellZest podcast.

Amol Avasare is Head of Growth at Anthropic, which is going through the most unprecedented growth trajectory in history—scaling from $1 billion to over $19 billion in ARR in just 14 months. Previously, Amol worked on the growth teams at Mercury and MasterClass. Before that he was a founder, and he cold emailed his way into the Anthropic role when no job listing existed. Most remarkably, he overcame a traumatic brain injury from a Muay Thai match that meant he couldn't work for nearly a year.In our in-depth discussion, Amol shares:1. How Amol landed his role by cold emailing Anthropic's CPO Mike Krieger2. How Anthropic is automating growth experiments with Claude (their internal tool called “CASH”)3. Why the ratio of PMs to engineers might need to flip (more PMs than engineers) as AI makes engineers exponentially more productive4. Why activation is the single highest-leverage growth problem in AI5. Why Anthropic indexes 70/30 toward big bets (the opposite of most growth teams)6. How he uses Cowork to detect team misalignment in Slack7. How the company's focus on AI coding created a research flywheel that accelerated their models—Brought to you by:WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUsVanta—Automate compliance, manage risk, and accelerate trust with AI—Episode transcript: https://www.lennysnewsletter.com/p/anthropics-1b-to-19b-growth-run—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Amol Avasare:• X: https://x.com/TheAmolAvasare• LinkedIn: https://www.linkedin.com/in/amolavasare—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Amol and Anthropic's growth(03:15) The story of cold emailing Mike Krieger to get the job(08:28) What it's like leading growth at the fastest-growing company ever(10:46) What the growth team actually does at Anthropic(12:16) The concept of “success disasters”(13:55) Why activation is the biggest challenge in AI products(18:05) Improving Mercury's onboarding experience(20:57) The importance of adding the right kind of friction(25:10) Anthropic's org structure(27:06) Why Anthropic focuses on big bets over micro-optimizations(33:34) Automating growth experiments with Claude (CASH)(38:20) How AI is starting to identify what experiments to run(41:07) The future of PM, engineering, and design roles(47:19) Why you might need more PMs as engineers get more productive(51:13) How Amol uses AI to prototype ideas and skip PRDs(58:10) Amol's morning routine: AI analyzes 20 to 25 charts automatically(1:03:31) Getting coaching from an AI version of your manager(1:06:27) How Anthropic's focus on coding and B2B drove their success(1:12:10) Balancing growth with AI safety as a core mission(1:18:09) Advice for thriving in an AI-first future(1:22:53) Anthropic's culture and the “notebook channels” on Slack(1:35:12) Failure corner: Shutting down his startup after raising money(1:38:25) The traumatic brain injury that changed everything(1:46:49) Lightning round—References: https://www.lennysnewsletter.com/p/anthropics-1b-to-19b-growth-run—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Simon Willison is a prolific independent software developer, a blogger, and one of the most visible and trusted voices on the impact AI is having on builders. He co-created Django, the web framework that powers Instagram, Pinterest, and tens of thousands of other websites. He coined the term “prompt injection,” popularized the terms “AI slop” and “agentic engineering,” and has built over 100 open source projects, including Datasette, a data analysis tool used by investigative journalists worldwide. What makes Simon unique is that he's made the leap from traditional software engineering to AI-native development more fully and visibly than almost anyone—and he's been documenting everything he learns in real time on his blog, SimonWillison.net.In our in-depth conversation, Simon shares:1. Why November 2025 was the inflection point when AI coding agents crossed from “mostly works” to “actually works”2. How Simon writes 95% of his code from his phone now and why he's mentally exhausted by 11 a.m.3. Why mid-career engineers (not juniors) are most at risk right now4. The three agentic engineering patterns Simon uses daily (red/green TDD, templates, hoarding)5. The next leap: the “dark factory” pattern where nobody writes or reviews code and AI does its own QA6. Why prompt injection is an unsolved security problem and the “lethal trifecta” that will likely lead to an AI Challenger disaster7. Why the pelican riding a bicycle became the unofficial benchmark for AI model quality—Brought to you by:WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUsVanta—automate compliance, manage risk, and accelerate trust with AI—Episode transcript: https://www.lennysnewsletter.com/p/an-ai-state-of-the-union—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Simon Willison:• X: https://x.com/simonw• LinkedIn: https://www.linkedin.com/in/simonwillison• Website: https://simonwillison.net• Agentic Engineering Patterns: https://simonwillison.net/guides/agentic-engineering-patterns—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Simon Willison(02:40) The November 2025 inflection point(08:01) What's possible now with AI coding(10:42) Vibe coding vs. agentic engineering(13:57) The dark-factory pattern(20:41) Where bottlenecks have shifted(23:36) Where human brains will continue to be valuable(25:32) Defending of software engineers(29:12) Why experienced engineers get better results(30:48) Advice for avoiding the permanent underclass(33:52) Leaning into AI to amplify your skills(35:12) Why Simon says he's working harder than ever(37:23) The market for pre-2022 human-written code(40:01) Prediction: 50% of engineers writing 95% AI code by the end of 2026(44:34) The impact of cheap code(48:27) Simon's AI stack(54:08) Using AI for research(55:12) The pelican-riding-a-bicycle benchmark(59:01) The inherent ridiculousness of AI(1:00:52) Hoarding things you know how to do(1:08:21) Red/green TDD pattern for better AI code(1:14:43) Starting projects with good templates(1:16:31) The lethal trifecta and prompt injection(1:21:53) Why 97% effectiveness is a failing grade(1:25:19) The normalization of deviance(1:28:32) OpenClaw: the security nightmare everyone is looking past(1:34:22) What's next for Simon(1:36:47) Zero-deliverable consulting(1:38:05) Good news about Kakapo parrots—References: https://www.lennysnewsletter.com/p/an-ai-state-of-the-union—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Christian Wiens joins the Restaurant Unstoppable Network for a live Q+A on May 5th, 2026 at 11AM EST. To join us and engage with all our guests and events, go to restaurantunstoppable.com/live -OR- to just catch today's guest, head over to restaurantunstoppable.com/cwe and we will get you a link to join that specific event for FREE! Christian Wiens is the Founder and CEO of Loman AI, a leading voice AI platform built specifically for restaurants that answers every call, takes orders, and helps operators capture missed revenue while easing front-of-house labor pressure. Since launching the Austin-based company in 2024, he has scaled Loman AI to serve restaurants nationwide, processing tens of millions of dollars in order volume and millions of phone calls as brands use the technology to boost sales by up to 22% and cut labor costs by as much as 17%. Before Loman AI, Christian spent a decade in B2B SaaS marketing and go-to-market leadership roles at companies like MixMode, Anchore, and AutoVitals, where he helped build and scale revenue engines in fast-growing tech businesses. Drawing on that background and thousands of conversations with restaurant operators, he's now focused on using AI to turn the restaurant phone from a bottleneck into a growth channel and to help operators run more resilient, profitable businesses. Join RULibrary: www.restaurantunstoppable.com/RULibrary Join RULive: www.restaurantunstoppable.com/live Set Up your RUEvolve 1:1: www.restaurantunstoppable.com/evolve Subscribe on YouTube: https://youtube.com/restaurantunstoppable Subscribe to our email newsletter: https://www.restaurantunstoppable.com/ Today's sponsors: - Restaurant Technologies — the leader in automated cooking oil management. Their Total Oil Management solution is an end-to-end closed loop automated system that delivers, monitors, filters, collects, and recycles your cooking oil eliminating one of the dirtiest jobs in the kitchen.. Automate your oil and elevate your kitchen by visiting rti-inc.com or call 888-779-5314 to get started! - Restaurant Systems Pro - Lower your prime cost by $1,000, and get paid $1,000 with the Restaurant Systems Pro 30-Day Prime Cost Challenge. If you successfully improve your prime cost by $1,000 or more compared to the same 30-day period last year, Restaurant Systems Pro will pay you $1,000. It's a "reverse guarantee."  Let's make 2026 the year your restaurant thrives. - US Foods®. Running a restaurant takes MORE than great food—it takes reliable deliveries, quality products, and smart tools. US Foods® helps you make it. Ready to level up? Visit: usfoods.com/expectmore. - Guest contact info:  Website Thanks for listening! Rate the podcast, subscribe, and share!