Podcasts about GTM

In this episode, we speak with Stewart Lynn, Partner at Serent Capital, a growth-focused private equity firm that partners with founder-led B2B software and technology-enabled services companies. Founded in 2008, Serent has partnered with leadership teams at more than 100 software and technology-enabled services companies and today, the firm has more than $7 billion in assets under management. Stewart leads software investments across verticals including real estate, transportation, govtech, and entertainment. Since joining the firm in 2013, he has spearheaded numerous investments and supported executive teams on growth initiatives spanning strategy, GTM scaling, pricing, and M&A. Prior to Serent, he was a Vice President at the Seabury Group and a consultant at Bain & Company. Serent was recognized as a Top Private Equity Firm of 2026 and Top Growth Equity Firm of 2025 by GrowthCap. I am your host, RJ Lumba. We hope you enjoy the show. If you like the episode, click to follow.

What if the traditional path to becoming a CRO isn't the only path? In this episode of Hunters and Unicorns, Ollie Kuehne and Simon Kouttis sit down with Ozge Tuncel Ozcan, Chief Revenue Officer at Forter, to explore her unconventional journey from engineering and customer success to leading an entire revenue organization. Ozge shares why revenue is much more than bookings and pipeline, how her experience across customer success, professional services, sales development, and post-sales shaped her leadership, and why listening to customers can become a CRO's greatest advantage. The conversation also explores customer retention, product feedback, GTM strategy, AI, and why companies need to connect innovation, sales, and customer success instead of treating them as separate functions. In this episode: • Ozge's unconventional journey to CRO • Why customer understanding is at the heart of revenue • How customer success shaped her sales leadership • Why CROs need to think beyond bookings and pipeline • Connecting product, sales, and customer success • Using customer feedback to drive innovation • Building a more cohesive GTM organization • Why listening may be the most underrated leadership skill   About Our Sponsor: Big thanks to Aurasell for sponsoring this episode. Aurasell is an AI-native GTM platform designed to intelligently automate every aspect of the sales cycle from prospecting and deal management to forecasting and coaching. Subscribe to Hunters and Unicorns for more conversations with the leaders building, scaling, and transforming high-growth technology companies. #HuntersAndUnicorns #OzgeTuncelOzcan #Forter #CRO #SalesLeadership #GTM #RevenueLeadership #CustomerSuccess #B2BSales #gtmstrategy Timestamps: 00:00 Trailer 02:58 What Defines you as a CRO? 04:49 The Power of Listening 07:51 From Engineer to CRO 09:17 From MBA to McKinsey 12:00 The MongoDB Opportunity 18:00 Building a Customer-First GTM 24:00 Why Customer Success Matters 31:59 The Next Generation CRO 34:08 The Future of AI Adoption 36:20 How to Evaluate a Company 41:28 Innovation Meets GTM 45:01 Building a World-Class GTM Team 51:34 The Future of AI Commerce 54:12 Building Careers, Not Jobs 56:08 The New Path to CRO

In this episode, Abyl Ikshanov, co-founder and CTO of Volve Technologies, shares insights into building AI-driven solutions for the construction industry, from early career highlights to technical challenges and future expansion plans.   key topics AI and knowledge graphs in construction Technical challenges in building AI products Product development and iteration with customer feedback Market expansion and industry-specific insights takeaways Understanding the importance of structured data in AI models The significance of evaluation and grounding in AI features Iterative development driven by customer feedback Challenges of building industry-specific AI solutions Keywords AI, construction tech, knowledge graph, product development, startup journey   Chapters 00:00 Introduction to Abyl Ikshanov and his background 01:36 Meeting co-founders and initial idea research 02:09 Identifying inefficiencies in construction documentation 03:29 Tech stack and architecture considerations for AI in construction 04:35 How the product works: from scope items to work packages 07:25 Most challenging technical feature and proudest achievement 08:52 Importance of evaluation and grounding in AI features 09:40 Proudest moments and iterative development process 10:43 Lessons learned and industry-specific insights 12:02 Supporting the mission and future expansion  

We break down what changes when AI agents become a bigger source of web visits than humans, and why measurement is the only sane response to generative search volatility. We also map the new playbook for go-to-market leaders who want durable advantage by building context, instrumentation, and systems that actually learn.•Agent analytics and deeper web measurement through server logs and segmentation•Turning GEO and AEO into a trackable marketing channel with baselines and experiments•Why the website shifts from destination to structured knowledge layer for AI agents•Context engineering through schema, FAQs, and connected site structure•Why “renting cognitive logic” from LLMs fails to create a moat•Decision tracing as a system for learning from GTM experiments over time•AI integration tax in fragmented stacks and how dynamic blindness breaks workflows•What a harness is and how constraints, monitoring, and stopping rules reduce risk•Leading the shift from point-solution operator to multi-agent orchestrator•Moving boards from token costs to value per token and closed-loop outcomesAI agents are quietly rewriting your analytics dashboard, and most teams are still looking at the old numbers. When bots and crawlers can drive more web visits than humans, “traffic” stops being a simple KPI and becomes a strategy problem: Who is visiting, what model sent them, where do they land, and do they convert? We sit down with Alexander Liss, Executive Advisor of AI Transformation at Brainworks and former VP of Data Science and AI at Huge, to talk about agent analytics, server logs, segmentation, and how to treat generative search optimization as a real, measurable marketing channel.We also go into the "website's" near-death and rebirth. The site is not disappearing, but early discovery is moving into AI Overviews and assistants, which means your content has to work as structured knowledge for machines and as high-trust depth for humans who arrive later. We cover context engineering, schema, FAQ patterns, and why social listening is back as models pull signals from places like YouTube, Wikipedia, and community forums, then change their minds a week later.From there, we get blunt about competitive advantage. If you are only renting cognitive logic from base models from Anthropic, OpenAI and Google, you are not building a moat. We unpack decision tracing, the AI integration tax, dynamic blindness in multi-agent workflows, and what it means to build a harness with constraints, monitoring, and stopping conditions so AI systems stay useful and cost-effective. We close with a fast, fun Spark Tank segment on Japanese business systems plus the “three feet from gold” resilience story. Subscribe, share with a growth leader, and leave a review. What part of your go-to-market stack needs better AI measurement first?Alexander Liss: https://www.linkedin.com/in/aliss77777Alexander Liss is the Executive Advisor of AI Transformation at BrainWorks and former VP of Data Science & AI at Huge. A data leader and systems-builder based in Denver, Colorado, his recent work includes pioneering Huge's emerging GEO practice and deploying NBCUniversal's award-winning conversational assistant, Oli, for the 2026 Winter Olympics. His prior leadership spans scaling enterprise analytics and machine learning strategies at global digital powerhouses like Accenture Song, VML and 22squared. He is currently pursuing his Master of Science in Artificial Intelligence from the Georgia Institute of Technology, holds an MBA from NYU Stern School of Business, and earned his Bachelor's in Japanese Language and Literature from The George Washington University. Website: https://www.position2.com/podcast/Rajiv Parikh: https://www.linkedin.com/in/rajivparikh/Email us with any feedback for the show: sparkofages.podcast@position2.com

AI in GTM is often framed as a productivity tool: write the email faster, automate the workflow or increase the volume of outreach. Harrison Rose, Co-Founder of Goodfit and Paddle, makes the case for a more fundamental shift.His argument is that AI becomes far more valuable when it moves from executing tasks to making decisions. Harrison traces that thinking back to Paddle, where classification models helped identify relevant software companies more quickly and accurately than a manual research process.He then looks at what today's AI makes possible. By combining market data with past wins, losses, contract values and interactions, teams can begin to predict which accounts are worth pursuing and how to approach them.Harrison explains how expected value can inform those choices and why GTM systems may increasingly decide who gets targeted, when, through which channels and with what level of spend.This talk was recorded during the EUVC Summit & Awards Show 2026.HighlightsWhy scaling old GTM workflows misses the bigger AI opportunityWhy Harrison sees decision-making as AI's core strengthWhat Paddle's early use of classification models revealedHow AI can use more context than an individual repHow expected value can improve account prioritisationWhy GTM strategy could become increasingly dynamic and machine-ledWhat this shift could mean for the buyer experienceTimestamps(00:00) Intro(01:00) Why AI in GTM needs a different approach(02:15) The GTM problem Harrison faced at Paddle(03:25) Automating prospect research with classification models(05:00) What Paddle's early use of AI revealed(06:10) Why automating bad GTM work does not make it better(08:05) Why decision-making is AI's real strength(09:45) How AI can outperform traditional account mapping(11:10) Using expected value to prioritise accounts(12:50) Letting AI decide channels, spend and outreach(13:55) What programmatic advertising tells us about the future of GTM(14:35) The future of AI-led go-to-market

This week, our host Ian Truscott goes solo, comparing the electrification of the automobile industry with AI for marketing. Ian sees a parallel for marketers: as electric engines in relatively inexpensive cars make performance numbers like 0-60 times and horsepower less relevant indicators of desirability for exotic cars, they need to lean on the brand experience. An obvious analogy there for marketers, with all the horsepower of AI, we can either do more with slop or do something differentiated. Thirsty after all that talking, Ian then joins Robert Rose in the virtual bar, The Rose & Rockstar, and over a refreshing cocktail, they discuss our industry's dependency on jargon.Enjoy!—The LinksThe people:Ian Truscott on LinkedIn Robert Rose on LinkedInMentioned this week:Tuesday 2¢ - Horses for More UsMy personal website and blogRobert in CMI Newsletter - AI-native full-funnel GTM motion (WTH?!)Robert's newsletter: Lens, his websites, robertrose.net and seventhbear.comRockstar CMO:The Beat Newsletter that we send every MondayRockstar CMO on the web and LinkedInPrevious episodes and all the show notes: Rockstar CMO FM.Track List:We'll be right back by Stienski & Mass Media on YouTubePiano Music is by Johnny Easton, shared under a Creative Commons licenseYou can listen to this on all good podcast platforms, like Apple, Amazon, and Spotify. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Now, everyone's talking about building AI harnesses and context layers. Kashish has been building these for years and Hightouch has now reached a stage where they are challenging giants like Adobe and Salesforce while crossing $100M in revenue.In this episode we discussed how and why LLMs like ChatGPT and Gemini are failing marketing teams at enterprises, why marketing is a coding problem, what makes a harness “ thick”, why it's a CEO's job to be Chief Pipeline Generator in early years, controversial decisions that worked perfectly for Hightouch and why now is the time for 100x Marketer.Listen to the episode to look into the future of marketing00:00 Quitting VC to become a Founder01:35 Travel Startup with Terrible Margins03:25 From Slack Agents to Customer Data04:31 Saving a Bank $6M on "Unmarketing"05:45 Why Make Composable CDP?07:53 Why Every Other CDP Was Broken11:33 Where Hightouch Creates Real ROI12:48 Agents That Surface Revenue16:47 Inside a Multi-Million Dollar Enterprise Rollout21:41 Will AI Replace Marketers?25:18 Why Skipped SMBs for Fortune 50026:54 The Rippling Model of Running a Startup27:48 Going Multi-Product Early29:38 The 100x Marketer33:03 Unstructured Data, Lead Lists & Synthetic Signals34:49 How AI Launches Ads in New Markets36:13 Does Marketing Success Guarantee Revenue?38:07 What Is a "Marketing Harness"?40:52 Thick Harness vs. Thin Harness45:10 How Hightouch Fits Into the Enterprise Stack46:54 Controversial Founder Decisions49:15 Building the Company of Tomorrow52:07 AI, GTM and Sales-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/https: https://www.linkedin.com/company/neon-fundTwitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us Fan Mail

https://youtu.be/lrnG7ydEErk Saul Marquez, Founder and CEO of Outcomes Rocket, helps medtech and healthtech companies get closer to revenue through healthcare-focused marketing strategy and execution. Driven by a desire to be a source of love and inspiration, Saul supports healthcare innovators whose work helps people live healthier, longer lives. He believes companies improving healthcare deserve to succeed and should not have to navigate growth alone. In this conversation, Saul shares his Leverage the 3 Forms of Marketing Framework—Owned (podcast, books, content), Earned (Stages, Testimonials), and Paid (Drive Traffic to What Converts). He explains why companies need a clear strategy and strong owned assets before pursuing earned exposure, and why paid marketing should amplify a funnel that already converts. Saul also discusses growing through primary research, thought leadership, podcasting, and conferences rather than relying on cold outreach. He shares why marketing metrics must connect to pipeline and revenue, and how sales blockers, opportunities, and needs can guide the creation of campaigns and sales enablement assets. —   Get Closer to Revenue with Saul Marquez Good day, dear listeners. Steve Preda here with The Management Blueprint Podcast, and my guest today is Saul Marquez, the Founder and CEO of Outcomes Rocket, a healthcare-exclusive marketing strategy and full-service marketing execution firm that helps medtech and healthtech companies accelerate their growth. Saul, welcome to the show.  Steve, such a pleasure to be here with you and your listeners. Thank you for the opportunity.  Well, I really have to get my A-game today because I rarely find a podcaster who’s recorded more episodes than I have. You beat that by a multiple of five or six. So definitely, I have to be on my best performance. But my first question is always the same, at least recently. What is your personal “Why,” and how are you manifesting it in your business?  My personal Why. I did some thinking. This was probably about 20 years ago. I did this program. I’ve always been very reflective, and I’m a big journaler. I love to write my thoughts. And I had the chance to, about 20 years ago, do a program called Date With Destiny. It’s a Tony Robbins program. It was a game changer for me. Five days with people that want to just crush it in life—personal, professional, financial, right? Like, they just want to do the best.  And so I had these five days to myself to really look inside, journal, question. And during that session, he has what he calls your primary question. You sort of look inside and you ask and you think about, like, what are those words, the stories that you tell yourself? And the primary question is that question that drives your life. And I was able to uncover that my primary question is, “I want to be a source of love and inspiration to myself and others.” And so I’m driven by love. I’m driven by inspiration. And so that’s my primary question and my primary Why. And then, when you think about it professionally, Steve, I’m very driven by mission.  So because of that, I started my career in medical devices around the same time that I actually did the seminar. And I’m driven by being able to help people live better lives and increase health span, not just lifespan. And that’s why the work that we do focuses around leaders innovating in the healthcare space. So very driven by those things.  But these are very noble ideas. And I mean, who wouldn’t want to live better, live longer? That’s an obvious need from everyone, really. And it’s a great thing if you can create an impact in that realm, that then you are creating something very valuable.  You are, Steve. And the data point here that I’ll share to pair the purposefulness, the data point, because we’re very data-driven as a business, and I’m a data geek, is that healthcare is essentially 18% of U.S. GDP, which represents $4.8 trillion annually. It’s larger than the German economy, and that’s just the U.S. alone. So whenever anybody says, “Oh, your niche is healthcare,” I say, “Well, I mean, my economy that I’m focused on is healthcare.” It’s huge.  Of course. Yeah.  Yeah.  Yeah. And probably, I mean, we can get into whether that’s not an overinflated number. Is it really that proportionate value? But if you think about it, the biggest resource is humans, then spending 18% of GDP on the biggest resource is not much.  It definitely isn’t. And then if you sort of zoom out and you take a look at globally, GDP focused on healthcare, it’s definitely higher than most first-world countries. And the outcomes aren’t commensurate to the investment. So the opportunity to improve access, affordability, better outcomes is a huge opportunity. And I'm in awe, and I have major respect for all the entrepreneurs and business leaders in this space that are looking to improve those metrics for us in healthcare, and that's why we love to stand behind them.Share on X  The stats are real. 50% of businesses fail within five years, and something above 80% fail within 10 years. And we believe at Outcomes Rocket that if you’re in the business of helping people live healthier, longer lives, you deserve to succeed, and we want to be behind you. And so that’s why we do what we do. The people doing the work, it’s hard, and they can’t do it alone.  Yeah. Love it. Just as an aside, whatever happened to this initiative of Warren Buffett and Jeff Bezos that they announced some years ago that they would reform the—  Yeah.  Healthcare?  Haven?  Yeah. I don’t know what it was called.  Yeah, Haven. So yeah, it was Berkshire Hathaway, Amazon, and JPMorgan. And it didn’t work. And it shows you that, like, even when the best of the best try to go do something about it, it doesn’t work. It’s hard. It’s hard work. Yeah. It’s hard work.  I bet it’s very hard. So hopefully AI will fix it. Let’s hope. What do you think about that?  That’s a great one, man. Like, AI definitely is not fixing it. However, properly deployed AI in solutions such as ambient scribing that helps physicians spend time with patients and no longer have to do what they call pajama time. Pajama time is the time that they spend at home after hours logging things into the medical record. Like, if you’re able to give a physician time back from not having to do that, and actually time back to look at you in the eyes when you’re in the waiting room, that’s awesome use of AI.  The use of AI in the elimination of waste is also beautiful. So I think as a tool, for sure, there’s huge promise in the use of AI for healthcare. Hell, in robotics, man. Like, I was just at a conference, Steve. I was in Miami. Where do you live, by the way?  I'm in Virginia.  Oh, you’re in Virginia? Cool. I’m in San Diego, where I’m literally at the SRS, so that’s the Society of Robotic Surgery. And I’m in the room, and there was a surgeon in Virginia, actually, and a surgeon in California, and the robots were operating on. It wasn’t a person. It was actually just like a simulation, but it was like a cadaver type of thing. And with AI, spatial AI, and the use of technology, these surgeons are operating in two different states on one person.  Remotely?  Yeah, remotely, and it’s working great through robotics. So all of this stuff, man, is coming together. Yesterday, I had a conversation with an entrepreneur in the materials and 3D printing space. She’s been in it for years, and just chatting with her was inspiring because what they could do now as far as custom-built plates for craniomaxillofacial or foot and ankle, they could literally print this stuff overnight on sheets, whereas it used to take months.  Like, we’re moving fast, and the innovations that are available. She called it patient matching, like the N of one. I mean, what’s possible today for a fraction of the cost than it used to be back then is just inspiring. And it’s happening right before us. So it’s a really great, great time to be alive.  And to stay alive.  And to stay alive. Exactly. Well said, my friend. Well said. Hey, you have to tell me about Summit OS and Fable, man. Like, I love what you have back there.  Well, I’ll tell you all about Summit OS on your podcast, but on this podcast, we talk about you. That’s fair. That’s fair.  I like that. I like that.  So let’s talk about frameworks because this podcast is a podcast of frameworks.  I love frameworks.  And I saw that you have the Discover, Define, Deliver, or something like that. But I’m looking for something more unique. Yeah. So something that maybe that’s more insightful or more unique or more you that you could share with the audience, which still can be explained in four to five steps or elements maximum to which give people an insight as to how to do things better.  Absolutely. So I think you and I are brothers from another mother, Steve, because I just love frameworks as well. So the 3D approach is easy, as you mentioned, right? But it is our approach and how we reproducibly bring about a program from start to finish for a client: Discover, Define, Deliver.  Underneath that, that is the hood to another framework, which when you start to deliver, the framework is essentially a four-part framework that starts with strategy, then it’s owned, earned, and paid, okay? And so those are the types of marketing that you could do. And you mentioned at the beginning we’re a marketing strategy and full-service execution agency, which essentially means we’re a revenue partner, we’re a commercialization partner.  So when you go to market with your value and your value proposition, it all starts with strategy. Strategy is so key. And one of the key quotes that we always share, Steve, is that, “Tactics are the noise you hear before the war is lost.” And I have to say, Steve, and everybody with us, in marketing, there are so many tactics. Too many. And guess what?  Today, with AI, there are so many tactics. I was just on a podcast a couple days ago where I made this connection. I hadn’t made the connection yet, but you have to have an AI strategy. If you don’t have an AI strategy, you become part of somebody else’s plan, and even worse, you become so fragmented, and it’s reflecting in your P&L. Like, you not having an AI strategy is showing up in your P&L in a big way. But anyway, back to the marketing thing and the framework. So start with the strategy.  Inside of your strategy are some very basic things, such as your personas, your ideal client personas, which is like firmographic, kind of number of employees, revenue, et cetera.Share on X Your core messaging. Your brand house essentially is your vision, your differentiation, and then your performance promise. It’s essentially like three pillars. That’s your strategy and your positioning, right? Then when you go to owned, earned, paid—and by the way, they’re in this order for a reason. It’s like algorithmic.  I liken it to the Rubik’s Cube. I was watching a YouTube video with my nine-year-old, and he was like, “Hey, Dad, figure out how to solve this.” I brought him a Rubik’s Cube from a conference. I watched this four-part video. The guy’s name is Cubastic. Have you ever watched it, Steve?  No.  No? Okay. Cubastic, literally, he’s a genius. Like, in four 10-minute videos, walks you through how to solve a Rubik’s Cube. And I can solve a Rubik’s Cube in less than two minutes and 30 seconds reproducibly now. It’s actually one of my conference tricks now, like whenever I go to a booth. And so I’m sitting there thinking, like, yes, no matter what, wherever the pieces are on the cube, if you use this algorithm, it's a four-part framework it gets you to the same end.Share on X  So I’m thinking, that’s exactly what we do. So the strategy, then owned, earned, paid, in that order. So owned is everything that’s on your website, what you put out on social. If you have a podcast like yours, Steve, this is owned. You own this. It’s the narrative that you own. Newsletters. Then you have earned. Why does this order matter? Well, if you try to do earned media, like if you hire a PR agency to do earned media, get you media attention, and you don’t have your strategy or your story straight, you’re going to confuse the market even more.  So that’s why earned is after owned. And with earned, it’s everything that you get. There’s a gentleman that put it really great. I have to get his name, but I got this from him. OPS, he calls it Other People’s Stages. And there’s digital and there’s physical stages. I’m on your digital stage. You’ve built this thing, and you’ve invited me, and I’m grateful for it. And by the way, I’m going to have you on mine. I want to learn about Summit OS, and I want to learn about your frameworks, and I want our audience to also learn about those.  So there’s an asset here, and we’re doing an exchange, which is beautiful, and we’re spreading ideas that make a difference. So in earned, you’re getting opportunities like on OPS, and those could also be written. So you get a byline article on a publication, right? Or you get invited to speak at a conference. That’s earned. And then there’s paid. And people ask me the question, like, “Hey, I’ve got these paid campaigns going on.” And I’m like, “Dude, you have nothing on your website that supports a narrative. Nobody is talking about you. Why would you even pay for anything?” I don’t care what it is.  Unless you got all those other things right, paid is there, like paid conferences, to get more people that’ll read the brochures that are owned, that’ll see the testimonials that are earned, that’ll convert to opportunities to check out your demo or sit with you to consider what you’re doing as a business to solve problems. And so the paid is essentially a way to increase traffic to an existing funnel that converts.  So essentially, this framework of strategy, owned, earned, paid is the framework of marketing that, if done right algorithmically, you will get results.Share on X And the result is the acceleration of someone, a business or a person, that goes from the awareness to consideration to decision funnel, which is essentially the business funnel for anyone, right? You accelerate the speed at which somebody learns about you and the problems you solve, considers you as a solution, and then makes a decision to work with you. So you’re not really selling a quick fix here, are you?  It’s not a quick fix. There are no quick fixes. You have to commit to the process and get it done.  Yeah, I like it. I mean, that makes complete sense. You have to have some assets that you start with that you own. I like the podcast. Okay, books can be like that. Yeah. Website, of course. Your frameworks are your asset, basically. And then you have to earn the right to actually share what you know. I love it. People validate, go through the stages, and get the testimonials, so you have to deliver. You have to prove that those assets are actually working, right?  Yes.  And then when you have a funnel, you already have a product that has proven itself, then it’s all about increasing the throughput. So paid is what? That’s right.  There is an amplifier.  That’s it. That’s it. Yeah. Yep. Yep.  It makes complete sense. It’s a very good framework. I’ve never thought about it this way, but it makes complete sense to me.  And it’s algorithmic. No matter where the pieces in the cube are, if you run it, you’re always going to end up with the same color on each side. It just works.  Yeah. So if you don’t have your assets, you haven’t earned your right, then you are just wasting your money on paid. Yeah.  Yeah. You’re wasting it. You are. Now, there’s a use case for paid to accelerate learnings. If you’re working on copy that you just need feedback on, there’s a use case to get mass targeted, like your ideal client looking at and interacting with it to understand how to better convert. That’s a use case, right? You’re after conversion optimization data. That’s fine, right? That’s fine. You could use paid to fine-tune conclusions as well.  Can you use paid for market research? Oh, yeah, for sure.  What you should be selling?  Yeah. You could definitely use paid for market research, for sure, if you have an end in mind. If your end in mind is to get more target, and then you just have to decide, right? Like, what payment model should you deploy? Should you put an ad out, or should you work with a partner that has access to a pool of qualified survey respondents? Would it be more efficient to just go through them, right? So it’s just a matter of what the end goal is.  That makes sense. So let me ask you a question, Saul. What drives growth in your business?  So a couple things. We do thought leadership, and so the thought leadership that we do is on healthcare marketing.Share on X And by the way, even though we focus on healthcare marketing, there’s fundamentals there that can apply to any business. So if you’re listening to this podcast and thinking, “I’m not healthcare,” there’s still fundamentals in the research that we do and the data that we mine that can help you.  So thought leadership based off primary research. So every quarter, we do two new reports. We conduct surveys focused on marketing strategies and tactics. Some of the latest ones we’ve done—we did one on podcasting, which is very interesting. We released this one about three weeks ago. This one’s gotten crazy media hits. Like, we’ve gotten over 30 media hits on this one.  PPC covered us, because it’s sexy still. Podcasts are sexy. But we had a lot of findings, and I’ll share the link with you. We don’t charge for our research. It’s free. We offer it to people so that they could do better marketing. Now, the thing that was most intriguing about that report for me, out of a lot of things, was that people are measuring the wrong thing as it relates to podcast marketing. You’ll see the results, but I remember these numbers. 57% are measuring engagement and downloads.  And that’s the wrong thing to measure. I’m going to segue to that later. The right thing to measure if you're a business is pipeline and revenue, not engagements and downloads.Share on X And the quote that I did on an article that I did recently is, “Downloads are vanity. Contacts and contracts are sanity.” Okay? You have to measure the right things. If you’re not a media company selling ads, who cares about downloads? So anyway, podcasts. We did a GTM report. We did one on public relations.  The other one that got a lot of really good traction earlier this year was one on chatbots. We analyzed over 5,700 citations to figure out what exactly are chatbots looking for. And we took a look at those 5,700 citations. It was a breakdown of Gemini, ChatGPT, Claude, and Grok. And so we said, why and what do these value? It changed the way that we actually post content on our site and our clients’ sites. So that’s a really valuable report that I would literally just take from our site, download into Claude, and say, “Based off of this report from Outcomes Rocket, how should I change up my copy and how I lay out my posts?” Because that’s going to help you get more AI visibility.  We actually have been running those plays on clients, and our numbers on ChatGPT and Claude Search and Gemini Search have gone up for them, right? It’s working. So we do this thought leadership stuff because we don’t like to experiment with our clients’ money. We like to actually do stuff that works and actually figure things out. So that’s thought leadership. And then the podcast is another thing that we do. So I love podcasting, Steve, as we were talking about before we hit record. I get a chance to connect with awesome people. Like, I keep thinking about Summit OS.  I’m going to learn about it on my podcast. I guess I can’t learn it on this one, but I get to meet people like you. We get to connect with listeners, like the ones—like, you’re listening to this because you want to be better. You want to improve your business, and it’s a chance for me to connect with you right now. And I’m going to invite you to reach out to me if something that I said resonates with you, because that’s why we do this. So podcasts are one of our great funnels, Steve. We do podcasting, and we meet a lot of great friends and collaborators and partners and clients through podcasting. And also conferences. So conferences are another amplifier for us. So I would say thought leadership, research, podcasting, and conferences are the best ways for us to grow our business. That's how we do it.Share on X  That’s very insightful. What I’m not hearing here is cold calling, cold emailing, spamming people. You’re not doing any of that. What I’m hearing here is you are giving people great content. You’re teaching people, and you’re connecting with people.  Yes.  Because podcasts, I agree with you, it’s all about connecting with people at a deeper level around interesting topics. Conferences are the same thing. You create relationships, and then you can follow up with the people who you like at the conference and turn them into partners or clients or whatever.  Yeah.  I love it. Love that.  And Steve, you’re a really insightful guy. I’m glad you went to that point. And so I was sitting there literally probably like five months ago, and I was having a conversation with a client of ours, and she was like, “Man, these cold emails that we’re doing,” because they wanted to do them, “I mean, we’re seeing clicks, and we’re seeing opens, but we’re seeing no replies and no meetings booked.” And I said, “Because the way we have to do it is through content.” Yeah. You have to offer.  If you go fishing and your hook has no bait on it, you’re not going to catch any fish, unless a dumb fish runs into your hook. You don’t want that fish anyway, right? That’s what you’re going to catch. And so I said, because I am kind of a data geek, as I shared with you, I said, “I want to own this frustration that you have right now. And here’s what I’m going to do. I’m going to hire four lead gen agencies, and I’m going to put one on your account, I’m going to put two on our company, and I’m going to…” I had another client that I was having this conversation with. “I’m going to put another one on their account.” And so I paid for this research project.  I said, “I’m going to learn. I’m either going to learn how to do cold email really damn good because I’ve been not doing well at it.” Guess what? Secret. Nobody’s freaking doing good at it, okay? So, like, three months later, they failed miserably. Nobody was doing anything. So then I said, “Okay, let’s try this. Don’t ask for an appointment. I want you to, on this mass cold email project, I want to change the copy, and I want to put, ‘I want to invite you to my podcast.'” These are the same people that were not replying. Steve, I kid you not, man. Like, within one week, we had 13 people in line that want to talk to them. They’re thrilled. So my takeaway was, cool.  You know what? I’ve been playing too small in sort of these podcast outreaches. I mean, look, at the end of the day, it’s working, but I said, “I’m going to use this mass email failure and turn it into a success by inviting even more people.” So now what we’re doing is actually, with these pools of people that are interested, taking a look at webinars too, so one to few. One to one, one to few, so that we could serve the many.  And so from the ashes of those failures, not only did I feel better about myself, like, dude, nobody is winning at cold email marketing. Nobody. But if you’re thoughtful about it in the way that you do it and what you offer, it’s got to be content-forward. But anyway, I wanted to pick up on your insight there and share our experiment that we ran.  I think also that you and I have been doing podcasting a long time. It’s actually now a huge thing. When I started it, for the first five years, I had no idea what I was doing, why I was doing it. I enjoyed it.  Yeah.  But it wasn’t really a thing. And then now I realize that a lot of people are now starting to do podcasting, but when you have a brand-new podcast, people are going to be much more circumspect to engage with you because they assume it may just be a lead gen engine. But when you have an established podcast with a lot of episodes, then you are a legit media, and then they will engage.  So anyway, that’s an aside. So let me ask you this, Saul, because you’re really a systems thinker, and I like that. What is one thing that you are trying to actively figure out in your business right now?  Right now, the thing that I’m actively working to figure out is really scalability and processes around delivery. So we have a very talented team. We’re small but mighty. There’s 25 people on my team, right? What we do is very bespoke, and our clients really do love how we execute and get results for them. What I’m working on is to master our delivery model so that we are more in line with our clients’ business growth. A few of the things that we’re doing to that end is getting closer to revenue.  What I mean by that is, I don’t know about you, Steve, but my career was mostly in sales in medtech before starting the agency three years ago full-time. And I realized that whenever there was a crunch in a business, some of the first positions to go were marketing. But nobody ever got rid of salespeople. So I started having this conversation with my team about we have to get closer to revenue, right? Get closer to the revenue conversations, have clarity around the revenue conversations. Because if you're close to revenue and you're helping drive revenue, you become more indispensable.Share on X  We might get T-shirts. I was kidding around that our theme is indispensable, and we might get T-shirts that say “Indispensable.” But that’s what we’re working on right now, is solidifying and optimizing our delivery so that we are more indispensable and we can continue growing at the pace that we’ve been growing to the next goals in our year and then our five-year and our 10-year plan.  So essentially, are you saying that getting closer to revenue means getting more directly impacting revenue, more directly making sure that the client is increasing revenue? Because marketing, you can increase marketing, but if it doesn’t have an impact, then it’s going to go away. So is this what you mean?  Exactly.  Yeah.  Because again, back to the whole thing, the quote, right? Like, “Tactics are the noise you hear before the war is lost.” In marketing, it’s so easy to report on metrics. But if your metrics are not tied to revenue, good luck.  Yeah. And there are some people who are doing great marketing, but they are doing very poorly in converting their leads, and therefore they’re wasting the marketing, and they might fire the marketing agency because you don’t have any clients. But they actually are downstream screwing things up. And if you can help them there, then your marketing is going to be much more resilient.  Yeah. And we’re getting awesome feedback from our clients. They love this. They’re telling us what they need. Right now, where we’re at, it’s three. So we do our weekly reporting, right, to our clients on kind of like what we’re doing on campaigns and research projects and execution. And so in our weekly report-out, we are finalizing sort of these three slides that are essentially sales. Like, the first slide is essentially the top five opportunities for the month and where they’re at. Sales blockers, sales opportunities, and then big needs, like sales needs.  So when the sales call happens, we get a transcript from that call, and the marketer asks the questions about, “Hey, what’s holding you back? What could help you move these deals faster?” We get that transcript, and then we create sales enablement assets or landing pages or campaigns that help move those deals forward. And that’s been awesome. Like, being able to do that, that’s being close to revenue, and it’s something that we’re adapting and being responsive to client needs, and that’s sort of where it’s taken us recently.  Yeah, I love it. Blockers, opportunities, and needs. So how are needs different from resolving the blockers and capitalizing the opportunity? A lot of times there’s overlap there on both of those. Yeah. The overlap is that a blocker could pair with a need that we could solve. But sometimes a blocker could be out of our control. Like, hey, we were going to sell them our microscope, but the MRI machine broke, and now they need to spend money on the MRI machine. Sh*t. There’s nothing we could do. Like, we’re pushing this deal to next quarter. Now, you could try to do stuff, creative financing or stuff like that, and we could have those conversations, but there’s some things that are out of our control.  I wonder if it’s needs or wants. Because I was thinking that maybe the needs are what the client already articulated as something that they realize that they need in order to grow revenue, whereas the blockers and opportunities will give you ideas how you can grow it in ways that they have not thought about.  Yeah.  And therefore, maybe instead of needs, it is the wants, and the needs are the ones that you add based on the blockers and opportunities. I don’t know.  I like that. I like it. No, no, I like it. I like it. And is the idea that, like, wants is a little more creative and open?  No, wants is something that they already articulated themselves that they want to do because they realize that they need that. But they need to do other stuff that they haven’t realized, and that’s what you come up with based on the blockers and the opportunities.  Correct. Correct.  It’s the unidentified wants basically. Yeah, yeah, yeah. I like that. I like that. I’m going to give some thought to that, Steve.  Yeah.  I like the idea.  Thank you. Thanks for the opportunity to give you a framework, or a framework. I love it, man. I love it. I had to pull it out of you. I had to pull it out of you. Actually, I didn’t. I couldn’t do it. You gave it to me, right? No, no. When the student is ready, the teacher comes, right?  There you go. Well, in that case, the teacher was inspired by the student, but—  I love it.  Whatever.  I love it.  So you said it’s medtech and healthtech companies and payers also that you target? Medtech and healthtech. And the payers, we actually—our clients sell to payers. So we do have a payer podcast where we interview payers and vendors in the payer market. But yeah, our main clients are health technology. So think Software as a Service, AI companies in healthcare, and then medical technologies like medical devices, implantables, wearables, that kind of thing, FDA-approved devices and technologies. Software as a medical device, that’s the medtech space.  Yeah. Love it. So these are the ideal ones. So if these people are listening to this podcast or they are seeing you on social media, you’re promoting this podcast together, where should they go? What do you want them to check out? How can they connect with you?  Yeah. Thank you, Steve. If you’re in the healthcare space and you want to raise your marketing game, increase your revenue, increase awareness of what your company’s doing, we’d love to be a part of it, whether it be you consuming our content. We don’t expect anything from you. Our content is all on outcomesrocket.com. Or you could find me on LinkedIn. If something that I post or that we post inspires you or makes you think differently, we always invite a conversation to explore working together. And so, yeah, outcomesrocket.com and LinkedIn are the best ways to reach us.  Okay. Saul Marquez, the CEO of Outcomes Rocket, thanks for coming on the show and sharing your awesome frameworks. I really enjoyed them. And if you’re listening to this, I mean, this is a goldmine for you guys. So make sure you follow us, you tune in to many episodes, because every week I bring a couple of fantastic entrepreneurs that will help you grow your business. So thanks for coming, Saul, and thanks for listening. Important Links: Saul's LinkedIn Saul's website

In this episode of Tank Talks, host Matt Cohen sits down with Rahul Parmar, Head of Go-to-Market at Railway and Venture Partner at Ripple Ventures, to unpack the real truths of building a sales engine in the modern AI era. Rahul shares his journey from being one of the first sales hires at Google Cloud in London to scaling Drata's sales team from Series A to Series C, and now leading GTM at the developer-first cloud platform, Railway. He explains why the traditional sales hierarchy is becoming obsolete, how he built Drata's engine by hiring in Toronto when it was a contrarian move, and why he believes Canadian reps are some of the best in the world.They break down the “time chamber” theory of selling in Canada, why the US market is a “shooting fish in a barrel” for trained Canadian talent, and the fundamental problem with Canada's ecosystem: a lack of capital and customers, not a lack of great founders. Rahul also shares his spicy take on the AI boom, explaining why the era of marking up token prices is over and why enterprises will soon distribute their AI workloads across multiple models.Whether you're a founder trying to land your first enterprise deal, a sales leader building a team, or an engineer contemplating a move to the Bay Area, this conversation offers a rare look inside the new playbook for winning in GTM.From Google Cloud to Drata: The Early Days of a Sales Career (02:07)* Joining Google Cloud in London as the 5th or 6th seller, when revenue was under $100M* The “confidence in the root” that Google provides, and using that shield to be transparent with customers* Why the best sales advice is: “Tell them what you do, tell them what you don't do, and then shut up”* Transitioning to Drata as the first sales leader, taking it from $50M in revenueThe “Time Chamber” Theory: Why Canadian Sales Talent is Elite (13:18)* The Dragon Ball Z analogy: Selling in Canada is like training in a weighted vest* Why Canadian buyers are incredibly difficult, forcing reps to master the fundamentals* How Canadian reps “crush it” when unleashed on the US market, where the propensity to buy is higher* The story of Drata's top AEs in every segment being based in TorontoThe AI Era of Selling: Fewer Reps, More Revenue (15:41)* Why you can now scale to $1B in revenue with a team the size of a Series A startup* The shift from a siloed GTM model to a unified one where GTM leaders are involved in everything* Why the era of the “forecast aggregator” sales manager is over* The new imperative: You're either building the system or you're selling deals* How Railway builds its own internal software, like its own Zendesk, to own the customer experienceRailway: The Developer Experience First Cloud (20:49)* What Railway is: The cloud where you point to a GitHub repo and it just launches* Why they run their own steel: The rate limits and poor fit of building a cloud on another cloud* The importance of owning the entire stack for a usage-based business* Why post-close support is a huge part of the revenue game at RailwayThe Canadian Ecosystem: Capital, Customers, and the Need for Network Effects (25:21)* Why it doesn't matter where the investors come from, but where the capital and customers are* The “tragedy” of successful Canadian companies, like Wattpad and Turbo Puffer, having their exit proceeds and financing led by US firms* The two fundamental problems for Canadian startups: access to capital and access to early customers* The “35 in Toronto vs. 35 in SF” thought experiment: Why you can't monetize your network in Canada* Why companies like Datadog and Snowflake would have died in the water if they started in TorontoAI, GTM, and the Future of Sales (35:41)* How Rahul uses AI at Railway to do lookalike mapping of customers based on usage patterns* Why he doesn't believe in high-volume, AI-driven cold outreach* The one quality he looks for in great GTM people: Intelligence, defined as listening for what's not being said and storytelling* How to call “b******t” on a sales hire in an interview: Ask for second-level details on a deal* His spicy take: The era of marking up token prices is over; enterprises will soon distribute workloads across a mix of frontier and open-source modelsThe Road Ahead: Building from Anywhere, Winning Everywhere (39:38)* The plan for Canadian founders: Build your product here, sell it in the US* The value of doing a stint at a high-growth US company, like Ramp or Anthropic, to absorb the culture and build a network* Why the next generation of Canadian founders should not apologize for building here* The “Build, Follow, or Get Out of the Way” mentality for the Toronto tech ecosystemAbout Rahul ParmarRahul Parmar is the Head of Go-to-Market at Railway, a developer experience first cloud platform, and a Venture Partner at Ripple Ventures. Previously, he was the first sales leader at Drata, where he scaled the sales team from Series A to Series C. He was also one of the first sales hires for Google Cloud in London. He holds a degree from business school and built Canada's largest coding bootcamp, Lighthouse Labs, before his career in high-growth SaaS.Connect with Rahul Parmar: https://www.linkedin.com/in/rahulparmargtm/Visit the Railway website: https://railway.com/Connect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

We're back for Season 2 of The RevOps Review, where we sit down with the operators, innovators and revenue leaders shaping the future of go-to-market.In this episode, Steve Dinner, VP of Revenue Operations at Owner.com, joins Jeff Ignacio to unpack what it actually takes to rebuild a RevOps function around AI – not just bolt it on. Steve shares how his team governs LLM access to Salesforce, why he treats every workflow as a "nano-service" instead of one big automation project, and how they're rethinking sprint planning now that AI can ship changes daily. He also gets into the real friction points: managing token spend across the organisation, avoiding duplicate rep-built workflows, and using AI to gamify BDR performance without losing the human side of the job. He provides a practical look at what AI-native operations actually look like inside a fast-moving GTM organisation.

175% Growth, 120 Countries, Agents as Buyers: Stripe's CRO of AI on The New AI GTM Playbook The fastest-growing AI companies are rewriting every rule in the GTM playbook, and Stripe has a front-row seat to all of it. In this episode, Maia Josebachvili, Chief Revenue Officer of AI at Stripe, breaks down the four patterns she's seeing across the world's top AI companies, and what they mean for how you build, price, sell, and scale. The numbers alone are staggering: top AI companies grew 120% in 2025 and 175% in 2026. Lovable went from $100M to $400M in eight months. Cursor hit a $2B run rate in under two years. And 48% of revenue at top AI companies now comes from outside their home market. Maia covers: Why the fastest AI companies are in 42 countries on day one and 120 by year three How pricing is evolving from seats to usage to hybrid models, and why getting it wrong kills retention Why enterprise sales is now a year-one problem, not a year-five one What it actually means that agent traffic to Stripe's docs 10x'd in a single year How to build systems that move with your customer across PLG, enterprise, and agent-led motions If you're building an AI company or trying to compete with one, this is the data you need. This episode is brought to you by Northwest Registered Agent.  Starting a business can get expensive fast. Website here. Email somewhere else. Business phone with another provider… Northwest Registered Agent gives you a complete Business Identity in one place — with free tools, resources, and built-in privacy from day one. Get more at northwestregisteredagent.com/SAASTRFREE

Join Mark Gordon as she interviews Mark Gordon, the Rebel CRO, on CTR Media Network. Discover strategies to overcome business growth barriers, focusing on clarity, alignment, and the core four system. Learn how to transition from a founder-led sales approach to building a team of leaders. Mark shares insights on delegating responsibilities and setting measurable goals for sustainable success. Tune in for valuable advice on scaling your business and enhancing leadership skills. https://igtms.com/contact-usThe Core Four GTM Transformation: Aligning your Core Four is our 120 days GTM transformation: Messaging, Lead Generation, Sales Execution, Revenue Technology.

What makes a senior GTM leader leave a successful career behind and bet on a company taking on one of the biggest challenges in AI? In this episode of Hunters and Unicorns, hosts Ollie Kuehne and Simon Kouttis sit down with Alex Varel, EVP of GTM at Cerebras, to unpack the decision that brought him into the world of AI infrastructure. Alex shares what he saw in Cerebras, why the demand for AI compute convinced him, and what he learned from moving between companies like MongoDB and Cerebras. In this episode: • Why Alex chose Cerebras over other opportunities • What makes Cerebras' AI infrastructure different • Taking on established players like NVIDIA • The importance of product differentiation in GTM • What Alex saw when he first met Cerebras' leadership • Why challenging markets attract great sales leaders • The mindset behind building and scaling a GTM organization A conversation about AI, enterprise sales, GTM leadership, career decisions, and what it takes to bet on a massive opportunity before everyone else sees it. Subscribe to Hunters and Unicorns for more conversations with the leaders building the future of technology and sales. About Our Sponsor: Big thanks to Aurasell for sponsoring this episode. Aurasell is an AI-native GTM platform designed to intelligently automate every aspect of the sales cycle—from prospecting and deal management to forecasting and coaching. Timestamps: 0:00 — Trailer 1:45 — Welcome & Introduction 4:17 — How Did You Know Cerebras Was the Right Bet? 8:46 — How Cerebras Came to His Attention 11:37 — Meeting Andrew Feldman & the Taco Story 17:10 — Getting Up to Speed on a Technical Product 20:14 — Tackling a Highly Technical Sale 22:32 — Shifting From Feature Selling to Value Selling 26:35 — Growth & Scale — What It Looks Like Now 27:50 — How He Sees the Market Changing in 3 to 5 Years 32:03 — How He's Had to Change as a Leader 34:20 — Hanging Back & Proving Himself First 37:25 — What From the Old Playbook Transferred 43:34 — The No BS Culture at Cerebras 48:08 — Why You Don't Need a Large Sales Team 49:31 — European Expansion 51:42 — Advice for Anyone Making the Hardware Leap 54:30 — How to Assess a Comp Plan 56:47 — Imposter Syndrome at Cerebras 57:53 — Closing

En este nuevo “Punto de lectura” reseñamos los tres libros de arte de VERMIS, unos manuales de un videojuego de rol que no existe... También el nº 129 de la revista GTM, y las siguientes novedades de Panini: Planeta Hulk y Animal Man de Gran Morrison 2. Esperamos que os mole. Enlaces: https://linktr.ee/reservademana

In this episode, Mairéad Morgan, community lead at Autodesk, shares her inspiring journey from architecture to digital innovation, highlighting key lessons on career growth, mentorship, and embracing technology in the AEC industry.   key topics Mairéad Morgan's career highlights and journey Mentorship and industry influence Digital transformation and AI in architecture Community building and industry support Challenges and lessons in technology adoption takeaways Hard work and persistence are key to career success. Mentorship and community involvement are invaluable. Digital transformation requires understanding diverse needs. Adapting to fast-paced AI developments is crucial. Prioritize well-being and work-life balance. Keywords architecture, digital transformation, Autodesk, BIM, community building, mentorship, career highlights, AI in AEC, industry innovation   Chapters 00:00 Introduction and Mairéad Morgan's background 02:08 Career highlights and full circle moments 03:57 Mentorship and industry influence 06:58 Proudest moments and achievements 09:07 A day in the life of a community lead at Autodesk 11:53 Digital transformation and technology adoption challenges 15:50 Lessons for smaller firms and tech ecosystem 19:00 Advice for aspiring professionals and industry insights 23:05 The future of AI and digital innovation in AEC 30:02 Well-being, personal growth, and rapid-fire questions  

How do you keep up when you go from 50 to 500 employees in a year?Jesse Zhang shares how Decagon scaled that quickly, from hiring ahead of demand to keeping the company connected as the team grows.He also explains why the zero-to-one phase is so different from scaling, and how Decagon found its way out of the idea maze in just three weeks.Guest: Jesse Zhang, Co-founder and CEO, DecagonConnect with Jesse ZhangXLinkedIn Connect with Joubin:XLinkedInEmail: grit@kleinerperkins.comFollow Grit on LinkedInX​Learn more about Kleiner Perkins

AI gives confident answers without the context to back them up. Kelly Hopping, four-time CMO and current CMO at 6sense, breaks down where AI's missing context creates risk for go-to-market teams. She explains how GTM intelligence platforms fill gaps that generic AI models miss, why context depth matters more than model size, and what marketers need to validate before trusting AI-driven insights across the customer journey.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI gives confident answers without the context to back them up. Kelly Hopping, four-time CMO and current CMO at 6sense, breaks down where AI's missing context creates risk for go-to-market teams. She explains how GTM intelligence platforms fill gaps that generic AI models miss, why context depth matters more than model size, and what marketers need to validate before trusting AI-driven insights across the customer journey.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

What happens when a seasoned GTM leader becomes CEO for the first time and steps into a founder-led company with a global team?  In this episode, Eric Anderson, CEO of Walnut, shares what it's really like to take over a business built by someone else. He talks with AJ Bruno about building trust across time zones, rebuilding the go-to-market motion, and making bold decisions while honoring legacy culture.  From listening tours to tough calls on pricing and segmentation, Eric opens up about the challenges and wins of his first six months in the role. Eric talks about: - What it's really like to step into a founder-led company as a first-time CEO - Building trust and alignment across a global, distributed leadership team - Why CEOs need to be willing to make bold and unpopular decisions - The challenge of walking away from revenue that doesn't fit the business - How pricing and market segmentation can force difficult but necessary changes - Why rebuilding the GTM organization can unlock a company's next stage of growth Chapters 00:00 Introduction to Eric Anderson and Walnut 02:30 From GTM Leader to First-Time CEO 06:49 Leading Across Cultures and Time Zones 09:11 The Boardroom: A New CEO's Balancing Act 11:36 Why CEOs Have to Make the Hard Calls 15:00 The Cost of Trying to Serve Everyone 16:52 Rebuilding Walnut for What's Next 18:30 Where to Find Eric and Walnut Try Walnut: walnut.io

Most AI playbooks are built for a workforce tethered to a desk, but many workers are not near a keyboard. In this episode, Beth Miles, Chief AI Officer at Advantage Solutions, breaks down how she's building an AI-ready culture across a distributed workforce of more than 60,000 teammates. She shares why AI literacy has to start with leadership, why redesigning a broken process matters more than simply automating it, and how she measures AI success well beyond adoption metrics.Key Moments:From Chief of Staff to Chief AI Officer (05:00): Beth explains how her chief of staff role gave her the cross-functional view needed to build Advantage's AI office.Hands-On Keyboards: AI Training in the C-Suite (11:00): Beth breaks down 10+ hours of executive AI training, including a CEO who sat down for a coding workshop.Inside the AI Champions Build Summit (22:00): Over 40 teammates spent five hours building AI use cases together, breaking down silos across functions.Why You Shouldn't Optimize a Bad Process (26:00): Redesigning broken processes before automating them matters more than speed.Adoption Is Step One, Not the Finish Line (31:00): Usage is only step one. Beth points to service excellence and time to complete as the metrics that matter.Key Quotes:“AI can be an accelerant, but you have to have all the mechanics and the process and the data to actually make it run really well.” - Beth Miles“It may require moving slower now to move faster later, to actually redesign and rethink work so you're not optimizing a bad process.” - Beth Miles“ To lead a change, we need to understand it.” - Beth MilesMentions:AI Transformation Requires Redesigning Work, Not Cutting RolesEast of Eden by John SteinbeckShoe Dog by Phil KnightGuest Bio:Beth Miles leads Advantage Solutions' AI strategy, enablement, and governance to drive value for the company's clients and teammates. She drives AI deployment across the enterprise, alongside the Data Office and Growth & Strategy Office. Beth joined Advantage in early 2024 and has served the last year as Chief of Staff, leading the company's AI workstreams from diagnostic through scaled execution, driving enterprise transformation initiatives, and overseeing CEO Office operations.Before Advantage, Beth spent more than a decade leading technology and corporate transformation at global software firms, including senior roles at Sage and Cerner (now Oracle Health). At Cerner, she drove the redesign of Cerner's portfolio management process for their $400m R&D investment. At Sage, she led GTM execution across new revenue streams and managed a large cloud partnership, leading cross-functional teams across governance and delivery. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

AIUC first got our attention with the NFDG backing, and have just announced a $40M series A today, with the most impressive industry advisor list we may have ever seen for an early startup behind AIUC-1, their agent standard backed by real insurance:From being Anthropic's first product hire to building the standards, testing, and insurance infrastructure meant to make frontier AI deployable, Rune Kvist is betting that the biggest constraint on AI adoption won't be capability it will be trust. In this episode, the AIUC cofounder joins swyx and Vibhu to announce a new $40M round and explain why companies like Cursor, Harvey, Lovable, and ElevenLabs are increasingly confronting a problem that gets harder as AI gets better: who is responsible when autonomous systems fail?We go deep on AIUC-1, the emerging standard for agent security, safety, and reliability; how AI agents are stress-tested for jailbreaks, hallucinations, and data leaks; and why Rune thinks standards and insurance could become critical infrastructure for AI. We also discuss the growing trust gap between governments and frontier labs, AI-enabled cyber and biological risks, why every model can ultimately be jailbroken, what happens when a $20 coding agent causes $200M of damage, whether AI engineers should be certified, and why even after AGI there may be one job the labs can never do themselves: be their own watchdog.We discuss:* Why risk, liability, and trust may become the binding constraint on AI adoption* Rune's path from reading the Scaling Laws paper to joining Anthropic in its earliest days* What Anthropic understood about scaling, compute, and the future years before it became obvious* Why Waymo illustrates the gap between AI capability and real-world deployment* AIUC's $40M round and work with Cursor, Harvey, Lovable, ElevenLabs, and other frontier AI companies* AIUC-1: a standard for AI agent security, safety, and reliability* How agents are tested for jailbreaks, hallucinations, and data leakage* Why most AI companies optimize the happy path without seriously stress-testing adversarial cases* Why AI standards may need to update every quarter instead of every decade* The emerging trust gap between frontier AI labs and governments* Cybersecurity, child safety, biological weapons, and the expanding frontier-model risk surface* Why standards and insurance may need to evolve together* How Lloyd's of London can insure AI systems and bring trust to enterprise deployment* What happens if a $20 Cursor subscription contributes to a $200M plane crash* The Air Canada chatbot case and how AI failures are beginning to clarify legal liability* Why copyright may be one of the hardest AI risks to insure* Evals, mechanistic interpretability, monitoring, and models becoming aware they're being tested* The impossible CISO mandate: adopt AI fast, but don't let anything go wrong* Why robotics will make AI liability dramatically more consequential* Whether AI engineers should have Level 1, 2, and 3 certifications* AIUC's roadmap across agents, frontier models, robotics, and universal red teaming* Why AGI could become a question of national sovereignty* Why the labs can never fully serve as their own watchdogs* The Big Short problem: how do you stop competing watchdogs from racing standards to the bottom?Rune Kvist* LinkedIn: https://www.linkedin.com/in/runekvist/* X: https://x.com/RuneKvistAIUC* https://aiuc.comTimestamps00:00:00 AIUC's $40M Round and the Risk Bottleneck for AI00:01:07 From Scaling Laws to Early Anthropic00:07:58 Why Trust, Not Capability, Could Limit AI Adoption00:12:19 Founding AIUC and Building AIUC-100:18:52 How AI Agents Are Audited and Stress-Tested00:25:26 Frontier Models, Government, and the AI Trust Gap00:33:32 Cyber, Child Safety, and AI-Enabled Biological Risk00:38:14 Why Standards and Insurance Belong Together00:41:45 What Does an AI Insurance Policy Actually Cover?00:50:44 The $20 Cursor Subscription and the $200M Plane Crash00:53:53 AI Liability, Monitoring, and Earning Enterprise Trust00:56:21 From AI Agents to Models to Robotics00:58:29 Copyright, Adverse Selection, and AI Insurance01:03:28 Evals, Mechanistic Interpretability, and Eval Awareness01:08:36 The Impossible Enterprise AI Mandate01:11:52 Prediction Markets vs. AI Audits01:14:43 Should AI Engineers Be Certified?01:19:10 AIUC's Roadmap, AGI, and Who Watches the Watchdogs?TranscriptIntroduction: AIUC, the $40M Series A, and Risk as the Adoption BottleneckSwyx [00:00:00]: Okay, we're in the studio with Rune from AIUC, the Artificial Intelligence Underwriting Company, with our trusty co-host, Vibhu. Welcome.Rune Kvist [00:00:10]: Thank you. Thanks for having me. Thank you.Swyx [00:00:11]: What are you announcing today?Rune Kvist [00:00:12]: We have raised $40 million, led by Ribbit Capital and First Harmonic.Swyx [00:00:17]: You first came to my attention when Nat and Daniel invested in you guys. Is the story, like, pretty much the same? Like, what are you today versus what you thought you were back then?Rune Kvist [00:00:26]: When we raised our seed round, we had a hypothesis that at some point risk was going to hold down adoption. At that point in time, that felt kind of hypothetical, and I think that is now over. Clearly, the moment is now with Mythos and Fable. It's pretty obvious that literally the binding constraint on adoption is risk. And so for us, it feels like this is a natural continuation of the same hypothesis, but where previously it was speculation, now it feels like fact.Swyx [00:00:54]: And let's get a list of the customers that you're highlighting as part of your Series A.Rune Kvist [00:00:58]: Totally. Yeah. So we are now working with folks like Cursor, Harvey, Lovable, ElevenLabs.Swyx [00:01:05]: Yeah. Amazing. Congrats.Rune Kvist [00:01:06]: Thank you.Swyx [00:01:07]: So you were famously one of the first hires involved in GTM and product. I'm just kind of curious: what was your path into AI? Just recap.Rune's Path Into AI: Scaling Laws, Capital, and AnthropicRune Kvist [00:01:18]: Yeah.Rune Kvist [00:01:19]: Late 2021, I sold a company, my first company, an edtech company. I had a bit of time to think about what was next. I came across the Scaling Laws paper, and that just struck me like lightning. I was just like, “This is a big idea.” In short, the Scaling Laws paper just says the bigger the model, the smarter the model.Swyx [00:01:38]: So this is the Kaplan one, not the Chinchilla one?Rune Kvist [00:01:40]: Exactly, the Kaplan one.Swyx [00:01:42]: Yeah.Rune Kvist [00:01:42]: And the important thing that clicked for me there was, oh, now capital will understand this. If you put in more money, you get more money out, and so that will kick off a hype cycle. And so you get a sense of predictable returns, which is, in fact, what's played out. And so I just packed my bags. I'd never been to San Francisco. I'd never been there. I just packed my bags, flew out here to find the people who had written it. And at the time, they had just started a small lab called Anthropic. There were around 40 people at the time or so. Drank a bunch of coffee until I eventually got introduced to Dario. And at the time, they were wrestling with some of these questions of, like, should we deploy our models? Should we make revenue? How should we engage with the rest of the world? They'd just broken off from OpenAI, and it's been publicly reported that they were kind of concerned with how they were dealing with deployment. So they were wrestling with some of those questions. At this point, this is early fog of war, like early 2022. The hottest product at the time was, like, Jasper. Like, there's nothing out there. So where value was going to accrue, and what the different parts of the stack were going to be, were all open questions.Swyx [00:02:48]: I want to highlight to people, you ask these questions because you have a PPE background.Rune Kvist [00:02:52]: Yes.Swyx [00:02:52]: I actually was in Singapore in one of the sort of feeder programs for prepping people for PPE. So I had a tutor. We learned, you know, philosophy and politics and economics. But, like, I think your kind of background matters. Machine learning people who read the neural, Scaling Laws paper would not necessarily draw the same conclusions that you did. Whereas any capitalist would read that and go, “Holy s**t.”Rune Kvist [00:03:19]: Correct.Swyx [00:03:20]: Right?Rune Kvist [00:03:21]: Yes.Swyx [00:03:21]: Who tipped you onto that paper? Because it's not a paper that you normally read, right, like, in your circles?Rune Kvist [00:03:26]: Yeah. I think I'd actually, ever since AlphaGo, had some appreciation that AI was a big deal.Swyx [00:03:36]: Yeah.Rune Kvist [00:03:36]: But it kind of felt like it raised all these kind of interesting philosophical questions, but it was kind of not clear from afar where exactly that would go. But it was obvious enough that it was like, this is going to be a big thing if we find the kind of right mechanism to kind of get the techno-capital machine to work on this. But it was just not clear. And so I think there was some way in which, like, that became obvious, and also it wasn't as obvious at the time than it is now, right? Like, it was just like, wow, this is so interesting. But it still felt, coming from kind of a philosophy and economics background, it felt like if this turns out to be true, you're going to be wrestling with all of the big questions in society. Everything you've learned about politics gets thrown out of the window. Everything you've learned about economics at least gets challenged. And so what felt interesting was to be at that frontier that has ramifications across everything. So that's why I sought it out.Swyx [00:04:32]: I mean, clearly really good insight. For people who don't know, the PPE program is, like, where prime ministers are born. So then you end up meeting Dario.Rune Kvist [00:04:41]: Yep. First Dario, yeah.Swyx [00:04:43]: Yeah. Well, I mean, like, so did you get extra insights from talking with them that you didn't get from your original hypothesis?Anthropic's Early Conviction and the Scaling Laws Crystal BallRune Kvist [00:04:50]: If you read the Scaling Laws paper, you get this, like, very vague sketch of like, wow, this seems kind of important. There are some lines on a chart. This seems kind of important. And what I think the team at Anthropic had thought more about than anyone was like, what are the implications of this if you really play this out? And back then they had, kind of vision documents for what the world would look like in 2026, and they were kind of in vivid detail playing out how much compute is going to be needed, what the CapEx was going to look like, what some of the societal concerns were going to be, but also what is the amount of economic value coming out here? And so it kind of felt like they held a crystal ball that in hindsight turned out to just be dramatically correct. And they weren't holding it like they were obviously correct. They were just like, “Take this hypothesis really seriously.”Swyx [00:05:38]: Think it through, yeah.Rune Kvist [00:05:38]: And think it through in the same way as the kind of situational awareness that isSwyx [00:05:43]: Across the street.Rune Kvist [00:05:44]: Across the street.Swyx [00:05:44]: Your office, yeah. Oh my God, we're all living across the street in the same one square mile.Rune Kvist [00:05:50]: Correct. And that's now a couple of years old, but also people keep referencing it these particular weeks with Fable and Mythos, and it's like, wow, if you take this one idea seriously- For the Scaling Laws, a lot of things fall into place.Vibhu [00:06:03]: And keep in mind, at this point, this is the same team that did GPT-1, GPT-2, and GPT-3.Rune Kvist [00:06:08]: Correct.Vibhu [00:06:08]: Which is also, like, it's not just some experimentation. Like, this is a real model that we just scaled up.Rune Kvist [00:06:14]: And they had deep conviction in this idea: if you take a big blob of compute and data, it just wants to learn, and out of that will come smarter and smarter models. And all the particulars were not clear.Vibhu [00:06:26]: Yeah.Rune Kvist [00:06:27]: And all the implications were not clear. But their deep conviction in this, like, core thesis, and that was kind of dizzying. It was both phenomenally interesting and exciting, and also very quickly you get to, like, the world we know today will no longer be if this hypothesis holds. So it also just felt, like, important in some kind of grand sense.Vibhu [00:06:48]: What kind of shaped you there? So that was early 2022. Not only had GPT-1, GPT-2, and GPT-3 come out, but, you know, the amazing founders of Anthropic that have never split up, the only ones, they actually had the conviction to leave OpenAI, start their lab. You said there were about 40 people there. What was the time like there?Inside Early Anthropic: Mission, Deployment, and RiskRune Kvist [00:07:06]: It was kind of remarkably like what it looks like on the outside today. Extremely cohesive, extremely mission-oriented, and living in this tension between their two ideas, which is AI could both go really well and really bad, and we want to be part of building it. That creates astounding amounts of tension. And they were wrestling with this incentive challenge where they know they're in a race that they're in where you might get forced to cut corners, but it also felt very important to them to be at the forefront of technology. And all of those ideas were just present at that time. It kind of feels like that line has been just very clear, and I think kind of love them or hate them, they have really stuck to their guns. There's a core set of beliefs that they hold more deeply than most companies hold any beliefs.Vibhu [00:07:58]: Yeah. Fast-forward to today.Rune Kvist [00:08:00]: Yeah.Vibhu [00:08:00]: What does that lead us to AI underwriting company? What are you up to? What motivated you to start this?From Waymo to AIUC: Confidence Infrastructure for AIRune Kvist [00:08:05]: Yeah. AIUC builds confidence infrastructure for frontier AI through standards and insurance. The link from Anthropic to building confidence infrastructure, looking out the windows at Anthropic offices and seeing Waymos driving by. Already back then, early 2022, Waymos were in some ways like AGI for cars. Like, they were superhuman drivers, but you couldn't take one to the airport. And now, four and a bit years later, you still can't take your Waymo to the airport, despite now everyone having kind of looked at the evidence and being like, “They're better drivers than humans.” So in that particular instance, what's clear is that the binding constraint on AI being useful is not capability, but is that liability or risk or trust. That problem is, general. The reason why right nowRune Kvist [00:08:52]: Fable is not open for access is not because it's not a good model, it's because it's a very good model. It's just hard to make promises about what it will or will not do. And this problem gets worse as AI gets better. Basically, more intelligent AI can be more autonomous. That's more valuable, but also the risk surface grows. And so - what Waymo illustrates is that unless you build the confidence infrastructure to make promises about AI, or at least bring light to the risks, you grind adoption to a halt. Governments, banks, hospitals, militaries need to have some sense of what AI will and will not do to be able to operate for them to incorporate it. And that's the problem that we're trying to solve. Now, why standards and insurance? If you trace this problem back through history, every technology wave has had some version of this problem. So if you go back to, like, year 1900, electricity comesVibhu [00:09:47]: Ben Franklin.Rune Kvist [00:09:48]: Cars burn down, sorry, houses burn down, lots of people die. 1930s, cars are a big deal, kill lots of people. 1950s, private nuclear energy is a big deal, poses big risks. In each of those instances, the market runs ahead of regulation to create confidence infrastructure because that's required to make go/go decisions. That is required for adoption, and the market fundamentally wants adoption. And in all of those instances, common blueprint emerges between standards and insurance. The reason these two components is standards kind of provide the rules of the road, and they also specify, like, what are the tests that need to be run so we can get a sense of how high the risk is. So take in the case of cars, that's like a car crash. Great, everyone, they inform your insurance pricing today, they inform your purchasing decisions, et cetera. That's basically the risk framework. The insurers are important because they pick up the bill. So they are the private institution that is most on the side of. That is best incentivized to quantify the risks truthfully and then figure out all the ways to reduce the risk ‘cause that increases their profit. So they're basically, they help shape the incentives. And these two work really well in unison. Now, how does that show up as a company? Well, one of the things that was obvious even - or starting to become obvious even a couple years ago was that frontier companies, some of our customers today, like Cursor, Sierra, ElevenLabs, Harvey, were going to have a very easy time selling a pilot to a bank. The, like, the demo just sells itself. It's magic. But bringing that through, if you want to do a wall-to-wall rollout at a bank or a hospital, you have to go through the risk process. These banks have no idea even which questions to ask, let alone which answers are sufficient, let alone, like, how do they go and test whether these agents actually work the way they're supposed to. And so they had this problem of, like, what can we say to earn the trust? And we think there's, like, a golden sentence that goes something like, “Hey, I hear you're really worried about hallucinations or jailbreaks or whatever it may be. We've had an independent third party test us against the gold standard. We passed with flying colors. And as a vote of confidence, the world's most conservative insurers have looked at the data.” And they're willing to take some of the risk onto their balance sheet.Swyx [00:12:06]: Yeah.Rune Kvist [00:12:07]: So if something does go wrongSwyx [00:12:07]: There's money behind it, yeah.Rune Kvist [00:12:09]: Exactly. So that's kind of like the link between all this. We can get into some of the hard parts related to the technical testing, which is, I think, the crux of the matter, but I'll pause there.Swyx [00:12:19]: How did you and Rajiv come together? This-- there's always, like, you come across very confident and, you know, and we're announcing your Series A and all these things, but I want to see, like, the early initial stages of, like, idea formation.Cofounding AIUC with Rajiv DattaniRune Kvist [00:12:31]: Yeah. Rajiv is actually my soon-to-be brother-in-law.Swyx [00:12:35]: Oh.Rune Kvist [00:12:36]: So I'm actually, in a week and a half getting married to Rajiv's sister.Swyx [00:12:42]: Okay, now you're tight.Rune Kvist [00:12:44]: Exactly.Swyx [00:12:44]: Now you know.Rune Kvist [00:12:45]: So - Rajiv and I have known each other for a decade. Funny story, I met both Rajiv and his sister, Hena, at the same time when Hena and I were interns at McKinsey in London, and Rajiv was assigned as my mentor. And so met them at the same time. For the longest time, it was not obvious that we were necessarily going to work together. I was in startups. He was, an insurance partner at McKinsey. Three or four years ago, I think Hena convinced him that AI was going to be a really big thing. And so he quit his job, cushy partner job at McKinsey in London, packed his bags, flew to San Francisco, and ended up joining METR. You guys are probably online enoughSwyx [00:13:24]: CEO.Rune Kvist [00:13:24]: Exactly.Swyx [00:13:24]: We've, we've, we've heard of METR.Rune Kvist [00:13:25]: You see the plot-- the chart of the horizons of the tasks that agents can take on is doubling extremely fast. So he was COO at METR, led their partnerships with Anthropic and OpenAI to test their models before release, but also working closely with the US and UK government, to figure out, like, how do you know whether a model can be released? And in some ways, that was, like, the perfect background. He's spent a lot of time in insurance, knows that world, spent a lot of time with frontier testing of models. And so when I was bumbling around this idea space, starting with some of the ideas we talked about related to Waymo, as soon as we got into the content, we were both like, “Oh, this would be an amazing business to build together.” This is wrestling with the problem that we both think is the most important in the world from a market angle, which is kind of our intuitions is that the market can do a lot, and the faster AI moves, the harder it is for government to solve some of these problems. And then it took a little bit of time to work through what is it like to work with family.Swyx [00:14:27]: Sure.Rune Kvist [00:14:27]: And,Swyx [00:14:30]: Because you were already dating at the timeRune Kvist [00:14:31]: Yeah. Yeah, exactly.Swyx [00:14:33]: Yeah.Rune Kvist [00:14:34]: Already back then, itSwyx [00:14:35]: Yeah.Rune Kvist [00:14:35]: We felt like we were a family.Swyx [00:14:36]: Nice.Rune Kvist [00:14:36]: And so starting a business together felt like kind of a big step. And, here we are with just immense amounts of trust.Vibhu [00:14:43]: Yeah. So now you're a company of how big? How big are you guys now?AIUC-1 Certification: Agent Security, Safety, and ReliabilityRune Kvist [00:14:46]: There are just 20 of us now.Vibhu [00:14:47]: 20 of you guys now, have Series A, and you have your first certification out, the AIUC-1. Let's bring up the certification. So this is the agent certification, right? What goes into the process? I have, like, two questions here. One is, walk us through the certification, and two is, what is the process for a company to get certified, you know?Rune Kvist [00:15:08]: Great. As it says right on the top, AIUC-1 is a standard for agent security, safety, and reliability. The fundamental design principle is take all of the concerns that slow down adoption, so all the questions, all the fears that keep, security leaders in the Fortune 1000 up at night, and put them into one comprehensive framework. That's what you'll see there. You can see the six categories. Two, you want to ground all of this in technical testing. So one of the concerns with security standards that often feel kind of like theater paperwork is that they're not actually ground out in, does any of this work? Does any of this matter? And so we had a conviction from early on that was going to be the kind of crux, was to pass this, you must get tested every quarter, basically run thousands of simulations to see, well, so can it actually be jailbroken? How hard is it to jailbreak? How often does it hallucinate? How often does it leak data? Et cetera. And then the last, core idea here, if you scroll up to the top here, is to refresh it quarterly.Rune Kvist [00:16:08]: So the core trait of AI is that it moves extremely fast. Whatever concerns we're discussing today were not the same ones three months ago, and this will keep changing. Typically, standards update on a, like, a decade cycle is obviously not going to work. But the question is kind of how do you update it? And the core thing here was to basically get the risk leaders of the Fortune 1000 around the table. So if you go over to the left hereVibhu [00:16:32]: YeahRune Kvist [00:16:32]: You'll see the AIUC-1 consortium. The consortium is a group of risk leaders who run real banks, real hospitals, real critical infrastructure, who are facing these challenges every day. And we meet with these folks twice a quarter and hear what's top of mind, what is keeping them up at night. There's tremendous amount of desire for that conversation. And then we operationalize that into a specific standard that gets into. And actually, we can go into and look at whatVibhu [00:16:55]: YeahRune Kvist [00:16:55]: What even is the standard. So if we go back to introduction, out there to the left, scroll up a little bit to the wheel, click into reliability. So if you take something like hallucinations sits in reliability. There is a number of requirements here. If you go into the top one, prevent hallucinated outputs, hallucinate outputs, this is one particular requirement. This is a technical control. Basically, we want some kind of ground in this filter. The first thing you see here is what's called a crosswalk. So everyone and their grandmother has put out a framework, very high-level framework for what are the AI risks.Swyx [00:17:27]: This is basically your competition,Rune Kvist [00:17:28]: In some ways our competitionSwyx [00:17:29]: Not seriously, yeah.Rune Kvist [00:17:30]: We're, in fact, friends with them. We'll come back to why.Swyx [00:17:31]: Yeah.Rune Kvist [00:17:32]: But mapping everything together so you have one superset. The claim you're trying to support here is, if you follow this framework, then you can also see how you follow the other frameworks. But the meat of it comes down here in control activities and evidence. So control activities is like, great, you have this high-level requirement. How do you turn that down to something operational? Here's what you must do, and then what is the evidence that we're looking for?Rune Kvist [00:17:57]: And the reason we go this deep is that there's actually not that much confusion about what are the big concerns in AI. Everyone agrees to these. The question, like, what are you actually supposed to do? And so. What we found a lot of demand for is getting down to the specific evidence, that people need to look for. Whether you are Cursor building something or, even JPMorgan building something, but also if you're just a risk leader at JPMorgan, like what exactly should you ask for? What can you ask for without sounding stupid? Like if you ask for some-- you won't believe the amount of time a risk leader has asked for the IP rights to the underlying model to Cursor or something, and you're just like “Sorry, what?” Like,Swyx [00:18:39]: You slip it in there and you seeRune Kvist [00:18:40]: SlipSwyx [00:18:40]: See if you notice.Rune Kvist [00:18:41]: See if they. Exactly.Swyx [00:18:42]: Yeah.Rune Kvist [00:18:42]: Put that in the questionnaire. All right, so that's kind of what our standard is, and we update this every quarter with these folks, to keep up with the latest concerns.Swyx [00:18:51]: Can I double-click on this one?Controls, Evidence, and Third-Party TestingRune Kvist [00:18:52]: Yeah.Swyx [00:18:52]: So first of all, the website's beautiful. Like, it's so confidence-inducing which is the whole point where, like, okay, I know exactly what I'm signing up for when I talk with you. Like, I don't even have to talk to you. I can just see your whole, certification, which is great. But, like, okay, so from here, like D001.1 configure a groundedness filter, how does that get applied? Like, you have a person thatRune Kvist [00:19:16]: Yeah,Swyx [00:19:16]: Goes through it?Rune Kvist [00:19:17]: If you, go backVibhu [00:19:19]: I did see somewhere there's like, you know, fifty-one requirements, a hundred thirty controls. There's like a wholeSwyx [00:19:25]: Right. I just want to. Like, to me, this doesn't translateVibhu [00:19:27]: Yeah.Swyx [00:19:27]: Into a test or an eval.Rune Kvist [00:19:28]: Yes. So if you go into, on the left-hand side. So actually, if - before we go in there are three types of requirements. The first is technical controls, like you must implement some guardrails.Rune Kvist [00:19:42]: Two, there are test controls. So you must have an independent third party go and run some tests against you. I'll show you one of those in a second. And then three, there are policy controls. For example, you must have a person whose name is on the line when you guys f**k up, and you must have a plan for how you tell your customers and how you engage with them. They're kind of more traditional, standard type stuff. So in this particular instance, we just check whether they in fact have a ground in filter. So we will partner with an auditor. So we partner with auditors like KPMG or like Schellman who go in and do the thing auditors do, which is to check the evidence. In this case, that might be a screenshot, it might be part of the code that they need to review to see that it actually. Just that it exists.Swyx [00:20:21]: Oh, okay.Rune Kvist [00:20:22]: And then the second thingSwyx [00:20:22]: So you're not testing the effectiveness of it.Rune Kvist [00:20:24]: That's the second thing. So if you go downSwyx [00:20:25]: Yeah.Rune Kvist [00:20:25]: To the third-party testing for hallucinations out on the left, that's basically the next requirement. This is where we test how well does it actually work.Swyx [00:20:32]: Okay, and is it you testing or the auditor?Rune Kvist [00:20:34]: We test them.Rune Kvist [00:20:35]: We test them.Swyx [00:20:36]: That's a lot of work.Vibhu [00:20:37]: How long does testing take? So if I want to get certified, justCertification Timelines, Remediation, and Quarterly UpdatesRune Kvist [00:20:40]: Yeah.Vibhu [00:20:40]: How long does the end roughly take?Rune Kvist [00:20:42]: Yeah, the end, almost always is dependent on, like, our customers needVibhu [00:20:47]: Yeah.Rune Kvist [00:20:47]: To look something for us. It takes somewhere between, like, 3 to 10 weeksSwyx [00:20:52]: Yeah.Rune Kvist [00:20:52]: Depending on how up to snuff they already are. So some people show up to us with, like, extremely rigorous security programs. When we test them, it works extremely well. We can get that done very quick. Some people come to us, and they're not that far along. We give them kind of the spec that they need to build towards, and then their security teams and engineers get to work and build to meet the standard. The testing itself typically takes a couple of weeks, including the time for them to remediate. Often, we'll find something that we cannot pass, where this is actually just not up to the standard. - you won't pass the standard. And then they will need to go and implement additional safeguards or additional remediation that makes them more robust so that they can actually kind of hand on heart look at their customers in the eyes and say, like, “Hey, we've done truly our very best.”Vibhu [00:21:35]: And they're certified for a year and have quarterly updates?Rune Kvist [00:21:38]: Correct, yeah.Vibhu [00:21:39]: And, yeah, it's pretty interesting. I think, you know, what's changed since. So this is certifying agents in production, right? Your customers, like you've had Lovable, ElevenLabs, Intercom, and they've all gone through this certification.Rune Kvist [00:21:50]: Yes.Vibhu [00:21:51]: What has changed? So I see you post, like, you know, Q2 added MCP agent,How Agent Risks Are Changing: Coding, MCP, and Agent-to-Agent InteractionsRune Kvist [00:21:56]: Yeah.Vibhu [00:21:56]: agent communication. Any other things that you want to kind of highlight since the first iteration? What comes in quarterly?Rune Kvist [00:22:03]: Yeah. So some of the changes have just been agents are not just one thing. So, like, if you take agents like Cursor and compare them to Sierra, they're really quite different. And compare them to Harvey again, compare them to you out of againSwyx [00:22:16]: ElevenLabs, yeah.Rune Kvist [00:22:17]: ElevenLabs, they're all quite different. And so we wanted to design a standard that works for all of the types of agents. And we started with one that was, like, pretty text-based, like, honestly, pretty customer support-focused. That's where there's a lot of existing demand. And then over time, we've picked, some of the frontier companies in each of these other domains that we could work with and build out the standard, so, such that we know that the same standard works for code, it works for customer support, works for automation, et cetera. So that's been one big thing. Yeah, then some of the things that have been top of mind recently, Mythos is bringing up a lot of concerns for security leaders. We're starting to get more and more questions around agent interactions. It's very nascent, at the moment, but it's starting to emerge. There've been a lot of, questions related to OpenClaw and MCP. Again, like agents starting to interact with each other, is really top of mind. Then as coding agents have really taken off, that's also where banks and hospitals, et cetera, are getting more and more precise on what it is they need. So really dialing in as that start to be, like, where most of the tokens flow through in the world, getting much sharper on that.Vibhu [00:23:26]: Can you share for people that are listening that don't really think about this? Like you mentioned, there's the obvious stuff, you know, hallucination, citations. What are best practices that people should do when building agents? Like, if they come to you pretty ready with certification like, you know, they'll probably pass certification. What are the things people don't think about that they should have?Best Practices for Agent Builders: Stress Tests and GuardrailsRune Kvist [00:23:46]: The most important thing is that a lot of companies have not done a serious stress test. They spend most of the time, perhaps rightly so, optimizing for how does it work in the good case, the average case, how high-quality is the output for the customer. And a lot of these companies are pretty new, so they haven't spent a lot of time stress testing the what is there as an adversary on the other side? What are some of the complicated corner cases that you've not really considered? So I think that's, like, a frame of mind. And you'll also see this in startups. It often takes a while until they hire their first security person. They- And that's a whole different kind of risk surface than just building a good product. So a lot of that applies. Most companies actually also have the right kind of architecture. Most of them will have some kind of guardrails in place, either some that come out of the box from their model provider or they'll have built their own filters that sit in between. They just don't work very well. The difference between putting a classifier in place that, like, maybe goes and checks whether you're giving medical advice when you shouldn't and says, “Hey, if this looks like medical advice, filter it out.” Lots of companies have that in place. The question is whether it works. And it's actually pretty fiddly to sit down and think about all the ways in which you could ask for medical advice, read the academic literature on what are the kinds ofRune Kvist [00:25:03]: Framings or tricks you might play to get an AI to give you medical advice when you really shouldn't. And so there's, like, an area of expertise that's just missing. So what we find is that most people have the right building blocks in place. They don'- It doesn'- It's not rocket science, but the finicky thing is, like, getting into the corners and testing whether it works such that you can look your customers in the eye, or maybe a bank or maybe a hospital and be like, “This is going to work for you.”Vibhu [00:25:26]: I see. So we talked a lot about the agent-level certification. Where do you guys go from here? So announcing series A camera, we talked about this a bit. There's the whole security risk of Fable, government stepping in. You guys are kind of announcing that you're also going into model certification?Toward Model Certification: The Government–Lab Trust GapRune Kvist [00:25:46]: When we do a bit of cutting afterwards,Vibhu [00:25:48]: YeahRune Kvist [00:25:48]: We will not yet be announcing this,Vibhu [00:25:49]: NiceRune Kvist [00:25:50]: The question that is top of everyone's minds now is at the model level. And Mythos, then Fable, has really brought this to the fore that in addition to the commercial risk and the kind of economic security risks that are happening at the agent layer, the models are going to present risk in the national security category. The shape of the problem is very similar. You have some people that are on the hook if something goes wrong. In the case of agents, it's often security leaders in the enterprise. In this case, it's the government. They don'- haven't necessarily spent their entire lives thinking about what are the new risks that come here, what is the kind of data you might be looking for, how might you test that? But they do have to make sure that their concerns are addressed. You have some frontier AI companies that are deeply technical. They know a lot about the risks, but they fundamentally have an incentive to not always be truthful. So you have a trust gap between the government and the labs. And in every other industry, you end up with some kind of body sitting between, a neutral third party sitting between those people. There's no other industry where you allow people to audit themselves. So there is going to be a need for a third party that can take the rigor of the labs to run frontier technical evals, but can also speak legible trust in the way that the government trusts PwC to go and run financial audits. And they know that they output audit reports in a way that's consistent, that's easy to read, that's factual, that's, trustworthy. Those two things need to be brought together. And what we've learned from our work with agents is that if you want those-- that communication between those two parties to be smooth, there has to be one common standard that is public, that people can go and inspect. What are the risks that matter? Within each of these risks, what are the kinds of threat models that you're really looking for? You need to specify for each of those risks, what are the guardrails that need to be in place, and what are the tests they need to run to see whether those guardrails are effective? And then you need to go and run audits that are - technical audits that are consistent. So if you're trying to bring trust, it's extremely important that you methodically work your way through the risks. You can't send one researcher in and say, like, “Come back with whatever you find.” You need to be able to explain exactly what you did, exactly what you tried, exactly what you did not try, and therefore the kinds of promises you can and cannot make at the end of it. I think ofNeutral Third Parties, CAISI, and Model Risk AuditsRune Kvist [00:28:13]: Fable as a direct symptom of this problem that the government was told that there's a risk. The government may struggle to assess just how big that risk is. They call Anthropic, and Anthropic is trying to tell them, “Hey, actually, every model can be jailbroken.”Swyx [00:28:28]: That's not what you want to hear, right?Rune Kvist [00:28:32]: As the government, that might be hard to trust.Rune Kvist [00:28:36]: And we think that a broker is the most natural solution. In other markets, you see something like, in financial markets, you see Moody's. Moody's goes in, and they look at a bond, and they output a rating. They say like, “Here's the evidence we found. Here's the rating.” We don't decide whether anyone should buy this bond or not buy this bond. Well, that depends on their risk appetite. But we do provide this common information layer that everyone can rely on. In the case of Moody's, the government, points to them and say, “Hey, pension funds, you should probably really take care. You shouldn't risk your pensioners' money, so you can only invest in triple-A rated bonds.” That means that now the government doesn't have to staff thousands of financial technical experts to rerun forecasts every week to see whether things are correctly rated. They get to point to some neutral third party. So my hypothesis is, my hunch is that you will see a third party that sits between the government and the labs, and it could either be the government builds it themselves. So something like CAISI was set up to do exactly this. And the questionSwyx [00:29:44]: Sorry, I'm not familiar with CAISI.Rune Kvist [00:29:45]: CAISI is the Center for AI Standards and Innovation.Swyx [00:29:49]: Okay.Rune Kvist [00:29:50]: I won't get into the details, but it's a body of NIST that typically sets standards. So it's basically a government body that has AI experts. Yeah, exactly. Exactly.Swyx [00:29:59]: Very key. Very key.Rune Kvist [00:30:00]: Very key.Vibhu [00:30:00]: I think, you know, it's one of those things where when you just sit back and listen-- look at it, like, is there enough technical expertise in the government to measure, test these things right now? Probably not, right? And Fable is a result of, okay, we've had to scale back and pause things,Rune Kvist [00:30:17]: Yeah. And they have excellent people, but they have an extraordinarily small budget compared to the scale of the challenge that's ahead of us. And I think they have a role to play. The question is kind of like, who does what? We have now outlined the jobs to be done, and they're quite extensive. Every model release, there is an astounding-- Given that they take in any input, their risk surface is astounding. And so the question is really: what can only the government do, and what can the market provide here that can keep up with the pace as AI risk changes? Our perspective is that also at the model layer, the risks that people care about today are not the same ones they cared about three months ago. So the pace of legislation is too slow to deal with pinpointing the risks here. And so we think there's a lot that the market can do to surface timely information. Ultimately, there is a bunch of policy decisions here. Is the national security risks of a model too high?Swyx [00:31:12]: Yeah.Rune Kvist [00:31:12]: That's a political answer. But what we want to make sure is that the process that produces this risk information is compatible with very fast innovation. So you don't want to. This is not a question of like, can you slow the things down? Can you keep, the models locked up until-- for months on end until everyone can make a guarantee? But it is this, can you, in the time it. Given that the US is competing with China on releasing models, can you insert risk information that allows the government to, like, make rapid decisions on some of these questions? Balancing that trade-off between failing to adopt AI is going to put us at risk, but also reckless adoption is going to put us at risk. And that's a very kind of fine balance that they're going to need, like, a lot of high-quality intelligence to make.Chinese Models, Data Flows, and National Security ConcernsSwyx [00:31:55]: Just a side mention, because you mentioned Chinese models, any specific concerns that you're hearing from your CISOs about that? ‘cause I guess it's free, but.Rune Kvist [00:32:05]: CISOs have a bunch of concerns around data flows in general that they're really concerned about. So there's a lot of questions like, if these models are Chinese, where does that, where does that data go? I think a lot of this can be addressed, but they come up often.Swyx [00:32:18]: I mean, they understand they're running on American GPUs.Rune Kvist [00:32:21]: Some of them, some of them understand that they're running on American GPUs.Swyx [00:32:23]: They're not, like, phoning home every time you, like, call home.Rune Kvist [00:32:26]: No. A year ago, there was not a lot of understanding of this. I actually think, you're seeing the security leaders becoming kind of AI literate at a blistering pace, and you're actually also seeing my Twitter timeline that's very pilled and my LinkedIn feed that used to not at all be pilled kind of converge. They're both talking about Fable.Swyx [00:32:45]: Right. Yeah, that's true.Rune Kvist [00:32:46]: They are both talking about whether you can prevent models from being jailbroken these days.Swyx [00:32:51]: Yeah.Rune Kvist [00:32:52]: Like national security national security risks are now the conversation that is actually emerging. Other than that, I think you mostly see a kind of general picture: there are no concerns with any particular model or any particular model output, but there is a general nervousness of having critical infrastructure run on models that are not produced in America by Americans where the American government has control.Swyx [00:33:14]: But it doesn't necessarily show up in your framework that directly, or it might, I don't know.Rune Kvist [00:33:18]: There's a bit of stuff in there actually on the, like, the provenance of the models and disclosing that. But I think there's a bunch of use cases where running a Chinese open-source model is just the best solution.Swyx [00:33:27]: Yeah.Rune Kvist [00:33:27]: And a concern is slightly more macro here, which is not best addressed at any particular certification level.Vibhu [00:33:32]: Is there anything interesting that you see at the. You know, if you're trying to fill that middle gap, that mediation gap, any interesting stuff that you guys forecast would be required other than, you know, what the average person might expect?Cyber, Child Safety, Bio Risk, and Expert CoordinationRune Kvist [00:33:47]: There's a bunch of interesting questions about what are the risks that matter here. So right now, the risk of the day is cyber, because it's very real, very tangible. And some of the risks that are also emerging as pretty real and pretty tangible are things like child safety is becoming both extremely important, but also politically important. And then there are some of the risks that are coming down the pipeline that today feel kind of speculative, but people who spend a lot of time with the models see them coming down is things like, risks that relate to biology.Rune Kvist [00:34:18]: And specifically whether models will help adversaries produce biological weapons and making that extremely cheap, extremely accessible, producing-- making the chance of another COVID or worse pandemic. COVID was not engineered to be bad, as if you were trying to do that. So I think those are some of the risks that are coming down the pipeline. I think one other thing to just note is that agents are kind of deliberately narrow. So, like, when a frontier agent company puts a chatbot that interacts with customers, they've really tried to narrow the topics it's interested in talking about. Such that if you ask it, like, “What do you think of the president?” it will just decline, which means that the kind of risk area is somewhat smaller. For models, it is infinite. And so there's not a single expert out there who can competently evaluate the risks of cyberattacks and fifteen-year-olds having month-long conversations with a chatbot and seeing whether it will in fact recommend suicide or something horrendous like that, and can evaluate the risks that terrorists can use AI to produce bioweapons. The risk surface is just too big. And so the central challenge actually becomes how do you get those subject matter experts to work within a one coherent framework that outputs one coherent report and rating that the world can go and inspect? ‘Cause that global perspective is central, but there's not a single organization today that could produce that.Swyx [00:35:47]: And you would be the presumptive one when you put out your model standards.Rune Kvist [00:35:51]: We think there can be one company that can, with a consortium of experts, build one coherent standard. I think we've shown that across all of the enterprise risks today. We think it could be one company that could, with a consortium, specify the audit rules, basically like the inputs and outputs that all these technical experts need. What access do they need? How should they treat infosec- info security? They can look at whether the eval- evals are well-produced without necessarily being able to say, “Hey, is this a threat or not a threat?” But overall, evaluating whether the evals are good, well-constructed, that set of audit rules that basically becomes the interface for all these experts, we think one clearinghouse could put together. To be clear. When I say one company, I think of it as one company coordinating lots of this in the same way that when we saw our consortium, it's not like we say we have all the answers on agent security. What we say is we are taking on the role of eliciting all of the concerns and being the secretary that puts it together and runs a tight house such that the standard updates lockstep every quarter, and that the audit reports that come out, in this case, 100-page audit reports, uniform and crisp and clear all to the level of detail that is required for executives that need to make a clear go/go decision. So that's kind of the role that we think we might play.OWASP, Frameworks, and the Operational Audit LayerSwyx [00:37:11]: I think in many ways you're performing the role that OWASP used to do there, and you said, like, you know, competition and partners.Rune Kvist [00:37:18]: Yeah.Swyx [00:37:19]: Can you go more into, like, how they partner?Rune Kvist [00:37:20]: Yeah. So first of all, OWASP is basically an open source community of security practitioners that are coming together to build frameworks for addressing the latest security concerns. We think they are phenomenal at creating frameworks. We'- In fact, we'- First of all, we're partners with them, so we have a joint article. Two, we've learned a lot from them. We think they're a tremendous source of intelligence. What OWASP does not do is building the machine that runs third-party audits such that a company like Cursor or a company like JPMorgan could get a third party to go and review them against this and say, “Hey, you've passed the standard, and here is the report that you can use to build trust and preempt your partners' or customers' questions.” So they fundamentally try to do something different. You - They are part of the information gathering and intelligence gathering and creating clarity, but the operational layer of turning this into promises is not the business they try to be in.Swyx [00:38:14]: The standard is emerging and is doing very well. Was it necessary to then also do underwriting? Obviously it's in the name, so please remember you thought about it first. I feel like if you just have enough consensus, you don't actually need the money angle, but it does help.Vibhu [00:38:30]: I did want to also note, you guys are a profit company too, right? It's not profit where there's a whole business side to it as well?Why For-Profit Standards and Insurers MatterRune Kvist [00:38:39]: Yeah. Yeah, so I'm just getting crazySwyx [00:38:41]: I think about the money part.Rune Kvist [00:38:42]: Yeah. Yeah, let's get into the money part. Let's start from actually your question, profit versus profit. In the security space today, cybersecurity, most of the standards are produced by nonprofits. I think that's an issue.Rune Kvist [00:39:00]: The question you have to ask yourself is, how do you create good incentives for these standards to be good and keep up?Rune Kvist [00:39:09]: Nonprofits tend to not have these adverse profit incentives where they, hollow out their standard and create a race to the bottom, but they're also not at all responsive by default to the communities that they serve. There's no process-- They don't have customers that they serve where they go and ask, “What do you want? What do you want? What do you want?” And when you look at the overall satisfaction with the security standards today, people tend to just not like them very much. You do see in other domains, that profit standards can serve the world quite well. So there are examples, like we talked about Moody's before. It's not without flaws, but, it is absolutely critical societal infrastructure that gets run at an astounding scale today. Your credit score, it's FICO. It's also a profit business. And when you go back even further in history, some of the crash testing standards came out of insurance companies.Rune Kvist [00:40:06]: The insurance companies together founded the Insurance Institute for Highway Safety because they were very interested in, like, how can we use standards to drive down mortality and save money? Go back, prior-- Our name actually pays homage to the Underwriters Laboratories, UL, which, was started right around when electricity came out. Houses started burning down. Insurers, again, were paying the bill, and they were maybe also good people, but their profit incentive was, let's prevent houses from burning down. Let's test all the electrical products, the light bulbs. All the light bulbs in here are probably tested, the toasters, et cetera. And they set up, an entity to create those standards. Today, UL has a profit entity and a profit entity. What they've recognized, they spun - They started profit. They spun out a profit because what they recognized was like, hey, actually to serve customers well, you need a profit entity. The lesson here is one of the ways that the market can align incentives so you're both responsive to customersRune Kvist [00:41:07]: And not hollowing out your standard over time is to align it with insurers because they fundamentally have good incentives. And so if you're a profit standard that works closely with insurers, you get the feedback loop in such that you're really tuned into your customers, but also have their interest at heart. So that's the model that we - the kind of inspirational model that we've learned a lot from, and that's also where the name comes from. In some ways, the term underwriting can both be associated with insurance, but it's also a broad term for, like, making decisions.Rune Kvist [00:41:40]: If you underwrite a decision, you're fundamentally kind of taking ownership for the consequences of it.AI Insurance Contracts, Lloyd's of London, and ElevenLabsSwyx [00:41:45]: Yeah, I mean, what does an insurance contract look like for AI?Rune Kvist [00:41:49]: Yeah. Most of the demand comes today for insurance contracts is, sitting between people who've built AI and people who are buying AI.Swyx [00:41:56]: Yes.Rune Kvist [00:41:57]: And what you want—the reason why people want insurers involved, both for the traditional reasons, hey, if something goes wrong, we want to be compensated, but it's in particular because insurers can bring trust to the equation. Because insurers will pay for the damages, if they're willing to write an insurance policy, that is them saying, “Hey, we think there is risk here, but that is manageable.” And that is kind of a. Their incentive aligns with the enterprises adopting it, so that's a really a good signal to the market. In the same way, actually, one of the things that Waymo tried to get their first permit to even operate in San Francisco was to get a lot of insurers to stack up a huge insurance policy. In the case if something went wrong, not because Google can't pay, but because it was very valuable to have a third party go and look at that dataRune Kvist [00:42:47]: That are trusted by governments, trusted by enterprises as conservative people and say, “Hey, we've looked at it. We're actually willing to take some of this on our balance sheet.” So that's, that's kind of the reason why people are interested in it. What it looks like is, in some ways like every other insurance contract. You specify what are the perils you want to cover, how much do you want to cover them, like up to what limits, and what does it cost to cover that. And in the case of, if we take a really concrete example, ElevenLabs, bought a first of its kind AI agent insurance policy. They work with some of the biggest, enterprises that work with governments. They're really interested in going above and beyond and making promises to their customers. So they wrote a policy that covers just some of the core concerns that their customers have been asking about. And, the crucial thing was really to get Lloyd's of London, the world's oldest insurer, one of our partners, to look at this data and be that third party alongside us to say, “Hey, we think there's something here that's worth underwriting.” and that's actually what it looks like. And so they will show that contract to their customers, and they can see how much they're covered for. They can see what exactly it covers, and that will also probably change next year. They will want to write an insurance policy that might cover more.Swyx [00:44:04]: When you say Lloyd's, is it reinsurance, or are they sharing somehow at the same level orRune Kvist [00:44:11]: Yeah. So typically, the way, new companies get into insurance is that they partner with insurers such that the insurers take the majority or all of the financial risks. Fundamentally, if insurance is useful, because it brings trust, you have to be able to pay the bill. Lloyd's of London is 400 years old. They've never not paid a claim. They're extremely trusted. What Lloyd's of London struggle to do on their own is to figure out which of the risks are real, what should we be looking for, what are the kinds of technical controls, and running the tests. So they use AIUC-1 as kind of the underwriting framework, and we produce a bunch of eval results that then directly feed in to inform the pricing. So this means that ElevenLabs customers know that payment will be there. They don't have to look to our series A and see, like, do we think they have enough cash on the balance sheet? They will look at Lloyd's.Swyx [00:45:05]: Yeah.Rune Kvist [00:45:05]: Yeah.Swyx [00:45:05]: And Lloyd's, like, famously very creative. I think I remember some headline like, they insured Jennifer Lopez's, butt or something.Rune Kvist [00:45:13]: Correct.Swyx [00:45:13]: Right?Rune Kvist [00:45:13]: And I think, was it, David Beckham's right foot?Swyx [00:45:16]: So, yeah. Right?Rune Kvist [00:45:17]: And stuff like this.Swyx [00:45:18]: So, like, clearly not a large data set.Rune Kvist [00:45:22]: Exactly. It's actually a remarkable institution that's both kind of has some of the truly school virtues of having been around for a long time. They, like, really. They really operate like a trusted entity, and they have appetite to figure out the future. And I think there's a lot of recognition that both there is, like, tremendous amount of risk in AI that is poorly understood today, so getting into this business carries real risks. But also this is where lots of the risk exposure will happen in the future. This is the one market where risk is truly growing. This is the one market that will also take out some of the existing markets. Take, like, auto insurance. When there are no human drivers, how's that market going to look? Well, it's clearly going to change. How are you going to assessSwyx [00:46:08]: You want to insure Waymo?Rune Kvist [00:46:10]: I. All I'll say is the principles for how you insure Waymo are very similar to how you insure other kinds of AI.Swyx [00:46:15]: Right.Rune Kvist [00:46:15]: So again, crash testing, that's what we do for customer share at Lovable. That will also need to happen for Waymo, which is not how you do it for human drivers. So there's this growing awareness that the world is changing very fast, and the only way to learn how to underwrite AI is to write some policies. You may incur some losses and think of that as R&D expense, really. But the question for them is, like, who are the trustedtechnical partners they can get into this business with that can help them navigate and make sure they don't make, kind of foolish mistakes? But also who is willing to hear the wisdom that they have? They've done this before. They've seen it was. They were there when cyber came out. So there are lots of ways in which AI feels completely new, but there's also lots of ways in which risks look the same. And so there's actually a tremendous amount of wisdom sitting in some folks that may have gray hair, but really have, like, a keen sense of, how to quantify risk.Swyx [00:47:08]: Yeah. And the number is. So it's basically like I want fifty million dollars worth of coverage against these perils, and Lloyd's will give you a quote on it, and then you have, like, a small markup or something, and then you turn it around and do that? Is that as simple as it is?Risk Capital, Premiums, and Working with InsurersRune Kvist [00:47:23]: You basically share some of that premium.Swyx [00:47:25]: Yeah.Rune Kvist [00:47:25]: X percent goes to the people who do the pricing of it.Swyx [00:47:28]: You're. It's kind of like a. It's kind of like a merchant bank for insurance type of thing.Rune Kvist [00:47:33]: Exactly. You basically split the fee, and you can think of the insurance supply chain as, like, there's bringing the capital, there is doing the pricing, and there is doing the distribution. And typically, you will pay out some X percent of premium here, Y percent of premium here, and the rest of it will go here.Swyx [00:47:46]: Does all the insurance world work like this, or is there some point at which, like. So if right now you have equity capitalRune Kvist [00:47:51]: Yeah.Swyx [00:47:52]: At some point, maybe you start raising, debt or whatever, and then you have enough of a bank account and enough history, let's say you've been in operation for ten yearsRune Kvist [00:48:00]: Correct.Swyx [00:48:00]: That you don't need Lloyd's anymore?Rune Kvist [00:48:02]: That's totally an option. And I could see some worlds where that makes sense, specifically if there are risks that we feel high confidence that we'd want to insure where the incumbent insurers are too slow to find appetiteSwyx [00:48:13]: Okay.Rune Kvist [00:48:13]: Or simply struggle to evaluate it such that they don't want to do it. But by and large, in general, you do not want to compete with insurers on, bringing risk capital to the game for two reasons. One is that's fundamentally a cost of capital game. They have extremely low cost of capital. Startups have high cost of capital, by and large. And two, you want to hedge your bets, and it's very helpful then to also have a portfolio of home insurance, of car insurance. And we're not about to become a car insurer nor a home insurer.Rune Kvist [00:48:43]: So they have some natural advantages, which makes it much more likely that we'll partner.Swyx [00:48:48]: Yeah.Rune Kvist [00:48:48]: And they bring that, the capital at scale, and we bring the technical expertise.Swyx [00:48:51]: You're, you're going to work with them for a long time.Vibhu [00:48:52]: How are the discussions with the insurers as well? So basically, they're going off of your certification, right? They're trusting the diligence on you that your certification is valid, you tested the right things, and they're backing the money that, you know, you have the right testing in place. So any interesting takeaways from working with insurers?Rune Kvist [00:49:12]: I think the maybe the first thing is they feed into the standard as well. So if there are things that they feel like they need that they're not seeing, we are also taking that as input into the standard, because fundamentally we think a good standard is one that creates a really healthy promise ecosystem, and we think insurers are a critical part of that. And again, they are the most well-incentivized to. They see all the lost data across every. Any particular CISO knows their particular concerns. Insurers see the concerns across the entire portfolio and often have direct access to, like, what exactly happened, who was at fault, et cetera, as they do part of their forensics. So they're actually, like, a great source of intelligence on this. One of the big takeaways from cyber insurance, which is a market that didn't work that well, was that the insurance and the technical expertise was not married up. What our conviction is that standards have to precede insurance. Fundamentally, what everyone first and foremost want, whether you're a CISO at JPMorgan or a CISO at Cursor or an underwriter at Lloyd's of London syndicate, is you want to not have an incidentRune Kvist [00:50:19]: In the first place. You want to know that the risk is well-managed, and only then does insurance start to make sense. So we'll see the standard ecosystem basically run ahead of the insurance. And the reason why we. You asked us kind of why I also do insurance, this is kind of proving what we think a whole promise confidence infrastructure ecosystem needs to look like, and we think it's very compelling to bring that to life, even if we think the standard is kind of the core linchpin that unlocks the rest.Claims, Liability, Air Canada, and Duty of CareSwyx [00:50:44]: There's been no claims yet, right?Rune Kvist [00:50:45]: Nope.Swyx [00:50:46]: This is one of those things where, you know, if people haven't really worked through what it means to cover things.Rune Kvist [00:50:52]: Yeah.Swyx [00:50:52]: So for example, I pay Cursor $20 a month.Rune Kvist [00:50:55]: Yep.Swyx [00:50:56]: And I write a vibe code something that makes, a plane crash, causing $200 million worth of damage.Rune Kvist [00:51:02]: Yes.Swyx [00:51:02]:

Send us Fan MailIn this episode of the B2B Go-To-Market Leaders Podcast, Vijay Damojipurapu sits down with Guy Galon, Chief Customer Success Officer at Obrela, to explore the role customer success plays across the entire go-to-market motion—and why making value visible is especially critical when customers can't always see the outcomes they're paying for.Drawing from more than 25 years across software engineering, professional services, program management, enterprise technology, cybersecurity startups, and customer success, Guy shares how his career shaped his approach to customer relationships, retention, and cross-functional GTM leadership.For Guy, go-to-market is a system that makes customer value visible at every stage—from sales and onboarding through adoption, retention, and expansion.They dive into:Why customer success can act as an orchestrator across sales, marketing, product, partners, and the customer.How selling cybersecurity managed services changes GTM when the best outcome is often one the customer never actually sees.Why strong retention comes from combining trusted relationships with a clear understanding of the outcomes customers expect.How customer success and sales can share responsibility for expansion while keeping retention firmly centered on customer success.Why CS teams should identify, qualify, and track expansion opportunities instead of leaving commercial conversations entirely to sales.How case studies, customer referrals, events, and partner enablement connect customer success directly to growth.Why continuous feedback loops between CS, sales, and product help companies understand both pre-sale expectations and post-sale realities.How Guy approaches high-touch relationships with strategic enterprise accounts while using signals and customer health data across smaller accounts.A GTM success story where early collaboration between partners, sales, finance, and CS enabled a complex enterprise customer to onboard in just six weeks.Why vendors need to clearly communicate customer responsibilities during onboarding instead of assuming buyers understand what implementation requires.How an earlier program failure taught Guy to escalate risks, challenge assumptions, and encourage his teams to speak up before problems become crises.Why curiosity, resilience, and deliberately stepping outside your comfort zone are essential for long-term GTM leadership.Guy's core perspective is simple: customer success isn't just about keeping customers—it's about understanding what success means to them, making that value visible, and aligning the organization around delivering it.Connect with Vijay Damojipurapu on LinkedInConnect with Guy GalonBrought to you by: stratyve.com

What happens when you're winning every deal—but your team is quietly reaching its breaking point? For Zach Sikora, CRO at Endor Labs, one of the biggest leadership lessons of his career came when he realized that driving results wasn't enough. His team was succeeding, but they were also fatigued and he risked creating a culture built around compliance rather than commitment. In this episode of Hunters and Unicorns, Simon Kouttis and Ollie Kuehne sit down with Zack to explore his journey from EMC and AppDynamics to People.ai and now Endor Labs, and the leadership lessons he learned along the way. Zack shares the moment his COO warned him that he might have a “mutiny” on his hands, why accountability has to come with empathy, and how explaining the “why,” using data, humanizing leadership, and giving people a voice transformed the way his team operated. The conversation also explores building a modern GTM organization, the role of RevOps and enablement, AI and application security, Endor Labs' growth journey, and why developing leaders is just as important as hiring more sales reps. In this episode: • The leadership mistake that nearly cost Zack his team's commitment • Why winning doesn't mean your leadership approach is working • The difference between compliance and commitment • How to balance accountability with empathy • Why leaders need to explain the “why” behind every decision • How data can help bring sales teams along with the strategy • Why RevOps and enablement should come before scaling headcount • Building a culture focused on leadership development • How AI is changing application security and software development • Endor Labs' growth strategy and 100% channel approach • What it really means to build a high-performing GTM organization About Zack Sikora Zack Sikora is Chief Revenue Officer at Endor Labs, with leadership experience at EMC, AppDynamics, People.ai, and Endor Labs. His experience spans high-growth technology, enterprise sales, go-to-market strategy, and revenue leadership, with a strong focus on building teams and developing leaders. About Our Sponsor: Big thanks to Aurasell for sponsoring this episode. Aurasell is an AI-native GTM platform designed to intelligently automate every aspect of the sales cycle from prospecting and deal management to forecasting and coaching. Subscribe to Hunters and Unicorns for conversations with the leaders building, scaling, and transforming high-growth technology companies. #HuntersAndUnicorns #ZachSkora #EndorLabs #SalesLeadership #GTM #RevenueLeadership #SalesManagement #SalesStrategy #AI #Cybersecurity #B2BSales #leadership Timestamps: 0:00 — Trailer 1:28 — Welcome & Introduction 2:43 — When It Started to Click as a Leader 4:18 — Accountability With Empathy — The Turning Point 5:48 — How He Actually Implemented Change 7:47 — From Compliance to Commitment 8:31 — Leaning on Mentors Through the Hard Moments 9:09 — The App Dynamics Story — A Baptism of Fire 12:34 — What Made Him Choose App Dynamics Over MongoDB & Medallia 14:52 — Why Dolly Said He Wasn't Ready 16:05 — The Rise to Third Line — Zigs, Zags & Getting Passed Over 18:11 — How to Systematically Build a Team From Scratch 20:21 — The Power of Activity Data & Leading Indicators 22:55 — When Did He Know He Wanted to Be a CRO? 25:08 — Was He Always Chasing the Title? 28:50 — Playbook First, Domain Second 32:18 — How Important Is Founder Support for Your Strategy? 33:39 — The Critical Criteria When Assessing a CRO Opportunity 35:24 — How to Assess a Founder Before You're In 38:03 — What Drew Him to Endor Labs 40:07 — The Four Problems Endor Is Built to Solve 43:49 — How Endor Stands Up Against the Big Players 46:10 — What's Stopping a Foundational Model From Doing This? 48:04 — Where Endor Is on the Journey & What's Next 49:44 — Why Endor Is a Great Place for Sales Leaders Right Now 51:18 — Closing

AI didn't kill the sales fundamentals. It just made everyone forget what they actually are.In this episode, John sits down with Kristie Jones, Author and Founder of Sales Acceleration Group, to talk about why the buyer now wants to talk to a human 80 to 90 percent of the way through their own research, why the post-mortem meeting needs to be replaced with a pre-call meeting, and why AI is forcing sales leaders to teach objective judgment for the first time in their careers.If you are in sales, sales leadership, RevOps, or GTM, this conversation gives you a practical way to think about writing a role brief for the AI tools on your team, replacing the post-mortem with a pre-call meeting, testing for objective judgment instead of just tactical skill, and why the old playbook is dead even though playbooks aren't.Want to build a sales team that can think critically in the AI era? Visit www.jbarrows.com and learn how you can Make It Happen.What You'll LearnWhy the buyer now wants to talk to a human 80 to 90 percent of the way through their own research, and what that means for how a rep opens the callWhy the post-mortem meeting needs to be replaced with a pre-call meeting built around objective judgmentWhy every AI tool on your team needs a role brief, the same way every hire needs a job descriptionWhy Kristie tests candidates by making them argue with an AI generated plan live, not just use oneWhy the four skills sales leaders need now are systems thinking, workflow design, decision leadership, and accountability clarityKristie Jones is the Author and Founder of Sales Acceleration Group, where she works with sales organizations on hiring, training, and leadership development. She's the author of Selling Your Way In and the upcoming The Sales Leadership Gap, releasing October 2026, which looks at how AI is forcing a fundamental rethink of how sales leaders hire, coach, and make decisions.Visit kristiekjones.com to learn more about Kristie's work and get updates on The Sales Leadership Gap.Connect with Kristie Jones: LinkedIn: linkedin.com/in/kristiekjones Website: kristiekjones.com Instagram: instagram.com/kristiejones.sales YouTube: youtube.com/@kristiekjonesJohn Barrows is a sales trainer, speaker, and founder of JB Sales with over 25 years of experience in the industry. He has made hundreds of cold calls a week, led startups to acquisition, and trained high-performing teams at companies like Salesforce, LinkedIn, Amazon, and Okta. Through JB Sales, John focuses on practical sales execution, helping reps fill pipeline, close deals, and build trust with buyers in today's AI-driven sales environment.Connect with John Barrows: LinkedIn: linkedin.com/in/johnbarrows Instagram: instagram.com/johnmbarrows TikTok: tiktok.com/@johnmbarrowsCheck out John's Membership: https://learn.jbarrows.com/pages/individual-packages Join John's Newsletter: https://www.jbarrows.com/newsletter

If you've ever wondered why splitting your brand budget from your demand gen budget always seems to end with brand getting cut first, this episode is for you. I sat down with Drew Neisser, founder of CMO Huddles, a peer community built exclusively for B2B CMOs with over 800 members, to dig into what's actually changed for CMOs in the AI era, and what hasn't.We talk about why AI is turning competent content into a commodity, and why that actually makes human judgment, humor, and original thinking more valuable, not less. Drew makes the case for the return of the big idea, and explains why treating brand and demand gen as separate budgets sets marketing and sales up to work against each other.If you're worried about CMO tenure or wondering whether the job is even still worth pursuing, Drew shares brand new data from a study he ran with Findem on how long CMOs actually last at public, venture backed, and private equity backed companies, and why the gap is bigger than you'd think.We also get into what a CMO should do in their first 90 days at a new company, why peer communities like CMO Huddles matter more than ever, and why knowing your customer better than anyone else in the room is still the strongest superpower a CMO has.Key Topics Covered"(00:00)" - Introduction"(00:04)" - Why CMO Huddles is built"(00:06)" - What's changed and what hasn't for CMOs in the AI era"(00:09)" - Why the pressure on marketers keeps rising"(00:13)" - Why splitting brand and demand gen backfires"(00:19)" - The rise of unified GTM teams and who should lead them"(00:21)" - Is the CMO role disappearing?"(00:23)" - The buyer journey in healthtech"(00:25)" - Agent sprawl: a new kind of MarTech debt"(00:28)" - Inside Reputracker"(00:31)" - New CMO tenure data from the Findem study"(00:36)" - The Quick Wins Playbook for a CMO's first 90 days"(00:39)" - Why marketers should join a peer community"(00:42)" - How CMO Huddles supports CMOs in transition"(00:44)" - Why AI fluency is now a make or break interview question"(00:45)" - Talking to customers: the real CMO superpower"(00:48)" - Adam's five key takeaways from the conversationIf you are interested in discussing this or any other topic, let's have a chat.  Reach out to me directly to schedule a no-obligation discussion. This isn't a sales call, but rather an opportunity to talk through your questions and challenges.Follow me on LinkedIn.Subscribe to The Healthtech Marketing Show on Spotify or watch us on YouTube for more insights into marketing, AI, ABM, buyer journeys, and beyond!Find all of our episodes on the Health Podcast Library.Thank you to our presenting sponsor, HealthcareNOW, 24/7 expert shows, interviews, and podcasts, powering healthcare leaders with innovation, policy, and strategy insights.

Jen Igartua, CEO at Go Nimbly, joins Sam Jacobs, AJ Bruno, and Asad Zaman. Her client roster is wild: Fireworks AI, Perplexity, Exa, and Wispr Flow, plus some of the biggest names in SaaS. Topics in today's episode include how the fastest-growing AI-native companies are growing in spite of their revenue operations (not because of them), the first project she runs when she wants to show ROI fast, and how she makes the case that RevOps is a support function and always was. Plus, whether the GTM engineer is anything more than RevOps with better marketing (and more)! Key Takeaways: - Operational debt at the fastest-growing AI companies tracks demand, not management quality. Go Nimbly's second customer was Zendesk in 2016, where the head of IT told her "oh, we've grown in spite of our operations." Jen shares what it takes to make a difference at the breakneck pace these environments demand. - RevOps spent a decade insisting it was strategic, and Jen thinks that cost the function credibility with the sellers it exists to serve. As she says in the episode: "I think that operators have been doing an injustice to sellers for a long time. And we are starting to really take onus of the fact that revenue operations needs to be a support function to the sales team. And we've been fighting it forever. We are not a support function. And I'm like, we actually are." Asad, who has argued the same line for years, put the failure mode in football terms: "if a goalkeeper starts trying to play like a striker, you don't let in a lot of goals, right?" - In a people business, staffing is the product, and Jen only learned that after COVID took Go Nimbly down to 20 people. The rule she rebuilt around is "I should only take projects of skills that I want to nurture," then staff someone who is 80% of the way there so the last 20% is stretch. "Our whole job is playing Tetris with resources." She is equally direct about what did not work: "I poured a bunch of money in being tech enabled... I burned millions of dollars trying to do it," because at her clients' scale "everything's custom." The payoff line is the one to keep: "if you nail staffing, you grow a really great services organization." Connect with the Hosts & Guests: Host: Sam Jacobs, CEO at Pavilion - https://www.linkedin.com/in/samfjacobs/ Host: AJ Bruno, CEO at QuotaPath - https://www.linkedin.com/in/ajbruno3/ Host: Asad Zaman, CEO at STA - https://www.linkedin.com/in/azaman1/ Guest: Jen Igartua, CEO at Go Nimbly - https://www.linkedin.com/in/jen-igartua/ Topline is more than a podcast: 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 00:00 Introducing Jen Igartua 02:15 Growing In Spite Of Their Operations 05:46 Top Of Funnel Is The First Fix 06:39 What AI-Native Ops Teams Actually Have 10:50 The Twenty-Rep Qualification Bar 11:37 Is Sales More Broken Than Before 15:00 RevOps Is A Support Function 16:29 Do Great Products Make Great Sellers 22:51 Caesars, COVID, And Twenty People 24:56 Lessons From A Services Business 28:21 Staffing Is The Whole Game 31:10 Why Contractors Do Not Work 40:09 What Is A GTM Engineer Really 49:26 What Scares Jen About 2027 54:40 Bulls and Bears

Sienna Quirk has worked on GTM motions and product marketing for at least 30 Salesforce apps, probably many more. On this episode of How We Got There, we speak with the Head of Marketing at Thread about all things Salesforce ecosystem for ISVs. Sienna's underinvested recommendation for ISVs is not getting more mileage out of product releases - it can be a strong demand gen engine. “A solid go-to-market strategy for your release, whether that be your quarterly release, whether that be a new product release, it can be a super big opportunity to re-engage with current customers, to reach out to prospects and showcase your product roadmap and show them your innovation.” It can also help answer that question of “build, buy, or partner” in a vibe-code/Claudeforce world where anything is easy to build that looks pretty good, but isn't enterprise ready.We talk about event strategy across having a booth by leveraging it as an opportunity to play around with your pitch/better together story as well as if you don't have a booth and ideas you can bring to Dreamforce next week.On a personal note, I am proud to have worked with Sienna at 3 different companies now and consider her a friend. She taught me the concept of product marketing and its importance is consistently overlooked by CROs, sales leaders, and partnership leaders. Her deliverable of creating the Messaging Exercise helped at least 20 ISVs refine their positioning in the Salesforce ecosystem, I think it was the single most impactful deliverable we completed for clients.This episode is brought to you by ⁠ISV Accelerators⁠. ISV Accelerators is your inside guide to co-selling with Salesforce: activating the right reps, accelerating real pipeline, uncovering new revenue, and closing more deals together. Senior alliances leadership without the cost of a full-time hire, and speed to answers on whether that hire is even worth making. See what your Salesforce partnership could really do.⁠⁠#salesforce⁠⁠ ⁠⁠#isv⁠⁠ ⁠⁠#gtm⁠⁠ ⁠⁠#salesforcepartners⁠⁠ #appexchange 

Matt Allison, CEO and Founder of Handraise, joins us to share why he's skipping the SMB trap and building an AI-powered media intelligence platform for enterprise from day one. Drawing on hard-won lessons from scaling TrendKite, Matt breaks down how Handraise is targeting $30K+ deals with brands like Walgreens and Hershey, the importance of patience in early go-to-market, and how AI is helping them build a more thoughtful, scalable, and enterprise-ready product from the start. Matt also talks about: - Why Handraise is skipping the SMB trap and going enterprise from day one - What TrendKite taught him about building for high-value, high-retention customers - Why patience and discipline matter when you're building an enterprise GTM motion - How AI is helping Handraise build a more sophisticated product with a lean team - Why being AI-first changes what's possible in product development - How to build a sustainable business without chasing hypergrowth at all costs Chapters: 00:00 Introduction to Matt Allison and Handraise 01:15 Why Handraise Is Going Enterprise From Day One 08:46 The Challenge: Building for $30K+ Enterprise Deals 11:56 Why Patience Matters in Enterprise GTM 15:17 How AI Is Changing Product Development 19:02 Building an AI-First, Enterprise-Ready Product 22:43 Why Handraise Isn't Chasing Hypergrowth 26:00 Matt's Favorite Book and Podcast 28:00 Where to Find Matt and Handraise Try Handraise: handraise.com

AI is increasingly moving beyond tools that simply help people work faster.The next step could be AI-powered digital workers capable of taking responsibility for entire business processes.In this episode of ThinkData, Alex Hutchings is joined by Dries De Coster, Founder & CEO of meet DWIGHT, a company building AI-powered digital workers to automate middle and back-office processes.After around 20 years in the HCM industry, including leadership roles at SAP and The Access Group, Dries launched meet DWIGHT just over three years ago.The conversation explores why he believed the timing was right for a new approach to automation, how the company identified genuine product-market fit, and what it takes to build and position an AI company in one of the noisiest technology markets we have seen.In this episodeWhy Dries left The Access Group to build meet DWIGHTThe problem meet DWIGHT is trying to solve with AI-powered digital workersWhy the timing was right for a new generation of enterprise automationHow early-stage AI companies identify genuine product-market fitHow to position an AI company when virtually everyone is talking about AIThe biggest GTM lessons from scaling meet DWIGHTHiring lessons from building an early-stage AI companyWhat Dries looks for when hiring into a startupWhat success looks like for meet DWIGHT over the next 12–24 monthsHow the digital workforce could evolve as AI technology matures

CRO Confidential: 0 to $600M in Under 4 Years. The ElevenLabs GTM Playbook with Carles Reina (Partner @ Baobab Ventures and Former VP of Revenue at ElevenLabs). Hosted by Sam Blond, CEO and Co-Founder of Monaco Carles Reina was the fourth employee and first GTM hire at ElevenLabs. Four years later, the company hit $600M ARR and an $11B valuation. In this episode of CRO Confidential, Carles breaks down exactly how they built it. He covers the distribution-first strategy that drove 0 to $100M in 20 months, the grants program that pulled demand away from every competitor in the market, and the 20X quota model that had reps hitting 300-600% attainment. He also gets into what he'd do differently — why sales enablement and senior sellers should come earlier than most founders think — and how ElevenLabs wired AI into their GTM motion before most companies were even asking the question. If you're building go-to-market from scratch or trying to figure out what "AI-native" revenue looks like in practice, this one is required listening. This episode of the SaaStr podcast is brought to you by Monaco:  If you're using AI in your go-to-market or you wanna get going, then you need to try Monaco. We're on it, SaaStr's on it, we love it. We use it for outbound, but you can use it for everything, actually. It's everything you need all in one place. At Monaco, it's the all-in-one revenue platform and your system of record. It builds your TAM, runs outbound, captures every interaction, and manages pipeline in one place, all in one great unified platform. Go to monaco.com to learn more. 

What happens when one of the most intense, competitive leaders in software sales finally decides to slow down? For Alex Patman, former CRO at Drata, stepping away from the game wasn't about losing his edge—it was about gaining perspective. In this episode of Hunters and Unicorns, Simon Kouttis and Ollie Kuehne sit down with Alex to unpack his journey from Fuze and Okta to becoming CRO at Drata, the mindset that drove his rapid rise through the software industry, and the leadership principles behind his success. Alex opens up about the tragic loss of his brother, why he made the decision to take time away from work, and how stepping back has changed his perspective on family, health, leadership, and his next professional mission. The conversation also explores the rapidly changing AI era, the pressures of modern sales leadership, and what it really takes to build and lead high-performing go-to-market teams. From increasing intensity and narrowing the focus to raising standards and leading from the front, Alex shares the playbook, principles, and competitive mindset that have shaped his career. In this episode: • Alex's journey from Fuze and Okta to CRO at Drata • Why pressure is a privilege—and the mindset of a “90-day contract” • How tragedy and time away from work changed his perspective • The importance of family, health, and mental resilience • Why curiosity and competitiveness can accelerate a career • Alex's perspective on the AI era and the future of software sales • The leadership principles behind high-performing GTM organizations • Why leaders need to increase intensity, narrow the focus, and raise standards • The importance of trust, communication, humility, and collective responsibility • How taking a step back can become the slingshot for the next chapter About Alex Patman Alex Patman is a former CRO at Drata and an experienced go-to-market leader who has built his career across high-growth technology companies including Fuze, Okta, and Drata. Known for his intensity, curiosity, competitiveness, and pace-setting leadership style, Alex has become one of the most prominent emerging leaders in software sales. Subscribe to Hunters and Unicorns for conversations with the leaders building, scaling, and transforming high-growth technology companies. About Our Sponsor: Big thanks to Aurasell for sponsoring this episode. Aurasell is an AI-native GTM platform designed to intelligently automate every aspect of the sales cycle—from prospecting and deal management to forecasting and coaching. #HuntersAndUnicorns #AlexPatman #Drata #SalesLeadership #GTM #SoftwareSales #AI #SalesStrategy #RevenueLeadership #B2BSales #ArtificialIntelligence #sales Timestamps: 0:00 — Trailer 1:17 — Welcome & Introduction 2:50 — Why Alex Is Taking Time Off 6:03 — Filling the Void & Keeping the Mind Active 10:47 — Why It's Important to Sit Through This 15:30 — Evaluating the Hardware Space 19:09 — Separating Signal From Hype in AI 21:19 — What He Looks for in a Founder 26:33 — How to Assess Founder GTM Commitment 30:04 — Career Arc — Fuse to Octa to Drata 37:33 — What Set Him Apart at Octa 41:37 — When Adam Arens Took Notice 44:17 — Managing Impatience & Over-Ambition 51:05 — Transitioning Into the CRO Seat 53:35 — Is It a Lonely Seat? 56:48 — Work-Life Balance as CRO 1:01:34 — What He Loves Most About the Game 1:02:54 — Guiding Principles & Leadership Style 1:05:41 — Closing

Deploy AI agents for marketing - https://www.graphed.com/GTM engineering is one of the fastest-growing roles in tech right now, but what does it actually involve, and why are companies scrambling to hire for it? In this episode, I sit down with Benyamin Holley, a GTM engineer who's worked at multiple startups including AirOps, to break down what this job really looks like day to day.We dig into why API completeness is becoming the new buying criteria for software (and why Salesforce is quietly winning the AI era), why you should skip tools like Zapier and N8n entirely and go straight to code, and how "just-in-time" internal tools are changing the way teams build workflows. Ben also walks through real systems he's built and explains why expertise, not just tooling, is what actually separates winners in an increasingly saturated market.We also get into the debate around cold email as a marketing channel vs. a sales channel, why attribution is still an unsolved problem, and what companies should actually look for when hiring a GTM engineer.In this episode, we cover:00:00 — What a GTM engineer actually is (and why every company is hiring one)02:54 — Why Salesforce beats HubSpot now: API completeness is the buying criteria05:29 — Skip Zapier and n8n entirely, go straight to code08:31 — Your GitHub repo is your GTM portfolio32:29 — The job-change bot: vibe-coding UserGems and what made it hard

Most cyber companies bought the Claude or ChatGPT seats, handed them to every rep, and are now asking "now what?" Tal Peretz, co-founder and CEO of Onfire AI, has watched that play out across hundreds of mid-market and enterprise security vendors, and his answer is to stop pushing from the top. Start with your best rep and let the rest of the team chase him. Then take the same idea outside: skip the CISO, find the director who owns the budget, and make him the one who carries you up.In this episodeThe three tiers of AI adoption Tal sees in cyber sales teams, and why 85 to 90 percent are stuck at "we gave it to the rep"Why he starts every rollout with the A-players even though they're rarely the best AI usersThe on-site workshop where one rep's transformed week pulled his whole region along"Five by five": the agent that picks a rep's five accounts and five prospects for the week, and why each rep tweaks itWhy trust from the front line, not the tooling, makes or breaks an AI transformationOnly about 2 percent of account entries go through the CISO, so at $50M to $100M ARR the target is the directorWhy "agentic SOC" vendors pitching the CISO are talking to the wrong personHow to reach out on a signal without sounding like a creeper (and why you never mention Reddit)Tal's 2030 take: reps flip from 80 percent admin to 80 percent selling, and quotas go with itAbout the guestTal Peretz is co-founder and CEO of Onfire AI, an AI-driven GTM platform for companies selling to technical buyers. Before Onfire he was CTO at OwnID and spent nearly seven years in Israel's Unit 8200, the last two as Head of R&D.Notable quotes"Sales is a competition. Once you put the best reps first with the AI, everyone wants to fall in line.""Probably your best reps are not the best with AI yet.""If you have fifty million of ARR and you need to grow to 100 million, the CISO is not your target.""Everyone goes to the CISO. So you go to that person, and no one is connecting with him."Chapter timestamps00:04 Why an 8200 alum built for salespeople, not cyber01:55 Spin the wheel: visionary or smoking crack02:51 Three tiers of AI adoption in cyber GTM05:24 Start with your best rep, not your CRO08:15 The five by five use case11:09 Scoring your whole TAM off five years of closed-won16:39 The three barriers to agentic GTM (and the one that matters)21:16 Quality beats volume: the A/B test with people26:46 Only 2 percent get in through the CISO30:59 The SOC example: hundreds of vendors, one buyer32:40 Where the real signals live: Reddit, Discord, GitHub38:11 How to reach out on a signal without being a creeper41:34 What Onfire is building next43:41 Visionary or smoking crack: the reveal Support the showThe Cyber Go-To-Market Talk is the show for cybersecurity sales leaders, founders, CROs, and go-to-market operators looking to improve cyber sales performance and build more predictable revenue growth. Hosted by Andrew Monaghan, founder of Unstoppable.do, covering cyber sales leadership, revenue leadership, sales onboarding, forecasting, pipeline generation, and cybersecurity go-to-market execution.Follow me on LinkedIn for regular posts about growing your cybersecurity startupWant to grow your revenue faster? Check out my cybersecurity sales consulting and trainingNeed ideas about how to grow your pipeline? Sign up for my newsletter.

Send us Fan MailGuests: Sangram Vajre, Co-Founder & CEO of GTM Partners, and Amos Bar-Joseph, Co-Founder & CEO of Swan AI  --  AI is changing what's possible with account-based marketing, but meaningful personalization still starts with strategy.In this special episode of SaaS Backwards, we revisit our fireside chat with Sangram Vajre, co-founder and CEO of GTM Partners, and Amos Bar-Joseph, co-founder and CEO of Swan AI, to explore how ABM is evolving in the age of AI.ABM has always been built around focusing deeply on the accounts that matter most. What's changing now is the speed and scale at which SaaS companies can identify signals, personalize outreach, create relevant content, and test new approaches.We discuss why AI should be used as an enhancement to human strategy rather than a replacement for it, how faster experimentation can improve go-to-market execution, and why the strongest ABM programs may look beyond net-new logos to expansion, retention, and pipeline velocity.You'll also hear how teams can use unique buying signals to engage prospects earlier, create more relevant account experiences, and combine high-tech execution with high-touch engagement.Key takeaways:Why strong ABM execution still starts with strategyHow AI can dramatically shorten campaign and experimentation cyclesWhy relevance matters more than surface-level personalizationHow unique signals can help SaaS teams reach buyers earlierWhy expansion and existing customers deserve a bigger role in ABMHow AI makes highly personalized, high-touch experiences easier to scale---Stalled pipeline? Lost deals? Diagnose your GTM gaps with a free, actionable checkup. 

AI could be bigger than electricity.Anastasios Angelopoulos is seeing that shift firsthand, with Arena ranking 500+ models and tracking roughly 10 new releases every week.On Grit, he explains why there may never be one dominant AI model and why the API layer may be less sticky than people think.Guest: Anastasios Angelopoulos, Co-Founder and CEO of ArenaConnect with Anastasios AngelopoulosXLinkedIn Connect with MamoonXLinkedInConnect with Joubin:XLinkedInEmail: grit@kleinerperkins.comFollow Grit: LinkedInX​Learn more about Kleiner Perkins

AI is not replacing customer success. It's turning it into the most important revenue function in the business.In this episode, John sits down with Elizabeth Salmoun, Head of Customer Success at Otter.ai, to talk about why products and tooling have become table stakes in the AI era, why the sales to CS handoff decides whether a client renews inside the first 90 days, and why AI is turning CSMs into AI advisors and workflow consultants instead of relationship managers.If you are in sales, customer success, RevOps, or GTM leadership, this conversation gives you a practical way to think about handoff timing between AE and CSM, segmenting a book of business by tier instead of treating every renewal the same, the three W's framework for discovery, and why AI generated work should never go out the door without a human reviewing it first.Want to build a customer success team that turns renewals into your biggest growth lever? Visit www.jbarrows.com and learn how you can Make It Happen.What You'll LearnWhy products and tooling became table stakes, and why the relationship is what actually differentiates you nowWhy the first 90 days after a deal closes decide whether a client renews or churnsWhy Elizabeth starts every renewal conversation 120 days out instead of waiting for the discount fightWhy AI is turning CSMs into AI advisors and workflow consultants instead of relationship managersWhy a sales rep who's just running the process is already irrelevant, and what still makes the role criticalElizabeth Salmoun is the Head of Customer Success at Otter.ai, where she leads retention and expansion strategy in one of the most competitive corners of the AI tooling market. She built her career in retail before moving into tech sales and account management, and she has spent the last several years turning customer success into a revenue generating function built on discovery, relationship building, and clear rules of engagement with sales.Visit otter.ai to learn more about Otter.ai's AI meeting assistant and how its customer success team helps teams capture, act on, and get real value from every conversation.Connect with Elizabeth Salmoun: LinkedIn: linkedin.com/in/esalmounConnect with Otter.ai: Website: otter.ai LinkedIn: linkedin.com/company/otter-ai YouTube: youtube.com/@Otterai Instagram: instagram.com/otter.aiJohn Barrows is a sales trainer, speaker, and founder of JB Sales with over 25 years of experience in the industry. He has made hundreds of cold calls a week, led startups to acquisition, and trained high-performing teams at companies like Salesforce, LinkedIn, Amazon, and Okta. Through JB Sales, John focuses on practical sales execution, helping reps fill pipeline, close deals, and build trust with buyers in today's AI-driven sales environment.Connect with John Barrows: LinkedIn: linkedin.com/in/johnbarrows Instagram: instagram.com/johnmbarrows TikTok: tiktok.com/@johnmbarrowsCheck out John's Membership: https://learn.jbarrows.com/pages/individual-packages Join John's Newsletter: https://www.jbarrows.com/newsletter

Evan Huck, CEO and co-founder of UserEvidence, opens up about the hard decision to shift from a tried-and-true outbound sales playbook to a long-term brand-building strategy. After eight straight quarters of flat pipeline growth, Evan had to trust the process, convince his board to stay the course, and rethink how UserEvidence reached its ideal customers. In this conversation, he shares what it takes to stay patient when results don't come fast, how to balance short-term pipeline needs with brand investments, and why the payoff was worth the wait. Evan also shares: - Why eight quarters of flat pipeline tested their commitment to brand over quick wins - How UserEvidence narrowed its ICP to unlock stronger growth - Why creative, one-to-one outreach can still break through with enterprise buyers - How Slack communities helped build credibility and generate early enterprise demand - Why product marketing came before demand generation in building the GTM engine - How customer advocacy became an increasingly powerful source of growth Chapters: 00:00 Introduction to Evan Huck and UserEvidence 04:31 From SDR to CEO: Evan's GTM Journey 07:54 Why UserEvidence Bet on Brand 09:31 Eight Quarters of Crickets: Waiting for Brand to Pay Off 12:30 Narrowing the ICP to Unlock Growth 13:51 The CEO Hustle Behind Enterprise Growth 16:00 Building Credibility Through Communities 18:00 Why the Right Team Matters 19:14 Why Product Marketing Came First 20:39 Turning Customers Into a Growth Engine 22:07 The "Chill, Humble" Leadership Philosophy 22:43 Evan's Favorite Book for Founders Try UserEvidence: userevidence.com

A strong B2B sales strategy is not about chasing more leads or more partners. For Ben Cronsberry, it is about knowing which relationships are worth investing in, building them patiently, and knowing when to walk away. What happens when you stop treating partnerships as a quick source of referrals and start treating them as a long-term sales ecosystem? And how do you decide which relationships actually deserve your time? In this Best Of episode, originally published in July 2025, Harry Kendlbacher revisits his conversation with Ben Cronsberry, former GTM Operations Lead at Shopify and now Head of GTM & Partnerships at Parsel. Ben shares a different perspective on modern B2B selling, from his “sniper approach” to partner selection to his belief that discovery starts before you ever pick up the phone. They explore why warm referrals can lead to stronger conversion rates and faster sales cycles, why partner enablement can easily go wrong, and why great sales storytelling starts with listening. The conversation also gets into a question many sales teams struggle with: when should you keep pursuing an opportunity, and when is it smarter to “lose fast”? ⏱️ Timestamps 00:00:00 Why warm referrals matter in B2B sales 00:03:08 The “sniper approach” to partner selection 00:06:14 Why partner enablement can easily go wrong 00:10:24 Why sales storytelling starts with listening 00:14:27 Why partner led sales requires patience 00:17:51 Why discovery starts before the sales call What you'll hear in this conversation • Why Ben believes a partner-led sales approach can outperform traditional cold outreach • How to identify and prioritize the partners worth investing in instead of trying to work with everyone • Why sales storytelling starts with listening and understanding the buyer before telling your story • How better sales discovery can help reps decide which opportunities to pursue and which ones to “lose fast” About Ben Cronsberry Ben Cronsberry is Head of GTM & Partnerships at Parsel and former GTM Operations Lead at Shopify. With a background spanning software, sales, partnerships, and go to market operations, Ben brings a practical perspective on partner led selling, sales discovery, enablement, and building productive commercial relationships.

What do you do after you've founded a company, sold it to Databricks, and could coast? Amandeep Khurana went back to being hands-on. In this episode of The Product Podcast, Carlos Gonzalez de Villaumbrosia (CEO at Product School) talks with Amandeep Khurana, now on Anthropic's go-to-market team, about a career built on deliberately choosing the harder path.Amandeep traces the whole arc: training as an engineer, moving to the Valley to go customer-facing at Cloudera, catching the company-building bug "by osmosis," and founding Okera in 2016, which eventually exited to Databricks. He talks about what he learned in the ups and downs of running a startup, why he later joined AWS as a founding PM (on Kiro) to run four zero-to-one initiatives instead of taking a comfortable management track, and how he thinks about the two fundamental jobs in any company: building a product and selling a product. He and Carlos dig into the tension between being a hands-on builder and being a manager, how to decide which domains are worth jumping into when you know nothing, and why he joined Anthropic to work on bringing AI safely to enterprises. It's a candid conversation about career design, first-principles decision-making, and staying close to the work.What you'll learn:- Why Amandeep repeatedly chooses the "harder path," and how first-principles thinking drives his career decisions- What founding and exiting Okera (to Databricks) actually taught him about company building- Why he went back to hands-on building instead of a pure management track- How to think about the two core jobs in any company: building the product and selling it- How to decide whether to jump into a domain you know nothing about- What running four zero-to-one initiatives inside a big company teaches you- Why he moved into GTM and enterprise AI adoption at Anthropic- How to stay hands-on as your career pulls you toward managementConnect with Amandeep Khurana:Anthropic (GTM / Enterprise); previously co-founder and CEO of Okera, founding PM for Kiro at AWSLinkedIn: https://www.linkedin.com/in/amansk/Host: Carlos Gonzalez de Villaumbrosia, CEO at Product SchoolLinkedIn: https://www.linkedin.com/in/villaumbrosia/About the Product Podcast: Product School's podcast brings you candid conversations with the founders and product leaders shaping tech.Social Links:Find out more about Product School hereFollow our Podcast on TikTok hereFollow Product School on LinkedIn here

"No data, no agent. Bad data, bad agent. Good data, good agent." Cris Mendes thinks data readiness is the number one reason the agentic enterprise fails, and he has the receipts — he ran the experiment on his own team before he sold it to anyone else.He's VP of Revenue at Momentum.io, acquired by Salesforce in large part because of the go-to-market motion he built there. Before that: Drift, Airtable, and LogMeIn. Before all of it, U.S. Navy search and rescue swimmer. I've known him almost twenty years and introduced him to LogMeIn myself, so take the enthusiasm here with whatever salt you need.His argument is that everyone is buying agents on top of a data layer that was never built. The average customer conversation moves about eight thousand words; maybe eight of them make it into the CRM. Companies don't have a first-party data layer, they have a human-input data layer — and that's what every agentic project quietly runs aground on.The story worth the listen: Momentum had a brutal drop-off after the first call. Rather than sampling a few calls and guessing at messaging or ICP, Cris pushed 800 first calls through deep research and got one answer back — the reps weren't prescribing a next step. So he had Momentum listen to the last ten minutes of every call and fire a clip into Slack whenever an AE either didn't prescribe or tried and missed. With humor, so it didn't read as surveillance. Prescription went through the roof. As he puts it: a team respects what you inspect, but you can't inspect everything — unless the inspecting is automated.WHAT WE COVER- Selling shovels in a gold rush, and why structured data was the bet- Why "not everything called an agent is an agent" — deciding vs. automating- The last ten minutes of a sales call as sacred ground, and how to enforce that at scale- Bringing AI into QBRs: "it got so real so fast" — no more recency bias and self-preservation dressed up as a forecast- 10x growth six quarters running, zero attrition, 95% of the team over quota — and why he credits hiring over product- Whether AI cuts headcount: expands capacity, thins middle management, and the ~10% of companies that are actually AI-forward are hiring hardest- Auto-advancing pipeline stages on deal milestones instead of trusting a rep's guess — "otherwise you're just buying something like Clari to miss your forecast very expensively"- His IDEC hiring model, and why he'd trade relevant experience for intrinsics every timeCONNECT WITH CRISLinkedIn: Cris Mendes — Cris with no H, Mendes with an SMomentum.io---The GOATS of Growth has two Founding Sponsor slots open — category exclusive, $1,250/mo, in front of GTM leaders and founders who don't answer their phones. Includes personal introductions where guests opt in. jay@thegoatsofgrowth.aiSubscribe, rate, and review — and send this to whoever on your team is being asked to "roll out agents" this quarter.CHAPTERS00:00 Twenty years, and a Navy rescue swimmer03:39 Selling shovels in a gold rush04:13 "No data, no agent"05:33 Is it deciding, or just automation?07:07 Running Momentum on Momentum08:33 800 first calls, one missing skill10:27 You respect what you inspect14:46 Does AI actually cut headcount?20:41 Killing hype in the QBR22:05 Zero attrition, 95% over quota25:57 8,000 words spoken, 8 reach the CRM31:43 Auto-advancing deal stages on milestones35:44 Orchestrate, or get orchestrated out38:39 IDEC: how Cris hires40:53 How to reach Cris

In Episode 197 of Open Tech Talks, host Kashif Manzoor speaks with Francis Brero, VP of AI Strategy at HD Insights, about what it really means for an enterprise to become AI native. Francis shares his experience helping an established enterprise organization move into the AI era and explains why AI transformation is much more than adding an LLM or purchasing the latest AI tools. The conversation begins with a practical framework for identifying where AI can create value. Francis explains how organizations can break down jobs into smaller tasks, assess each task's value, and identify which activities are suitable for AI automation. The discussion then moves into one of the biggest challenges facing enterprises today. Many organizations have an AI mandate, but they are adopting AI simply because they feel they need to "do AI." Francis explains why organizations need to focus on the problems they are trying to solve instead of chasing every new model and tool. A major theme of the conversation is institutional knowledge. Enterprises often depend heavily on experienced employees who know how things work. Francis explains how AI can turn that knowledge into an organizational asset rather than letting it remain locked inside individual employees. The episode also explores AI-native software development, multi-agent workflows, AI-powered code reviews, system optimization, security controls, and the role of different AI models in the software development lifecycle. Francis also explains why traditional machine learning remains important. Not every problem needs an LLM, especially when organizations are working with structured data or systems that require deterministic outcomes. The conversation concludes with a discussion about managing the incredible speed of AI innovation. Instead of changing systems every time a new model appears, organizations should build flexible systems that let them introduce new models and capabilities without rebuilding everything. Episode # 197 Today's Guest: Francis Brero, VP of AI Strategy at HD Insights Francis Brero is a longtime AI and GTM operator who has spent the past 15 years building data-driven SaaS products that help revenue teams make smarter decisions. Website: HDInsights LinkedIn: Francis Brero What Listeners Will Learn: How to Build an AI Native Enterprise How to Identify the Right AI Use Cases How to Break Jobs Into AI-Ready Tasks How to Measure the Value of AI Automation How to Turn Institutional Knowledge Into an Organizational Asset How to Build AI Native Development Workflows How AI Agents Can Improve Code Reviews How to Use Multiple AI Models Effectively When Traditional Machine Learning Is Better Than LLMs How to Build AI Systems That Can Adapt to New Models How to Balance AI Innovation With Enterprise Stability Why System Thinking Matters in AI Transformation Resources: Website: HDInsights LinkedIn: Francis Brero

Lindsay Rios is a Fractional CRO & owner of GTM Done Right, helping B2B companies turn accidental growth into intentional growth. Many of the businesses she works with have built revenue growth through referrals, word of mouth and organic inbound. But eventually those channels stop producing the same results, growth becomes less predictable, and leadership realizes they need a more intentional go-to-market strategy. This is where Lindsay comes in to identify the real causes of stalled growth, whether it's a misaligned ICP, a disconnected revenue strategy or what she famously calls a "poopy pipeline." Lindsay also partners alongside Mandy McEwen, where they help business with their revenue growth by integrating social selling into their GTM strategy. Links https://www.lindsayrios.com https://yourfuturebiz.com https://nextwave.golumi.io If you're enjoying Entrepreneur's Enigma, please give me a review on the podcast directory of your choice. The show is on all of them and these reviews really help others find the show. iTunes: https://gmwd.us/itunes Podchaser: https://gmwd.us/podchaser TrueFans: https://gmwd.us/truefans Also, if you're getting value from the show and want to buy me a coffee, go to the show notes to get the link to get me a coffee to keep me awake, while I work on bringing you more great episodes to your ears. →  https://ko-fi.com/entrepreneursenigma Support me on TrueFans.fm → https://gmwd.us/truefans. Support The Show & Get Merch: https://shop.entrepreneursenigma.com Want to learn from a 15 year veteran? Check out the Podcast Mastery Community:https://www.skool.com/podcasting Follow Seth Online: Instagram: https://instagram.com/s3th.me LinkedIn: https://www.linkedin.com/in/sethmgoldstein/ Seth On Mastodon: https://indieweb.social/@phillycodehound The Marketing Junto Newsletter: https://MarketingJunto.com Learn more about your ad choices. Visit megaphone.fm/adchoices

What kind of founder walks away from a multi-billion-dollar fintech company to start building rockets?Baiju Bhatt, co-founder of Robinhood, joins Joubin Mirzadegan to talk about life after one of the most consequential companies in modern finance, and why he's now betting on Cowboy Space Corporation, an orbital infrastructure company building AI data centers that ride to space inside its own rockets."I still have that dog in me," he says.Baiju opens up about building alone for the first time in 15 years and what he learned about product-market fit while scaling Robinhood through its most chaotic years.Connect with Baiju:XLinkedInConnect with Joubin:XLinkedIn​Email: grit@kleinerperkins.comFollow Grit on LinkedInX​Learn more about Kleiner Perkins

The PMM role is under real pressure right now.VPs of Marketing and CMOs are being asked to do more with less: fewer headcount approvals, leaner teams, tighter budgets. And PMM is often the first place that gets squeezed.Instead of hiring a full-time PMM, companies ask the existing team to absorb the work. Or they bring in a fractional. Or they just... don't do it, and wonder later why GTM feels off.At the same time, the scope of what PMMs are expected to own has exploded.Positioning, messaging, sales enablement, competitive intelligence, content strategy, analyst relations, product launches...and more.And now there's AI on top of it, which hasn't lowered the pressure to produce more, faster.So here's my take: we're at a real inflection point for this role. And in this episode, I explain where I believe we're headed.Here's the LinkedIn post where I talked about it first.Jump in:02:26 - Marketing teams are under pressure to do more with less03:38 - The PMM scope has expanded beyond what it was 5 years ago04:09 - Introducing the "superPMM"04:39 - Using ahaPMM to speed up customer research synthesis05:22 - Building messaging infrastructure with MojoPMM06:07 - Faster creative and landing page production using Claude, Figma, and Vector06:31 - Why AI still needs human judgment07:32 - Execution is democratized, but judgment is not08:17 - Why this topic is getting so much engagementSubscribe to Building With Buyers on Apple or Spotify or wherever you like to listen, and don't forget to leave a review if you're lovin' the show.Anna on LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠linkedin.com/in/annafurmanov⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Website (getting a facelift, stay tuned): ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠furmanovmarketing.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠One Insight⁠⁠

What if AI could produce a better board memo than any CFO — and you could trust every data point in it? In episode #385, Ben Murray breaks down how he is using AI to generate complete 5-page financial board reports, and more importantly, why the outputs are reliable. This isn't AI hype — it's a working FP&A process Ben is running today inside his fractional CFO practice. If you're still manually assembling dashboards, PDFs, and narrative summaries before every board meeting, this episode reframes what's actually possible right now. Why a deterministic metrics engine — not AI — is the non-negotiable foundation that makes AI-written board reports trustworthy (and how Ben built one backed by 35 pages of documentation) The exact structure of a SaaS financial board report: executive overview, metrics vs. benchmarks, ARR growth, customer retention, GTM efficiency, financial capacity, data anomalies, and top priorities — all generated by AI How Ben identified 84 unique data points in a single AI-written board memo using ChatGPT — and why that number changes how you think about QA The MCP connection to SoftwareMetrics.ai that makes this a repeatable monthly process — and how Replit is now building it as a native app feature What's coming in October: Ben's SaaS Metric Sprint, a live walkthrough of building the data foundation, calculating metrics, and connecting MCP so you can run this for your own company Tune in to see how Ben is turning month-end close into a board-ready narrative in minutes — and how you can build the same process for your SaaS company. Resources Mentioned Ben's app: https://softwaremetrics.ai/ SaaS Metric Sprint (October 6th): https://www.thesaasacademy.com/saas-metrics-implementation-sprint-sept-2026

Building AI tools that replace one-size-fits-all personality tests with real career plans is what got Corey Kossack into K12 education. He's the founder and CEO of Orchard, an AI-powered career counseling platform for schools, and Aspireship, an employee upskilling platform — work born from watching his own father cycle in and out of a job he hated for years. Kossack has spent his career helping people who want to help themselves climb toward ambitious goals, and Orchard is the result: an AI career counselor named Orchie, paired with real interviews from working professionals across hundreds of occupations. For Ruckus Makers wrestling with overworked counselors and an outdated career-readiness playbook, that's the credential that matters. Most schools still run career readiness on a conveyor belt: next grade, next test, college, then hope for the best. Corey Kossack argues that model was built for a workforce AI has already started dismantling — and he's built a system for what comes after it. This is the episode for any Ruckus Maker rethinking what career readiness actually means right now.

For about a decade, a customer data platform's job was to be the place your customer data lived. What happens to that job when the most valuable thing a system can do goes beyond remembering and evolves to deciding and acting?Agility, in a category that's been declared dead more than once, isn't chasing whatever the market renames itself this quarter. It's being able to change what your product does without losing the thing your company is actually good at.Today we're going to talk about what it takes to evolve a category from the inside. Specifically:- Why fifteen years of data infrastructure turned out to be an advantage rather than baggage to shed.- What actually changes — in the product and in the company — when a system stops solely storing customer data and starts acting on it.- Where the line sits between a marketer steering an agent and a system running on its own.This conversation also kicks off a four-part series we're running this week with the product team at Treasure AI: four leaders, four vantage points: the vision, the roadmap, the operational reality, and the design.To help me start it off, I'd like to welcome Rafa Flores, Chief Product and Growth Officer at Treasure AI.About Rafael FloresAs a proud immigrant from Honduras, Rafael's journey is rooted in three core values passed down from his family: the power of education, treating everyone with dignity—no matter their background—and lifting up the next generation. Once he was an wide-eyed kid with big dreams, and today, he's living them. He's dedicated his career to scaling SaaS companies from startup spark to high-growth success. At Meltwater, he played a key role in driving product innovation and market readiness that led to a successful IPO. At Datanyze, he led strategic initiatives that culminated in an acquisition by ZoomInfo—boosting their data intelligence footprint. At ARM, he spearheaded innovation in Retail SDK and IoT, opening new frontiers in connected experiences. And at 6sense, he led all automation, data, and AI-powered products, building inclusive teams and solutions that empower GTM leaders to sell smarter. At Treasure Data, he helped orchestrate a landmark $600M acquisition by ARM and secured record-breaking funding for the Customer Data Platform. Now, he's back—leading Treasure Data into a new era of intelligence and automation built for scale. It's more than a comeback; it's a homecoming. This is a team he deeply believes in—brilliant, driven, and purposeful. Beyond tech, he's a devoted father of three and married to a registered nurse whose compassion and strength inspires him every day. My expertise lies in product strategy, CDPs, data privacy, and scaling GTM solutions—from agile startups to Global 2000 enterprises. He's passionate about solving hard problems with technology, driving sustainable growth, and mentoring rising product leaders. As a member of the Forbes Technology Council, he aims to share insights, spark dialogue, and help shape the future of innovation—one conversation, and one leader, at a time.Rafael Flores on LinkedIn---------- Resources ---------- Treasure AIReach your customers with Reddit. Spend $500 in ad spend, get $500 back in ad creditCheck out the Treasure AI Studio sandbox for yourself.Enjoyed the show? Tell us more at and give us a rating so others can find the show.Connect with Greg on LinkedInDon't miss a thing: get the latest episodes, sign up for our newsletter and more.Check out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology.The Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. Hosted on Acast. See acast.com/privacy for more information.

Don’t miss this massive channel shift! Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ In this episode of the Ultimate Partner Podcast, host Vince Menzione sits down with Alexandra Zagury, Corporate Vice President of Channels at Microsoft, to explore ecosystem shifts, partner-led growth, and AI transformation. https://youtu.be/9jrSGX5bM90 Key Takeaways Microsoft’s telemetry and propensity data offer unprecedented insights that many partners are currently failing to unlock. Partners must evolve past basic licensing models and build comprehensive managed service stacks across the entire customer lifecycle. Establishing an AI Center of Excellence on the Microsoft platform is critical for capturing future market share and technical intensity. The modern tech ecosystem demands a shift from product-led growth to true partner-led growth driven by multi-partner collaboration. Renewal engines targeting 110% to 125% retention require an always-on motion starting well in advance of contract expiration. Investing in sales readiness and precision velocity training ensures that end sellers can effectively articulate the value of the Microsoft platform. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags Microsoft, telemetry, Hyperscalers, channel, small medium enterprise, telcos, hosters, SSPs, Cisco, CSP, agentic GTM, propensity data, SPX, PUPP, cloud descent, MSPs, managed XDR, Agent 365, skilling, co-sell, renewals, flywheel, AI Center of Excellence, enterprise Transcript Alexandra Zagury Audio Podcast [00:00:00] Alexandra Zagury: One of the things that I think is the best kept secret at Microsoft is the telemetry that we offer our partners. [00:00:08] Vince Menzione: You can feel it happening. The ecosystem is shifting beneath us, the way Hyperscalers are partnering, how AI is remaking the channel and what it means to win in 2026. Welcome to the Ultimate Partner Podcast. [00:00:22] Vince Menzione: I’m Vince Menzi, own your host, and each week I sit down with leaders at the intersection of technology. Partnerships and outcomes. The voices shaping how ecosystems actually work. We talk about what’s real, what’s changing, and what it takes to lead in this era where the partner channel isn’t just part of the strategy. [00:00:41] Vince Menzione: It is the strategy because being in the room changes everything. Let’s [00:00:46] Guest: start. [00:00:50] Vince Menzione: I am thrilled because I get to have a leader that is somewhat new in role and is the corporate vice president of channels for Microsoft. And so Alex is here to, is joining us for the first time at Ultimate Partner. I’m thrilled to have you. Thank, thank you so much. Thank you so much. Welcome, welcome. [00:01:14] Vince Menzione: Thank you. Thank you. You and I are gonna be in the center seat in the middle. Okay. Yeah. Yeah. We wanna. So I am thrilled. Um, you’re relatively new in your role at Microsoft. [00:01:24] Alexandra Zagury: Seven months. [00:01:25] Vince Menzione: Seven months, [00:01:26] Alexandra Zagury: yes. [00:01:26] Vince Menzione: Wow. That’s a, that’s a crazy. So take us through, ’cause for those who don’t know you, but a little bit of an introduction, your title, your role, CVP, and uh, your remit. [00:01:37] Vince Menzione: Let’s talk about that and what your organization is focused in, in your mission. Yeah. [00:01:41] Alexandra Zagury: So hi everybody. Really exciting to be here. Thanks for the invitation, Vince. I always love to be with partners and with the channel. So my, I came to Microsoft to lead a new role, which we call Global Channel Sales, and it was part of the strategy of Microsoft to really bet on the growth in small, medium enterprise. [00:02:04] Alexandra Zagury: With the channel partner led growth. So my remit is really to lead our managed partners, but also to lead the strategy when it comes to the channel. Very specifically looking at the telcos, the distributors, the hosters. The sis and, uh, the SSPs of course. And, and so my role can be summarized in one word growth, and that’s what we do every single day is map our ambition to your ambition and figure out how we actually conquer this age of ai. [00:02:40] Vince Menzione: It’s a pretty big role. It’s a pretty, you know, I forgot about hosts ’cause we don’t, we don’t talk about them as much these days in the cloud. [00:02:48] Alexandra Zagury: Still a lot [00:02:48] Vince Menzione: of opportunity. You’ve got, and you’ve got all the telcos as well, which are pretty significant organizations, right? Lumen Field, just around the corner. [00:02:56] Vince Menzione: Mm-hmm. Right up the. Right up, I said across the river, but it’s across the pond in Seattle. And, uh, what does that look like from an organization perspective in terms of dollars? Are you allowed to disclose numbers? [00:03:08] Alexandra Zagury: No, we [00:03:08] Vince Menzione: don’t talk numbers. Okay. And, but you have a long career in this type of environment, in this role. [00:03:15] Vince Menzione: 10 years at Cisco, right? [00:03:17] Alexandra Zagury: Mm-hmm. [00:03:17] Vince Menzione: Tell us a little bit more about your background, [00:03:18] Alexandra Zagury: please. Yeah, sure. Um, well I started out as a, a sales leader. Uh, actually I started out in banking, if you wanna go all the way back. Um, but I fell in love with the channel actually, when I was at Yahoo, believe it or not. Wow. [00:03:32] Alexandra Zagury: Because it was the first interact sales model that I had the privilege to operate in, and I really understood. Stood the power of going through a channel. And then I was at Blackberry where I, I, I also, um, well had various sales leadership roles, but our model was all through, was all through the channel. [00:03:51] Alexandra Zagury: Yes. Um, some startups and then help [00:03:54] Vince Menzione: in those days too, right? [00:03:55] Alexandra Zagury: Yeah. It was all sps and I learned from Jim Balze the channel fundamentals. So, um, and then ended up, of course, at Cisco the last. Nearly 11 years, which I think is also one of the greatest channel companies. Yes. And really has thrived in a partner, partner led growth, and in a partner led model. [00:04:12] Alexandra Zagury: And so when, when Microsoft called, I mean, this was just the opportunity of a lifetime. To lead the channel during an era where we’re all getting disrupted, we’re all having to blueprint new systems, new ways of working, where go to market is getting identified, and we’re all having to figure out how to become customer zero ourselves, but then also how to go to market. [00:04:36] Alexandra Zagury: With, with agents. And so this was the, the most exciting time that I could think of to join the Microsoft ecosystem and really take us to the next level. [00:04:44] Vince Menzione: Well, your background is perfect for this. I, I, Rodney Clark is a friend, has been a guest on the podcast and in, in the studio, and I think of Cisco is the quintessential channel company. [00:04:56] Vince Menzione: Like when I think about how channel got created and how it worked well, it was always Cisco that did it. So give us like your perspective now, like seven plus months and like how does this feel like you’ve You’ve got a big remit and, uh, various, uh, routes to market. We’ll call them, uh, channels to market. [00:05:14] Vince Menzione: So take us through a little bit of that, like Yeah, sure. [00:05:16] Alexandra Zagury: Describe [00:05:16] Vince Menzione: the transition, what it’s been like [00:05:18] Alexandra Zagury: for you. So one of the things we’re focused on is supporting the channel through the transformation, and we look at it in a couple of lenses. The first lens is really helping the channel become customer zero. [00:05:29] Alexandra Zagury: We really believe that the partners that invest in. Actually identifying their own processes and their own go go to market are the channels, are are the partners that it can actually win because if you are using it, you’re gonna be able to sell it. The second layer is all about technical intensity, and I’m very passionate about this because I see it from two lenses. [00:05:51] Alexandra Zagury: One is. I would like each and every of our partners to lead in building an AI Center of Excellence based on the Microsoft platform. It is a unique opportunity. I can give you lots of numbers, right? We’ve all heard about the trillions of agents that are gonna be here by 2030. We’ve all heard about the tam. [00:06:12] Alexandra Zagury: I mean, our TAM is going from 777. Million to over a billion, uh, to over a trillion. I, it’s, the numbers are just enormous, insane, right? Over half a billion of customers in our base that are using, that are, that are based already on the Microsoft platform. So all the goodness of our IU IQ platform can be unlocked with all the services that the partners can build. [00:06:36] Alexandra Zagury: So investing in that technical skilling and building those practices are gonna be, is gonna be essential. The third part. The third pillar is all about ag agentic, GTM. So once you’re actually using, uh, the Microsoft platform, you’re gonna have to reimagine all your business’s pro processes, your sales processes, and the more that you are integrated into how we do, how we do things. [00:07:00] Alexandra Zagury: One of the things that I think is the best kept secret at Microsoft is the telemetry that we offer our partners. Yeah. I mean, it’s unbelievable. I’ve never seen the quality of propensity data. And now I’m gonna give you the ABCs, which is please, A SPX, which is where we get all our copilot data, PUPP, which is our proposal upsell, uh, planner, right, cloud descent, where you can actually get your next action directly to your sellers. [00:07:27] Alexandra Zagury: There is just so much goodness that we give, which is part of our, our investment in partners. Which takes me to the fourth pillar, and that is all about value alignment and one of the things that I’m very focused and I bring with me from, from Cisco and the work that I did with MSPs is really thinking through what is that value exchange between us and the partner. [00:07:49] Alexandra Zagury: I believe that I’m in the business of earning your trust. Earning your preference. And, and, and we do that by really mapping that value alignment. So not just, one of the things that the whole industry has copied from Microsoft is really looking at our incentives across the customer lifecycle. Yes. So really mapping the value alignment across the customer lifecycle, not just at the point of the deal. [00:08:13] Alexandra Zagury: ’cause that was the whole purpose of CSP. That’s right. The investments we make in tele telemetry, the investments we make in our go-to-market assets and having those bi-directional feedback loops so that we can be continuously improving. So those are sort of the things that I’m thinking about every day. [00:08:29] Alexandra Zagury: There’s a couple of others as we lead and support you in this transformation. ’cause I think of my job as to supporting and driving growth with you so that we have that joint ambition. But also supporting the transformation that both of us are on this journey. [00:08:44] Vince Menzione: And Microsoft was the first with Jay McBain was with us yesterday, and we talked, we’ve talked about this before, but you were the first company to take and look, get rid of the old metal systems, right? [00:08:55] Vince Menzione: The bronze, silver, gold, mm-hmm. And move to basically a point system for partners so that they can come at it from a kind of a global perspective on how they drive success. What was, um, what did you learn about this partner community, your first months that you didn’t expect? [00:09:12] Alexandra Zagury: Can I say something controversial? [00:09:14] Vince Menzione: Absolutely. Okay. [00:09:14] Alexandra Zagury: I love, [00:09:14] Vince Menzione: we love controversial, [00:09:15] Alexandra Zagury: so I think one of the things that I was surprised was that I didn’t see all the partners really unlocking the value of CSP. Yeah. What do I mean by that? When Microsoft moved to the Point System, and I was on the other side, really, I was so jealous of CSP when I was running managed services. [00:09:34] Alexandra Zagury: Here’s an offer that is for partners, for Partner that gives you that initial. Guarantee in terms of the margin that helps you throughout the customer life cycle with all the incentives and, and programs that we have. And I didn’t see, I mean there are some partners, but I was surprised not seeing more partners really building their value stack and their services across the lifecycle. [00:09:59] Alexandra Zagury: And I think there’s such a great opportunity now to do that. That was one of the most surprising things I thought. Oh my gosh, there must be so many, so many services stack, so many people really unlocking, unlocking that, that value. That was a, that was a little bit surprising. [00:10:14] Vince Menzione: Why? Why do you suppose, why do you suppose that was happening? [00:10:17] Alexandra Zagury: I think some of the things that we were listening here, there’s some, sometimes complexity. There are things that we still have to. Get better on, and I’m one of the first ones to say that like our partner experience, we have, um, you know, a lot of focus right now on partner center and ensuring that we’re identifying it. [00:10:36] Alexandra Zagury: I don’t know if I can say it, but, you know, one of the things that we’re looking at is replicating internally. We have Agent J. That supports our sellers through the sales process. Nice. We’re looking at having something similar for our partners. Very cool. Getting some claps there. So, yeah, so, uh, I think, you know, that I, some, there are some lockers that we, we have to acknowledge a lot of them are operational and comes with being a 50-year-old company. [00:11:02] Vince Menzione: I, I’ll give you my perspective too. I wanna get your thoughts on this. ’cause I got to, I’ve gotten to know this MSP community, which is a, you mentioned managed services and that, um, I think that some of them, well, I think Microsoft is leaning in, in a much bigger way. Um, we had Jose on stage yesterday and just the, the energy around the room, there he is, he’s back here. [00:11:23] Vince Menzione: The energy in the room around the MSP community is palpable and it maybe it wasn’t there a few years ago. Maybe some people got off the bus, so to speak, like they weren’t really paying attention mm-hmm. To all the change and all the investments. That you men, you’re mentioning or being made to support this, would you, what would you say about that? [00:11:42] Alexandra Zagury: Yeah, I’d say that that that is correct, but I’d also say that what I learned, you know, leading MSP at Cisco was that. All the stars have to be aligned, right? And if one thing is not right, if you don’t have product market fit, if you, if you, if you don’t have a good way to, uh, consolidate your offer, if you don’t have that investment in practice development, like there’s a series of things that we need to get right. [00:12:07] Alexandra Zagury: And I think Jose and I spent a lot of time thinking through those things and we’re, we’re ready to, to welcome the community and specifically around security. I mean, that was the second thing that I was really surprised because I lost so many deals on the other side to Microsoft, and I was like, and then I come on this side and I’m like, there’s all this opportunity everywhere. [00:12:28] Alexandra Zagury: I look underneath this chair, this opportunity, and I’m like, why are people not going after it? There is the opportunity to build managed XDR solutions, the opportunity to reinvent the song. There’s, there’s just so much opportunity and I think people get so stuck in the. Just thinking of it from a licensing model and not thinking it from the, the full on end-to-end value that you can then unlock through the best licensing model on the planet. [00:12:55] Alexandra Zagury: And so, look, we’re here, we’re, we’re ready to talk to all of you and, and really figure this out ’cause we are gonna place really bet big bets next year on ensuring that we’re growing with the MSP community. [00:13:08] Vince Menzione: So what are you personally focused on in changing Microsoft to drive this. [00:13:13] Alexandra Zagury: Well, uh, I don’t know. [00:13:14] Alexandra Zagury: Changing is a, is a, is a big word. Well, I like evolution, change, [00:13:17] Vince Menzione: evolution, evolving. [00:13:18] Alexandra Zagury: You know, I [00:13:19] Vince Menzione: transition. [00:13:21] Alexandra Zagury: I think there is, there is a couple of things that I’ll say that one of the things that I, I’m really focused on right now, the first one is skilling. We’re at a time of such transformation. That investing in skilling, and if you look at our skilling model, it has four pillars. [00:13:37] Alexandra Zagury: The first pillar we’re best in class, in which is certification specializations, and making sure that our ecosystem is certified to go to market. The second one, which we call project ready. It’s sort of how we help you, uh, technically skill the folks that sit in your practices. And there’s more work to do there, and there’s more that you can learn about how we can actually help you. [00:13:59] Alexandra Zagury: And then the middle part I’m obsessed with right now, which is sales ready and tech sales ready. So it is ensuring, because AI is new for everybody. Yes. It’s a muscle, it is a proposition that you have to sell it’s value that you’re selling. I, I love what the gentleman from Lenovo was talking about. It’s, it’s that CSB always on motion. [00:14:21] Alexandra Zagury: Yes. And so really getting very crisp to the end seller at the, at the reseller. For example, at the end, seller at the partner about why Microsoft. Why now, how do I sell and how do I win? And giving them the assets, the competitive battle cards, the, the, the ability to end objection handling all these. Great, we have them. [00:14:45] Alexandra Zagury: I mean, the amount of content we have, but it’s about doing it at what I call precision velocity. [00:14:51] Vince Menzione: Precision [00:14:51] Alexandra Zagury: velocity, right? Which is this concept of how do we get very precise at a persona level. So that we get the velocity of impact. And so I’m, I’m very obsessed with that right now. And there’s two other things I’m very obsessed with, right? [00:15:04] Alexandra Zagury: The other one is this practice building, ensuring that we are together building these AI centers of excellence, um, especially for all our managed partners. This is something that I’ve put on, uh, every single PDM in our org is gonna, is gonna be talking about that. And then the third one is one that I find super interesting, which I think all of us. [00:15:25] Alexandra Zagury: Have a lot of work to do, which is partner to partner. [00:15:29] Vince Menzione: Yes. [00:15:29] Alexandra Zagury: If we look at a customer outcome, thank you. A customer outcome is built of many partners, right? There’s so many different touch points. I think some folks talk about seven partners in, in a customer outcome, and so how do we actually. Use agents, use agent solutions to suddenly unlock this opportunity because most of the time you’ll see an SSP with an si, maybe an ISV in the middle of a transaction to deliver on that customer outcome. [00:16:04] Alexandra Zagury: Yes. So what can we Microsoft do? And I’d love ideas, right? I haven’t cracked this. I don’t think the industry has completely cracked this. It’s more of a, a science than, than, well, more of an art than it is a science today. So that’s the third thing that I, I, I’d love to really improve and, and get better at. [00:16:20] Vince Menzione: I wish you got to see my slide earlier. ’cause I had the seven seats. I had this, I had the seven partners surrounding the customer. Customer is able to make their decisions now because with their cloud commitments [00:16:31] Alexandra Zagury: mm-hmm. [00:16:32] Vince Menzione: They’re in the, they’re in the seat where it used to be. I would rely on the partner to tell me what to do. [00:16:38] Vince Menzione: I, I’m cobbling together the best solution for my organization. Based on the trusted partners, to your point, those seven seats, and that’s partner to partner action. And Jay McBain was here yesterday and he took us through a great example. It’s AstraZeneca, that Microsoft won AWS, thought they were gonna win the deal, and then there were Microsoft partners in involved. [00:17:00] Vince Menzione: And the, the decision was made in December, but the deal didn’t happen until July. And that whole process was because all these different partners showed up. And influence the decision and the solution areas for that customer. [00:17:12] Alexandra Zagury: Yeah, that’s the best example of PLG partner led growth in action, which I think, again, that is the other thing that I’m super excited about is that actually p proving in the AI era that it’s about PLG as partner led growth, not the other PLGI. [00:17:31] Vince Menzione: I love that. I love that. Instead of product led growth, it’s partner led growth. So I understand there’s three layers that you’re very interested in that you want, you were gonna take us through today. Okay. Do you know about this, [00:17:44] Alexandra Zagury: the layers [00:17:45] Vince Menzione: of we have, uh, copilot chat. Oh, yes. 365 and, and agents. [00:17:49] Alexandra Zagury: Yeah. [00:17:50] Vince Menzione: From a product perspective, I thought maybe, [00:17:51] Alexandra Zagury: yeah, sure. [00:17:52] Alexandra Zagury: This is, I mean. This is the, uh, advantage of choosing the market Microsoft platform. Yeah, so as you look, look at it, there’s, there’s definitely different options, but when you look at Microsoft, what’s really, really interesting is that we have all the, all the layers. There’s no AI without data, and we’ve got that data foundation. [00:18:15] Alexandra Zagury: We also have the intelligence data foundation, right? Then we’ve got the, the layer of actually building those AI agents, and then the last layer that we have is actually the experience or the application layer. So when you look at our platform is a completely integrated platform with the different choices. [00:18:35] Alexandra Zagury: We are not behold, beholden to one LLM or another LLM. You’re actually able to bring your data and, and bring the LLM that you want to deliver on the, on the outcomes that you need. And I think that is very, very unique about our proposition. Yeah, I [00:18:50] Vince Menzione: agree. [00:18:50] Alexandra Zagury: But the other thing that is unique, it’s the most exciting product out there. [00:18:55] Alexandra Zagury: Agent 3, 6 5, our own oh oh seven. It really is a differentiated proposition that every single partner can build services around. Starting with your advisory services, tell me customer, what is it that you are thinking of? Then you actually move on to thinking about security because again, just like there’s no AI without data, you have to start with that data foundation. [00:19:24] Alexandra Zagury: There’s no AI without security and no security without ai. And so really thinking through how, uh, agent 3, 6, 5, I love it. I get these claps once a, I love, love really thinking about how Agent 3, 6 5 really unlocks, not only. A security budget, but an observability budget because you can do both. You are talking to both, uh, folks at the customer, right? [00:19:47] Alexandra Zagury: You can actually start talking about how you’re gonna actually govern all of these agents, manage all of these agents, but also there’s the observability layer, which is gonna tell you what actually can you do? How can you actually deliver on the outcomes that we all want from ai, which is productivity. [00:20:05] Alexandra Zagury: Experience and efficiency and all the other things. So I think this is the biggest opportunity this channel has ever seen, and every single partner is gonna have to make a choice on what platform they’re gonna lead with, and we believe it should be ours because it is completely integrated across these three layers with our very own oh oh seven. [00:20:30] Vince Menzione: I wanna get your perspective, but it feels like many of these partners in the MSP community are stuck at that CSP level. We were having this conversation about getting through that, coaching ’em through it. What would your be your perspective on that? [00:20:44] Alexandra Zagury: Um, I’d say, uh, use this moment to unlock that opportunity. [00:20:50] Alexandra Zagury: First off, invest in your skills. Get your, get your team skilled and, uh, on Microsoft, build your center of excellence. Map out your strategy where actually you’re gonna monetize and use all the different assets that we have. And if you are being really, um, I mean, most of them are serviced through a distributor. [00:21:12] Alexandra Zagury: Make your distributor accountable for supporting you in packaging the offers and giving you the, uh, information that you need in terms of skilling. And then in terms of co-selling, again, distributor has a lot of tools that can help you understand how to unlock the co unlock, the co-selling opportunity with Microsoft. [00:21:35] Alexandra Zagury: I think that’s another really big competitive advantage that Microsoft has. When, uh, Microsoft changed what it started by, by, by changing its strategy. I think clarity is kindness. We were very, very clear that where we want, we wanted partners, of course, to play an enterprise with a services stack, but we were very clear that we were betting on a partner led growth in small, medium enterprise, right? [00:22:03] Alexandra Zagury: And so we’ve built our whole operating system. Around that. And so I think it’s really about finding the information that you know you want, planning your strategy, getting skilled and go to market with us. Our co-sell Advantage is very, very unique. It is one of the only companies that has the sales teams completely aligned because CSP is our hero motion. [00:22:29] Vince Menzione: So being with a customer through the journey on CSP, but also renewals are a big component of growth. Talk to us about that. [00:22:36] Alexandra Zagury: Yeah. Renewals are, uh, a machine and an engine that is just absolutely beautiful. It’s your [00:22:42] Vince Menzione: flywheel. [00:22:43] Alexandra Zagury: It is your flywheel. Um, and it is the gift that keeps on giving. We, of course, have a very focused, um, and in fact, one of the things that I’ve done since. [00:22:52] Alexandra Zagury: Uh, since we’ve started, it started a very focused motion in terms of looking at our renewals. We have very specific targets. We, we like to see a renewal at 110% at the moment of renewal. And then we like to see a motion, t plus three, T plus six. That gets us to that a hundred and and 25%. But what we’ve also found out is that this needs to be an always on motion. [00:23:18] Alexandra Zagury: So one of the things that we’re doing is using this concept of precision velocity becoming very rigorous. ’cause we have all the data. Yeah. In terms of what is that next action, and really looking at starting that renewal process, we see that the partners that are able to reach the targets are the ones that start at T minus. [00:23:36] Alexandra Zagury: Six, maybe T minus three, you’re cutting it, but T minus six. And really building that constant motion, getting out in front Yeah. With the customer is really important. And then of course, we now even have these amazing go back motions, uh, with, with our partners where we actually, after the renewal, we go and. [00:23:56] Alexandra Zagury: The renewal was not at the target. We just constantly keep on going. Uh, going back with the, with the partners and we’ve unlocked a, a bevy of data. We, our operating model, we call it the pods, where the PDM sits at the center and orchestrates it with all the different roles that we have. And so we now have a very systematized moment, uh, motion of how to do the renewals. [00:24:18] Vince Menzione: So for the partners in the room, what’s one investment that they should make and what should every partner in the room do differently? Going into, uh, July 1st. [00:24:27] Alexandra Zagury: Well, I think the first thing, remember, CSP is our hero motion, so really for, uh, real, really focused on that. But the one investment, can I say two? [00:24:38] Vince Menzione: Please, please. [00:24:38] Alexandra Zagury: Your time. The, the first one is skilling, right? This is the time. [00:24:43] Vince Menzione: Yeah. [00:24:43] Alexandra Zagury: Technical intensity is super, super important, and really making those investments in skilling not only from a practice perspective, your your, your technical practice, uh, teams, but also from a sales readiness perspective. [00:24:59] Alexandra Zagury: This is a new muscle. It’s we’re all learning how to truly sell outcomes, and so getting your sales teams ready. Is is really important. And then the second one very tied to that is building your AI Center of Excellence based on the Microsoft platform. Because as we go into FY 27, you will see that the partners that prefer and grow with us are the ones that will see the investment come to them. [00:25:28] Vince Menzione: So I want to use the term front. I I, I’ve been avoiding the term frontier firm, but I think it is super critical. Everything I’ve heard today, like you need to be customer zero. You need to get in train, advance on it and go build against it. [00:25:41] Alexandra Zagury: Absolutely. Well, if you had, let me a third, I would’ve said customer zero. [00:25:46] Vince Menzione: You have it? Alright. We have less than a minute. Would you be okay if we ask for like maybe one question? Yeah, absolutely. We’ll do like one, maybe two. So good to have you by the way. Thank you, Vince. So nice to have you here. [00:26:04] Vince Menzione: I think we did such a good job. Oh, here we go. Here’s, here’s fun. A mic is coming your way. [00:26:20] Vince Menzione: Thank you. Here we go. Okay. [00:26:22] Guest: So we’ve been very keen on, um, skilling our people, and I still find it very hard to get all of the information out of Partner Center to get a complete global view. Are you and your team thinking about maybe having an MCP server access and having a real portal working with that data? [00:26:41] Alexandra Zagury: You just touched on one of our areas of improvement. Absolutely. In fact, um, thank you for, stay tuned for holding us accountable to that. That is definitely one of the things that we’re working on is how to integrate skilling hub into partner center. Right. As most of you will know, there are it Qs, and so that’s one of the things that we are definitely prioritizing, but thank you for holding me accountable to that one. [00:27:09] Vince Menzione: Awesome. [00:27:13] Vince Menzione: We have one more back here, David. I see. We wanna see how fast they can move that microphone across the room. Relay system here. The relay team. There we go. [00:27:25] Guest: That was excellent, Alexander. Thank you. And welcome. [00:27:28] Alexandra Zagury: Thank you. [00:27:29] Guest: Can you point to a specific example? ’cause I think it’s so critical what you highlighted just the skill piece and the customer outcomes piece. [00:27:35] Guest: Right. Can you point to a specific example of a story that you really love that highlights, uh, customers lighting it up with ROI. [00:27:44] Alexandra Zagury: Yeah, I think, you know, we’re, we’re, we’re a platform, so I just saw a win wire. Like at Microsoft, we get these win wires all the time about how an SSB actually won a, a deal against one of these big AI only companies. [00:28:00] Alexandra Zagury: And it was really about selling the full platform, right? Yeah. Because if, if you, if you put a full platform against an LLM proposition, I mean, the full platform really stacks up because it’s completely integrated. It’s not behemoth to one, you’re not making a bet on one company. And it really highlighted our mantra around trust and intelligence. [00:28:24] Alexandra Zagury: The customer was able to see, they, they were an M 365 customer, so all their iq, all their intelligence was already there. They knew that they, there were guardrails against it. They had a problem with shadow ai and by actually standardizing on copilot and going on that journey from copilot paid to agents, they sue the, they saw the full, uh, value proposition. [00:28:49] Alexandra Zagury: And so we won that deal and it was one of the. First E seven deals that we won, so it was great. [00:28:55] Vince Menzione: Fantastic. Great. Congratulations. [00:28:57] Alexandra Zagury: Thank you [00:28:58] Vince Menzione: Alex. I am so honored and thrilled that you got, you chose us to be your I I would say the first big Yeah, absolutely. Presentation in front of the partner community. [00:29:07] Vince Menzione: I’m so excited to have you. [00:29:08] Alexandra Zagury: Thank you [00:29:09] Vince Menzione: guys, and hopefully many more times ahead with us. [00:29:10] Alexandra Zagury: Absolutely. Invite me at anytime. [00:29:12] Vince Menzione: Okay. Well, thank you [00:29:13] Alexandra Zagury: so much. [00:29:14] Vince Menzione: Thank you. So great [00:29:15] Alexandra Zagury: to have you. Thank [00:29:16] Vince Menzione: you so much. Thanks for listening to The Ultimate Partner Podcast. If today’s conversation resonated, share it with a partner leader in your network. [00:29:26] Vince Menzione: Subscribe where you listen, and head over to the ultimate partner.com. For show notes related content and the resources for this episode. And if you haven’t already, now’s the time to register for the Ultimate Partner Live Event in Reston, Virginia, October 26th through October 28th. Until next time, keep showing up in the rooms that matter because being in the room changes everything.

Elena Burger is joined by a16z's Andy McCall and Joe Schmidt to break down two very different ways AI startups can go to market: the lighthouse and the landgrab. Should founders win a handful of marquee customers whose credibility unlocks an entire industry, or move quickly across a broad market where the ROI already speaks for itself? Drawing on Joe's Lighthouse or Landgrab framework and Andy's experience building sales organizations at Samsara and Meraki, they explore how founders can determine which strategy fits their market, when social proof matters more than math, and why the current rush to adopt AI has created a rare window for startups to sell big software again. They also get tactical on POCs, pricing and ACV, hiring early sales teams, moving from mid-market to enterprise, and why founders shouldn't spend too much time perfecting their GTM strategy before talking to customers. As Andy puts it: spend 1% of your time on strategy and 99% executing.   Resources: Read Joe Schmidt's "Lighthouse or Landgrab": https://a16z.com/lighthouse-or-landgrab-how-to-pick-your-ai-sales-strategy/ Follow Andy McCall on LinkedIn: https://www.linkedin.com/in/amccall/ Follow Joe Schmidt on X: https://x.com/joeschmidtiv Follow Elena Burger on X: https://x.com/VirtualElena Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The problem with marketing is that so many teams think their job ends when a lead becomes an opportunity. The highest-performing GTM teams know that's where the REAL work begins.In this episode, Amber sits down with Aviv Canaani, CRO at Datarails, to unpack how the company transformed from a 90% outbound sales motion into a scalable revenue engine powered primarily by inbound demand. Rather than optimizing marketing and sales separately, Aviv shares why the entire funnel should be treated as one connected revenue system and how RevOps, automation, and marketing architecture make that possible.In this episode:Why marketing and sales should operate as one revenue engineHow Datarails shifted from 90% outbound to 90% inboundWhy the best marketers take bigger creative risksHow RevOps can automate the revenue engine, not just report on itWhy attribution has limits, and what leaders should measure insteadHow AI is changing the future of GTM operationsIf you're trying to create more pipeline, improve sales efficiency, or better align marketing and revenue teams, this episode will absolutely challenge how you think about building a modern GTM organization.-----------------------------------------------------