Podcasts about GTM

133 million learners. 100% of the Fortune 100. And the woman steering go-to-market behind those numbers will tell you to stop chasing churn. Monika Saha, CCO of Articulate, doesn't trade in best-practice platitudes. In this episode she takes the sacred cows out back: why "customer education is a cost center" is half-wrong instead of all-wrong, when fighting retention is a flat waste of energy, and why PLG companies are quietly light-years ahead while everyone else optimizes the wrong thing. Host Josh Schachter pokes the bear. Co-host Samantha Murray pushes back. Monika doesn't blink. If you run customer success, education, or GTM and you're tired of being told what you already know, this one's built to make you uncomfortable in the good way.Josh is writing a book on building customer relationships. Follow his journey and insights at www.joshschachter.com---What You'll Learn- Why "customer education is a cost center" is partly true- How to standardize and modularize content so you stop reinventing the wheel- When improving churn is actually a waste of energy- How to segment a long tail so you invest where returns are real- Why PLG companies dominate in-app and digital motion- A simple QBR exercise to find AI-ready process bottlenecks- How to structure a number across a core product plus early cross-sells---Want the playbook, not just the conversation? Subscribe for deep-dive, actionable breakdowns from every episode at unchurned.substack.com.---Timestamps0:00 - Preview and Meet Mac, Monika's dog1:08 - Meet Sam Murray, Gainsight & Monika Saha, Articulate2:11 - Articulate's Overview4:20 - Monika's remit as Chief Commercial Officer: trial to renewal5:37 - Lessons from her Gainsight CMO days9:00 - Customer education & internal enablement14:53 - Debate: is customer education a cost center?20:30 - Controversial take: when fixing churn is pointless23:43 - Why digital motion is foundational at a PLG company26:56 - Can non-PLG B2B companies experiment like this?28:48 - Embracing efficiency with AI32:30 - Hitting the number: core product vs cross-sell---Where to Find the GuestSamantha Murray: https://www.linkedin.com/in/samantha-murray613/Monika Saha: https://www.linkedin.com/in/monikasaha/---Where to Find Josh:LinkedIn: https://www.linkedin.com/in/jschachter/Unchurned Substack: https://unchurned.substack.com/

AI may change software overnight, but company building still takes time.Ryan Smith explains why, despite the pace of AI, “the race is going to be way longer than anyone thinks.”He reflects on Qualtrics surviving multiple market cycles and ultimately being acquired by SAP for $8 billion days before going public.Guest: Ryan Smith, co-founder QualtricsConnect with Ryan SmithXLinkedInConnect with Joubin:XLinkedInEmail: grit@kleinerperkins.comFollow Grit: LinkedInX​Learn more about Kleiner Perkins

In this today's segment, Dan Sperring, founder and CEO of Align ICP, breaks down a mistake most revenue leaders make when defining their ideal customer profile. The instinct is to chase the highest lifetime value customers, but those segments are often the hardest to win, the slowest to close, and the first to break when the market shifts. This clip focuses on how to balance three critical factors inside your ICP: lifetime value, ease of acquisition, and market health. Dan explains why ignoring any one of these creates pipeline risk, and how leaders can avoid over-rotating into segments that look great on paper but fail in execution. For leaders responsible for predictable growth, this is about making smarter tradeoffs, not just better targeting. Dan Sperring is the founder and CEO of AlignICP, a company focused on helping revenue teams align around high-value customer segments to drive predictable growth. He brings experience across customer success, revenue leadership, and scaling SaaS businesses through product-market and go-to-market alignment. Connect with Dan: AlignICP LinkedIn Books mentioned: The Innovator's Dilemma by Clayton M. Christensen The Innovator's Solution by Clayton M. Christensen and Michael E. Raynor Predictable Revenue by Aaron Ross and ​​Marylou Tyler  Amp It Up by Frank Slootman Tools and podcasts mentioned: clay.com zoominfo.com The Science of Scaling Podcast Listen to the full episode: Aligning Pipeline to Ideal Customer Profile with Dan Sperring Get the Force Management framework for aligning your ICP, sales motion, and customer lifecycle around high-value use cases and measurable business outcomes: The Predictable Revenue Framework: Guide for Leaders Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

We're back with some hot topics in GTM!This week we discuss something that's been on everyone's mind lately: AI token and credit spend. How do you choose the right tool and model for the job? Do you *really* need AI for that deterministic workflow?Then we test Hubspot's AI SDR Avatar and see how things have changed since the last time we reviewed an AI Avatar. Lastly, we plug a great job opportunity… One right here at CS2!We'll see you there.This week: 00:00 Intro01:27 How to manage AI token/credit spend in GTM20:48 Testing Hubspot's AI SDR avatar39:00 CS2's new job openingHear more from us: Subscribe to us on Youtube: https://www.youtube.com/channel/UCN-x5u0G03LWmU0Ds_4zR8wSubscribe to our newsletter here: https://www.cs2marketing.com/revenue-growth-architects#subscribe-to-newsletterFollow Crissy on LinkedIn: https://www.linkedin.com/in/crveteresaunders/Follow Charlie on LinkedIn: https://www.linkedin.com/in/charliesaunders/

Sam Blond is the Co-founder and CEO of Monaco, the revenue engine for startups.Sam is one of the best sales operators in tech. He spent four years as CRO at Brex, where he helped scale it to a ~$12B valuation, ran sales at Zenefits before that, and got his start at EchoSign.If there's a modern GTM playbook, Sam helped write it. Our conversation walks through how AI has rewritten a big chunk of it. But most importantly, we talk about what hasn't changed.We get into the sales work AI is now better at than humans, and why Sam thinks 90% of startups misdiagnose their bottleneck as conversion when it's really demand gen.He explains why he doesn't measure early brand marketing at all and trusts anecdotes over attribution, walks through the full Monaco launch playbook including the Super Bowl box-truck story, and shares a rev-ops insight from Brex, including how they figured out a specific ICP converted at 4x the rate of another.Thank you to Numeral, Flex, Amplitude, and Merge for supporting this episode.Numeral: The end-to-end platform for sales tax and compliance https://www.numeral.comFlex: Get premium banking and a net 60 day credit card at 0% APY https://home.flex.one/referral/bananacapitalAmplitude: AI analytics, all you have to do is ask https://www.amplitude.comMerge: Every modal. One API. Total control. Check out Merge's Agent Handler. merge.dev/turnerTimestamps:(0:00) Scaling Brex to $12B(1:14) How AI speeds up prospecting and TAM building(5:19) Using AI to get more leverage(9:15) Incubating Monaco at Founders Fund(12:56) Innovator's dilemma in AI(15:57) Why AI companies build full platforms, not wedge products(23:30) Revenue is just a math equation(27:18) Two ways AI increases conversion rates(36:56) AI will never replace spending time with customers(39:46) Don't measure the impact of brand marketing(49:03) Your marketing must be different (and hard)(58:39) Customer discovery calls and working with design partners(1:03:03) The zero to 100 launch(1:11:00) Monaco's launch playbook(1:19:00) Send gifts that are unique and social(1:22:17) Naming your company(1:28:04) Founders should send early outbound(1:32:38) How multi-channel augments AI outbound(1:39:42) Using intent signals and outreach timing to increase conversions(1:43:28) Two common ways founders mess up when scaling revenue(1:50:22) Monaco's Forward Deployed AE'sReferencedTry Monaco: https://www.monaco.com/Careers at Monaco: https://jobs.ashbyhq.com/monacoSam's launch post: https://x.com/samdblond/status/2026420015793320129?s=20Follow SamTwitter: https://x.com/samdblondLinkedIn: https://www.linkedin.com/in/sam-blond-791026b/Follow TurnerTwitter: https://twitter.com/TurnerNovakLinkedIn: https://www.linkedin.com/in/turnernovakSubscribe to my newsletter to get every episode + the transcript in your inbox every week: https://www.thespl.it/

You've been posting. Building followers. Showing up consistently. Then LinkedIn changes the algorithm — and overnight, your reach drops to nothing.In this episode, Dave and Regan expose the uncomfortable truth about building your brand on a platform you don't own — and the real strategy that's still generating booked calls, inbound leads, and pipeline in 2026.What you'll learn:→ Why LinkedIn stripped Dave of his Blue Tick (and what it reveals about the platform)→ The algorithm change killing reach for people with 10K, 25K, even 100K+ followers→ Why building on someone else's platform is the biggest hidden risk in sales→ The "manual connection ritual" generating real inbound without paid ads→ How to train AI to write content that sounds like YOU — not generic slop→ Why your best prospects never like your posts but are still watching and buying→ The one message 99% of reps never send after connecting on LinkedIn→ The Alex Hormozi consistency lesson every salesperson needs to hear→ Why quitting after 2 weeks is the #1 reason LinkedIn never works for youIf you've ever wondered whether LinkedIn is actually worth it — this episode gives you the honest answer.

It's official: Knownwell is now part of 2X, and together we're building the leading human-agentic GTM services company, unifying B2B marketing, sales, and customer success. In this special in-person episode of AI Knowhow, Knownwell CEO David DeWolf and 2X founder Dom Colasante join host Courtney Baker at 2X headquarters to break down the news and what drove the acquisition, including: why software and services are converging into a single category and what humans uniquely own in an agentic world. In this episode: The big news: 2X acquires Knownwell to build the first human-agentic GTM services company The trend permeating the services world: customers buy outcomes, not software or services Go-to-market fragmentation, and why marketing, sales, and customer success must run as one motion The 90/10 retention paradox every revenue leader should know Why all work should have a human in the loop Moving Knownwell's commercial intelligence up the stack into sales and marketing Show Notes: Read today's press release: https://2x.marketing/press-release/2x-acquires-knownwell/ Connect with David DeWolf: https://www.linkedin.com/in/ddewolf/ Connect with Dom Colasante: https://www.linkedin.com/in/domeniccolasante/ Connect with Courtney Baker: https://www.linkedin.com/in/courtbaker/

For years, healthcare organizations focused on securing digital channels while treating phone calls as a trusted service channel. That assumption no longer holds true.  In this episode, Sandy sits with Jason Barr, the Vice President of Strategic Sales for Healthcare at Pindrop, who explains how AI-powered voice cloning, deepfakes, and synthetic identities are transforming the cybersecurity landscape. Jason shares how healthcare organizations can defend against AI-driven fraud, verify identity in real time, and protect patients, providers, and employees in a world where even a familiar voice may not be what it seems. In this episode, they talk about: AI has transformed the phone from a trusted service channel into a rapidly growing cybersecurity threat vector for healthcare organizations. Cybercriminals can now use AI-powered tools to launch thousands of voice-based attacks per day, dramatically increasing the scale and efficiency of fraud attempts. Many attackers use voice channels not for immediate theft, but for reconnaissance, collecting sensitive information that can later be used to target providers, payers, and patients. Traditional identity verification methods such as knowledge-based questions and one-time passcodes are becoming increasingly vulnerable to modern fraud tactics. Continuous identity verification is emerging as a new security model that validates users throughout an interaction rather than only at the point of authentication. Pindrop analyzes thousands of signals during voice interactions to determine whether a caller is who they claim to be, whether they pose a risk, and whether they are even human. Healthcare organizations are facing a growing challenge in distinguishing between legitimate automation and malicious AI-powered bots. Deepfake technology is now sophisticated enough to mimic both voices and video, creating new risks across hiring, workforce management, and patient-facing operations. Help desks and support centers remain attractive targets because attackers often use social engineering tactics to pressure employees into resetting credentials. Voice-based security solutions can reduce fraud while simultaneously improving operational efficiency and the customer experience. One healthcare organization achieved a 90% reduction in fraud after implementing voice authentication and risk detection technology. Healthcare leaders must begin evaluating voice security as part of their broader cybersecurity strategy, as AI-enabled attacks continue to grow at an unprecedented pace.  A Little About Jason: As a West Point graduate and former U.S. Army Officer, Jason brings the operational rigor, discipline, and leadership foundation of combat-tested command into the boardroom and the GTM arena. He thrives where GTM transformation is mission-critical: aligning strategy to investor outcomes, building high-performing teams, and delivering predictable growth.

In this insightful interview, David Niewiadomski, General Manager at Building Works, shares his journey from civil engineering to leading innovative building data solutions. Discover how Building Works is transforming construction closeout processes, improving data management, and future-proofing buildings with cutting-edge technology.   key topics Building data management and closeout processes The role of technology and AI in construction Building lifecycle and owner data needs   Chapters   00:00 Introduction to David Niewiadomski 00:19 Career Journey and Pivotal Moments 02:44 Mentorship and Learning 03:38 Joining Building Works 09:10 Challenges in the Construction Industry 11:10 Understanding Client Needs 15:05 Technology and Data Management 16:58 Future of Building Works 18:34 Conclusion and Final Thoughts  

What's up everyone, today we have the pleasure of sitting down with Lindsay Rothlisberger, Director of GTM Innovation at Zapier.(00:00) - Intro (01:23) - In This Episode (02:00) - Sponsor: Knak (03:08) - Sponsor: MoEngage (05:49) - How Zapier's RevOps Team Built Its AI Foundation (19:43) - Why Visibility Has to Come Before Governance in AI Adoption (24:58) - Sponsor: GrowthBench (25:58) - Sponsor: GrowthLoop (29:48) - How Zapier Fights Context Rot in Its AI Shared Brain (35:55) - How Zapier Governs Shared AI Skills from Review to Long-Term Ownership (39:27) - What Happens to RevOps When Everyone Around Them Can Build (45:05) - The Director of GTM Innovation Role and the Sharing Problem Nobody Has Solved (50:47) - What Keeps Lindsay Grounded in the Middle of All This Change (52:00) - Lindsay on Getting Buy-In and What She's Reading Summary: When a startup claimed in April 2026 that it invented the marketing engineer role and that RevOps professionals "just do tool integrations," Lindsay Rothlisberger had heart palpitations. Her team at Zapier had been building AI into GTM workflows for years before the announcement. In this episode, she walks through the 6-component AI governance model she published publicly: a golden path to Cursor, a structured shared brain in Google Drive, data policies built with the security team, a visibility layer powered by a custom Zapier agent, a context engineering strategy that fights context rot, and a red-yellow-green skills review gate. She also names the part of the model that's still broken, and it's more honest than most AI governance conversations allow. If your team is figuring out how to govern AI at scale without killing the momentum, this is the inside view from someone who's done it.About Lindsay RothlisbergerLindsay Rothlisberger is Director of GTM Innovation at Zapier, where she leads the company's AI-powered GTM transformation internally and works alongside customers navigating the same shift. She spent 4 years building Zapier's RevOps function from zero, scaling it into a cross-functional engine covering AI, systems, analytics, planning, and enablement, and growing ACV 10x in that time. Before moving into the innovation role, she led marketing operations and lifecycle programs at UNiDAYS across B2B and B2C markets. She writes on LinkedIn about what Zapier is actually shipping, what works, and what doesn't.How Zapier's RevOps Team Built Its AI FoundationMost RevOps teams doing serious AI work have been doing it longer than the current conversation suggests. The tools are newer and the terminology has changed, but building automated workflows that take unstructured data and produce structured, actionable outputs for salespeople and marketers? That's exactly what good RevOps teams were doing before anyone put a trending name on it.Lindsay's team at Zapier started experimenting with AI several years ago, when it was first becoming accessible. Zapier gave its RevOps team the tools to experiment early, and rather than waiting for a strategy to materialize, they picked a specific, annoying problem: sales handoffs. Salespeople were going into first calls without enough context about the lead. The team pulled all the relevant unstructured data, engagement records, support tickets, email threads, and used AI to generate clean, contextualized briefing materials. The result was a measurable lift in lead-to-opportunity conversion rates, and a pattern the team has used ever since: find something specific that's visibly broken, prove AI fixes it, then apply that logic somewhere else.That early foundation matters now because the landscape has shifted in a way that affects RevOps directly. Claude Code, Cursor, and similar tools have made it possible for people with no engineering background to build real things. Sales managers are writing AI skills that generate quarterly revenue strategies for reps. CS reps are building account monitoring tools. Lindsay's read on this is that the RevOps team's job isn't to slow that down. It's to give it a governance structure so it can scale without creating a mess, and to be the team that built the foundation those builds are operating on.At Zapier, that governance structure is anchored by an AI center of excellence led by a chief AI officer. The architecture is a hub-and-spoke model: the central team sets the frameworks, the guidelines, and the enablement resources; Lindsay serves as the spoke into go-to-market, with a partner who works alongside her. The 2 of them act as a feedback loop between what's happening on the ground in sales, marketing, and CS and what the central team needs to know. The center of excellence is small, just a handful of dedicated people, but it reaches into every function through the spoke structure.The first thing the center of excellence built for non-technical GTM employees was the golden path to Cursor. Cursor had already been adopted by Zapier's product and engineering teams. For GTM, the barrier wasn't the technology itself; it was the setup. Someone who's spent their career in spreadsheets and CRM doesn't automatically know how to configure a development environment. The golden path is step-by-step onboarding: from installation through a fully configured Cursor environment with the right MCP connections (Databricks, Zapier), the right rules, and the right context already loaded. The whole point is removing the 2-hour configuration overhead that otherwise kills adoption on day 1.That context is the shared brain: a structured Google Drive hierarchy with company-level, department-level, team-level, and working group-level folders. The first iteration meant converting existing documentation into markdown files and organizing them into a folder structure that agents could traverse predictably. Lindsay describes the experience of setting it up as oddly satisfying for an ops person who has spent years wishing the organization's institutional knowledge lived somewhere findable instead of scattered across a Google Drive that nobody had cleaned up in years. The goal of the initial build wasn't completeness. It was a working foundation that gave people enough context to get value from their agent setup without needing to build from scratch.The companies operating furthest ahead in AI adoption right now are the ones that treated the shared brain as infrastructure rather than a side project. Getting every GTM employee configured, context-loaded, and working from a shared knowledge base is unglamorous work, but it's the layer every other build depends on.Key takeaway: Before anyone on your GTM team builds anything with AI, create a centralized setup guide that handles environment configuration, approved MCP connections, and context loading from a structured knowledge base. Start with the tools your technical teams are already using and build a version of that golden path for non-technical employees. The 2-hour configuration friction that stops people on day 1 is a solvable problem, and solving it once prevents you from solving it individually for every person who tries to onboard.How Long It Actually Takes to Build a Shared BrainThe shared brain question that comes up in every version of this conversation is a practical one: how long does it actually take? Zapier's first rollout was a 4-week sprint, and the design of that sprint was deliberate about scope. Rather than trying to capture everything the organization knew, the team focused on what Lindsay calls the slow layer of context: things that don't change often. Company strategy documents. Ideal customer profile definitions. Lead and opportunity definitions. Basic playbooks. These documents already existed. The sprint was mostly ...

AI agents are not just changing sales tools. They are changing the job of the seller.In this episode, John sits down with Kris Billmaier, Executive Vice President and General Manager of Agentforce Sales and Growth Products at Salesforce, to talk about Agentforce, headless software, AI-native sales workflows, and what happens when sellers start managing teams of agents.If you are in sales, sales leadership, enablement, or GTM strategy, this episode gives you a practical look at where humans still matter, how agents can support pipeline and qualification, and why AI adoption needs clear use cases, measurement, and training.Want to stay ahead of where sales are heading next? Visit www.jbarrows.com and learn how you can Make It Happen.What You'll LearnWhy product-led growth is moving toward agent-led growthHow Salesforce is thinking about headless software and conversation-first AIWhy AI-first SaaS is not just a front-end feature or branding exerciseHow agents are changing SDR and BDR work at SalesforceWhy successful AI adoption starts with a narrow use case and a real training planWhat sellers need to become as agent teams take on more busy workKris Billmaier is Executive Vice President and General Manager of Agentforce Sales and Growth Products at Salesforce, where he leads the product strategy and vision for Agentforce Sales. With more than 20 years of experience across productivity software, search, and enterprise technology, Kris has launched category-defining products, scaled startups, and is now building a future where agents and sellers work together to grow revenue.Connect with Kris Billmaier:Website: https://www.salesforce.com/ap/Li: https://www.linkedin.com/in/krisbillmaier/John 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: https://www.linkedin.com/in/johnbarrows/ Instagram: https://www.instagram.com/johnmbarrows/TikTok: https://www.tiktok.com/@johnmbarrowsCheck out John's Membership: https://go.jbarrows.com/Join John's Newsletter: https://www.jbarrows.com/newsletter

Text us your thoughts on the episode or the show!For years, the hard part of ops work was building the technology. Now the tech is getting easier while the people and process side is getting harder. So why are so many organizations still stuck debating AI instead of activating it?In this episode, host Michael Hartmann sits down with Andrea Tarrell, President of the Tech Services line at Trilliad and CEO of Sercante. Together, they discussed the human side of change in the AI world with speed, trust, risk tolerance, and the trade-offs GTM teams are making right now.In this episode:Why the technology got easier, but the people and process side got harderHow much of AI adoption is really a trust and change management problem, not a tech oneFear of job replacement vs. plain organizational inertiaAI may not replace your job, but someone using it well may outperform someone who refuses to adaptSolving the tension between "move faster with AI" and "watch out for the risks."What companies get wrong about risk management and tolerance for risk in the AI worldWhy old governance frameworks may not fit a world of fast experimentationAnd a lot more...Whether you lead an ops team or sit inside one, this is a timely conversation about innovation, speed, governance, and practical business reality.If you enjoyed this episode, subscribe, leave a review, and share it with someone in the ops community who would find it valuable.Episode Brought to You By MO Pros The #1 Community for Marketing Operations ProfessionalsSupport the show

Michael Walrath, Chairman and CEO of Yext, returns to break down why the market has left a profitable, $400 million mid-cap public software company trading at one times revenue, even with over $100 million in EBITDA. He joins AJ Bruno and Asad Zaman to argue that the so-called SaaS apocalypse has almost no data behind it, that most AI layoffs are really a decade of go-to-market overhiring unwinding, and that boring compounders still out-return the hypergrowth darlings. Topics include how venture capital distorts software valuations, why no one is coming to help the 2021 unicorns stuck in broken cap tables, the great GTM despecialization, and the extend-and-pretend game inside venture funds. Plus, a Quiz Pro Quo on new business creation in the US and a Bulls and Bears debate on the future of mid-cap software and the stickiness of the AI platform. Read Michael's essay, No One's Coming to Help You: https://x.com/michaelpwalrath/status/2051364181237010778 Key Takeaways: - The market has left profitable mid-cap software for dead in favor of AI-native growth stories, and Michael Walrath, Chairman and CEO at Yext, leaned into how strange that is for a business that still prints cash. As he put it, "who's writing our obituary? It's the venture capitalists who are funding high-growth ARR companies," even as those same firms can't say what that ARR really means. - The loudest voices setting software valuations are venture investors, and Michael argued their certainty is out of step with their actual hit rate. He called them "remarkably sure of themselves for guys whose whole business model is being right 5 to 10% of the time," noting that being right much more often than that would mean a VC is playing it too safe. - Michael's answer to the hypergrowth-or-die mindset is that durable value comes from compounding cash flow, not chasing the next high-growth story. Pointing to a century of market history and operators like Berkshire Hathaway and Liberty Media, he said, "if you compound effectively, you will out-return these super high growth stories, unless those super high growth stories eventually become compounders." - A lot of the layoffs being blamed on AI may be a decade of go-to-market overhiring finally unwinding. Michael framed the skeptic's question directly: "is it really AI? Or is this a choice that you're making because you overhired for 10 years." Asad Zaman, CEO at Sales Talent Agency, agreed, pointing out that even inside the most AI-native companies he visits, the fundamental way the business runs has not really changed. Connect with the Hosts & Guests: Host: AJ Bruno, CEO at QuotaPath - https://www.linkedin.com/in/ajbruno3/ Host: Asad Zaman, CEO at Sales Talent Agency - https://www.linkedin.com/in/azaman1/ Guest: Michael Walrath, Chairman & CEO at Yext - https://www.linkedin.com/in/michael-walrath-b63166/ Topline is more than a YouTube Channel: Subscribe to Topline Newsletter: https://toplinemedia.substack.com/ Tune into Topline Podcast, the #1 podcast for founders, operators, and investors in B2B tech: https://www.joinpavilion.com/topline-podcast Join the free Topline Slack channel to connect with 600+ revenue leaders to keep the conversation going beyond the podcast: https://www.joinpavilion.com/topline-slack Chapters:  00:00 Cold Open and Intro 02:33 Dead But We Just Don't Know It 08:47 Narrative Violations and Hype 11:00 VCs Right 10% Of The Time 14:22 Whose Case Are You Making? 19:20 Why Boring Compounders Win 24:55 The SaaS Apocalypse Myth 28:47 Are AI Layoffs Really AI? 36:16 The Great GTM Despecialization 39:55 Quiz Pro Quo 48:54 No One Is Coming To Help You 55:11 Extend And Pretend 1:01:41 Doubling Cash Flow In 5 Years 1:04:17 Bulls and Bears 1:07:30 What's The AI Moat?

Maksym Tereshchenko is building a startup while his country is at war.  ✅ What you'll learn:Stop founder-driven sales and build a predictable machineSurvive the mental toll of doing 10 jobs all at onceHire your first GTM person without taking a guessSpot supply chain disruption before it hits youLeverage relationships to close dealsReach out to Max:LinkedIn: Maksym TereshchenkoWebsite: mantisanalytics.com

Send us Fan MailGuest: Ivan Lee, Founder & CEO of DatasaurWe're looking at what happens when AI changes the market faster than the old SaaS playbook can keep up.Ivan Lee, founder and CEO of Datasaur, joins SaaS Backwards to share how his company navigated one of the most dramatic shifts in enterprise AI. Datasaur started as a data annotation platform before ChatGPT changed customer priorities, paused AI roadmaps, and forced the company to rethink its product, GTM strategy, and business model.Ivan explains why out-of-the-box tools like ChatGPT Enterprise and Microsoft Copilot can be useful starting points, but often hit a ceiling for regulated enterprises that need private AI trained on their own data, workflows, and processes.He also shares how Datasaur moved from a traditional SaaS model toward end-to-end AI solutions, what founders can learn from disrupted marketing channels, and why the future of SaaS may depend less on selling software access and more on solving the customer's actual job to be done.Key Takeaways:Why enterprise AI often breaks down when it lacks access to private data and internal workflowsHow ChatGPT disrupted Datasaur's original AI roadmap and customer baseWhy old SaaS GTM channels stopped working in a crowded AI marketHow Datasaur rebuilt around private, secure AI for regulated industriesWhat SaaS founders should measure when marketing “best practices” stop producing results---Stalled pipeline? Lost deals? Diagnose your GTM gaps with a free, actionable checkup. 

EPISODE DESCRIPTIONI sat down with Amit Mahensaria, co-founder of Pred, to explore why the $500 billion sports betting industry is ripe for disruption. Amit isn't a typical Web3 founder , he came in as a degen, a 22-year sports trader who got tired of the house always winning. In this episode, we dig into how Pred is building a trustless, peer-to-peer sports prediction exchange on Base, why live sports demand a completely different architecture than general prediction markets like Polymarket, and what it really takes to build an on-chain order book that can keep up with a goal being scored in real time. We also get into the state of the prediction market industry, who's going to win the space, and why Amit believes the Hyperliquid of sports trading hasn't been built yet , until now. DISCLAIMERNothing mentioned in this podcast is investment advice and please do your own research. It would mean a lot if you can leave a review of this podcast on Apple Podcasts or Spotify and share this podcast with a friend. Be a guest on the podcast or contact us - https://www.web3pod.xyz/CONNECTPred Website: https://www.pred.app/trade/fif-cdr-den-2026-06-03Twitter/X - Pred: https://x.com/predofficialWeb3 with Sam Kamani: https://www.web3pod.xyz/KEY POINTS WITH TIMESTAMPS• [00:02] Sam introduces Amit Mahensaria, co-founder of Pred, a sports-native prediction exchange at the intersection of AI, crypto, and blockchain• [01:11] Amit shares his background , not a typical Web3 founder, but a 22-year sports trader and DeFi degen since the 2020 DeFi Summer• [02:32] His co-founder is a Web3 OG and former product and design head of Binance India• [03:38] The origin story: Amit built a peer-to-peer sports trading community 7 years ago after getting frustrated with sportsbook middlemen always taking a cut• [05:43] The core thesis , middlemen are being removed from every industry, and sports betting is one of the last frontiers where the house still always wins• [07:16] Why general-purpose prediction markets like Polymarket and Kalshi are not designed for sports UX or speed• [10:27] The biggest technical challenges: building an off-chain order book with on-chain matching, achieving 10x lower latency than competitors, and managing correlated multi-outcome order books in real time• [14:44] The Venn diagram problem , crypto users and frequent sports traders overlap by around 40%, poker bettors and crypto users by 60%• [16:29] How Pred abstracts crypto complexity away for mainstream users, and partnerships with fund.xyz and swap.com for on-ramping• [17:47] Key product learnings from 200-250 beta users over 8 weeks , sports UX must look nothing like a financial trading terminal• [19:47] Why Pred chose to build on Base , speed via Flash Blocks, distribution, and a roadmap conversation with Jesse Pollak• [21:55] The prediction market landscape has over 120 projects, but the space is still very early , the Hyperliquid of prediction markets hasn't emerged yet• [25:54] Pred is coming out of invite-only beta and opening to the public by end of month, starting with soccer only• [28:46] Advice for Web3 founders , do not launch a points program before you have PMF; GTM too early will kill you• [32:22] Long-term vision: a trustless, globally accessible sports trading exchange where users own the platform and trust every trade• [34:09] Liquidity management strategy , a transparent algo-driven vault similar to Hyperliquid's HLP, plus easy API onboarding for sports-focused market makers• [38:20] Current asks: users who want to trade and give feedback, sports-focused market makers, and a larger fundraise planned post-public launch

We have another guest this week! Our GTM Engineering Lead, Xander Broeffle joins us for a conversation on various tools and tech and how they are adapting in today's market. First, could Zoominfo be back from the dead with their new GTM.ai? Can agents find this data without ZI or does ZI have an edge? Then, Xander covers some Clay use cases we've been seeing in clients and discusses recent updates. Finally, can Marketo turn the ship around and release actually useful AI features? Let's dive in! This week: 0:00 Intro 5:00 ZoomInfo's new GTM.AI 21:05 Clay Updates 39:30 Can Marketo Turn the Ship Around? Hear more from us: Subscribe to us on Youtube: https://www.youtube.com/channel/UCN-x5u0G03LWmU0Ds_4zR8w Subscribe to our newsletter here: https://www.cs2marketing.com/revenue-growth-architects#subscribe-to-newsletter Follow Crissy on LinkedIn: https://www.linkedin.com/in/crveteresaunders/ Follow Charlie on LinkedIn: https://www.linkedin.com/in/charliesaunders/

Too many B2B marketing teams are still talking leads when they should be talking revenue. In this episode, Shahin sits down with David Heyworth, GTM advisor and former Head of Marketing at Vocus, to unpack what it really takes to drive commercial outcomes in the second half of 2025. From ditching MQL vanity metrics to building genuine alignment with sales, finance, and product, David brings hard-won lessons from complex B2B environments in Australia. This is a conversation packed with practical frameworks and honest war stories, including one of the most creative ABM activations you'll hear about: a commissioned coin ceremony at the Australian War Memorial, hosted by former Governor-General Sir Peter Cosgrove, that cemented a 20-year defence sector partnership without a single sales pitch. Guest Introduction David Heyworth is a GTM advisor and CMO with deep experience leading marketing in complex B2B environments across Australia, including his tenure as Head of Marketing at Vocus, one of Australia's leading fibre and network solutions providers. He specialises in go-to-market strategy, sales and marketing alignment, and account-based selling for enterprise and government markets. Key Topics Why agility, balance, and growth are the non-negotiables for B2B marketing teams in the second half of 2025Shifting the conversation from lead generation to revenue opportunities and why talking in dollars gets marketing a seat at the tableHow marketing can own the full GTM motion: building interlocks with sales, finance, and product leadership rather than operating in isolationThe case for an inside sales or sales discovery rep function that sits within marketing and how to prove the model before committing headcountWhy ABM works better when reframed as account-based selling (ABS) and how to sequence one-to-many, one-to-few, and one-to-one engagementBattle-tested lessons from event marketing gone wrong and how champions and pre-agreed outreach schedules turned it aroundA standout defence sector ABM case study: creating a custom commemorative coin and hosting a ceremony at the Australian War Memorial to honour a 20-year partnershipGo-to-market fundamentals that get skipped: market definition, value proposition, messaging frameworks by segment and buyer persona, and why these must come before the marketing plan Resources & Links People Peter Cosgrove-former Chief of the Defence Force and 26th Governor-General of AustraliaSeth Godin - Author and marketing thought leader; David recommends his book Purple Cow on differentiation. Companies & Tools Vocus -Australian telco where David served as CMO.Akimbo -Seth Godin's Podcast Books Purple Cow by Seth Godin Contact & Credits Host: Shahin Hoda Guest: David Heyworth Produced by: Shahin Hoda and Alexander Hipwell Edited by: Alexander Hipwell Music by: Breakmaster Cylinder APAC's B2B Growth Podcast is Presented by xGrowth

"Let me think about it." Every salesperson has heard it. Most accept it. Almost none of them know what it actually means.In this episode, Dave and Regan break down the most feared phrase in sales — why it almost always signals something you did wrong earlier in the conversation, and the one vulnerable move that turns a dead deal back into a live one.What you'll learn:→ What buyers actually mean when they say "let me think about it" in 3 different scenarios→ Why this phrase almost always means you asked the wrong questions upfront→ The deal size caveat — when it's actually okay to hear it→ Gong data: this phrase extends your deal cycle by 173%→ The calendar trick that forces a decision without pressuring the buyer→ The one phone call 99% of reps never make — and why it closes more deals than any script→ Why vulnerability and authenticity beat every objection handling technique→ The "marbles in a jar" trust framework that turns stalled deals into closed ones→ What never to do after you hear it (most reps do this immediately)If you've ever ended a call feeling like you blew it — this episode changes how you handle every deal from here.

SUMMARY: After the first successful AI IPO of 2026, we dig into what makes the Cerebras WSE architecture unique in the market for fast inference. GUEST: Andy Hock, at Chief Strategy Officer at Cerebras AISHOW: 1033SHOW TRANSCRIPT: The Enterprise AI Show #1033 TranscriptSHOW VIDEO: https://youtu.be/ed2nVbOtZiASHOW SPONSORS:OutShift - “Scaling Out Superintelligence”  The Internet of Cognition architectureShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoSHOW NOTES:OpenAI announces 750MW partnership with CerebrasCerebras and AWS partnershipCerebras announces IPOTopic 1 - Welcome to the show. Tell us about your background, and what you focus on today. Topic 2 - For anyone that's not familiar with Cerebras, give us an overview of the company, and especially an overview on the Cerebras technologies (e.g. Wafer-Scale Engine).Topic 3 - Cerebras' WSE architecture is different from many of the GPU or GPU-like architectures in the market today. Centralized vs. distributed architectures always have their tradeoffs. Walk us through the technical and economic value of the Cerebras architecture.Topic 4 - Congratulations on the recent IPO (raised $5.55B). Let's use that as a point in time vs the previous planned IPO. How has the market changed in that timeframe, and how has the Cerebras position changed? Topic 5 - Cerebras (today) offer both WSE hardware, and Cerebras Cloud (API) - very different GTM paths. Can we expect both of those to stay top priorities, or have the market dynamics shifted such that the priorities shift more towards the WSE business - as we're seeing OpenAI, AWS and other engagements announced?Topic 6 - Is Cerebras a training and inference company, or are the economics of inference significantly different enough that it needs to be the sole focus of the company (for now)? Topic 7 - How much effort is it for any company to add support for the Cerebras chips if they have previously been using other architectures?Topic 8 - An IPO is a major milestone for any company, but the markets will now look for your future story. How do you see the AI market evolving over the next 2-5 years, and what are some things that people aren't understanding yet about how it will evolve?FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

After 122 episodes covering SaaS metrics, GTM analytics, and the evolving world of AI-native software, Dave "CAC" Kellogg and Ray "Growth" Rike announce that The Metrics Brothers podcast is going on hiatus.In this abbreviated special episode, Ray and Dave reflect on what made the show work, what made it hard, and why now is the right time to take a pause. They share their top-performing episodes across 122 weeks, including the all-time most-listened episode on NRR, a breakout episode on pipeline generation, and the Intercom AI transformation episode that set a record for first-week downloads.They also explain the primary driver behind the break: AI changed the subject matter faster than their accumulated operator experience could keep up. What started as two veterans trading war stories about metrics they had lived with for decades became something that required more prep, more research, and more time to do with the quality they demanded. Both hosts decided they would rather spend more time inside AI from an operator's perspective to gain real-life AI experience, then return with better stories to tell.Ray shares his plans to accelerate the AI to ROI podcast and newsletter, expand AI benchmarking initiatives with partners including Scale Ventures, and build out advisory services for companies trying to measure and justify AI business impact. Dave will be putting more time into Kellblog, deepening his work with the Balderton Capital portfolio and his advisory clients.The Metrics Brothers consistently ranked in the Top 25-50 on Apple Podcasts in the Business Management category. All 122 episodes remain available in the archive.Listeners with feedback or ideas for what comes next can reach the Metrics Brothers at metricsbros@benchmarkit.ai.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Episode SummaryIn this season premiere of Lessons in Product Management, John Fontenot introduces a new direction for the podcast - one focused on the intersection of artificial intelligence, product management, go-to-market strategy, product-market fit, and organizational alignment.While AI is transforming how products are built and launched, many organizations are discovering that faster execution doesn't automatically lead to better outcomes. In fact, AI often exposes existing weaknesses in strategy, communication, and cross-functional alignment. This episode introduces the concept of the Product-Market Gap: the disconnect between what organizations build, what customers need, and how products are brought to market.John shares why this gap exists, how AI is amplifying it, and why the future belongs to organizations that can align product, marketing, sales, and operations around a common understanding of customer value. Throughout this season, listeners can expect conversations on AI product management, product-market fit, go-to-market execution, category creation, product marketing alignment, product operations, and the evolving role of the modern product leader.Whether you're a product manager, founder, product marketer, GTM leader, or executive navigating the AI era, this season will provide practical lessons and strategic insights for building products, and organizations, that can thrive in a rapidly changing landscape.

Hitting growth targets is often treated as a pure success metric, but missing them can create far wider risks for an organization. This episode reframes growth through a risk lens, exploring how revenue targets connect to confidence, culture, and long‑term performance. James Hargreave, Strategist for Growth and Strategy at Moody's, joins host, Alex Pillow, to unpack why “missing your number” can be the most significant risk in business and how smarter strategy can reduce that risk. Together, they explore how data‑led GTM strategy, clearer targeting, and better enablement help organizations grow more confidently and sustainably. Key topics discussed: Why “missing your number” is a business risk that goes beyond revenue alone The importance of a strong Ideal Customer Profile (ICP) in focusing sales and marketing on winnable deals The role of data quality and connected systems in modern go‑to‑market strategy How CRM ecosystems and AI improve relevance, efficiency, and sales enablement Why optimizing go‑to‑market shifts where and how companies compete and differentiate Additional resources: Moody's Sales and Marketing landing page Growth and Strategy ebook Growth and strategy blogs and case studies Steve Kleinmann and Ian Godfrey LinkedIn profiles Salesforce events Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Everyone is talking about AI replacing marketers.But what if the bigger problem isn't AI at all?In this episode of The Barber's Brief, Marc Binkley and Vassilis Douros explore a series of stories that challenge some of marketing's biggest assumptions.They unpack new research showing that most CMOs aren't worried about AI replacing jobs. They're worried about whether their teams have the skills to use it effectively. The conversation quickly expands into a deeper question: is marketing facing an AI skills gap, or are we simply exposing a fundamentals gap that has existed all along?The discussion also covers:Why only 40% of marketers believe advertising is understood in the C-suiteThe eight barriers preventing organizations from integrating brand and performanceWhat H&R Block learned when its marketing mix model became too slow to be usefulWhy marketers continue to retreat to last-click attribution during moments of uncertaintyThe rise of AI as an "Iron Man suit" that amplifies marketers rather than replaces themPlus, Ad of the Week goes to Brazilian beer brand Brahma for a brilliant World Cup campaign that transforms 24 years of disappointment into hope by reminding Brazilians not what happened, but who they are.This episode is ultimately about one question:Are we optimizing for the dashboard, or are we optimizing for the business?Key TakeawayThree-quarters of CMOs are concerned about the AI skills gap.AI is transforming marketing into a talent transformation.Understanding marketing fundamentals is crucial in the age of AI.The effectiveness say-do gap highlights a disconnect in marketing.Dynamic marketing mix modeling can enhance decision-making.Measurement should build confidence, not just justify spending.Less than half of marketing decisions are evidence-based.AI should be seen as a tool to enhance human capabilities.Brahma's campaign focuses on identity and belief, not just sales.Nostalgia can be a powerful motivator for consumer engagement.Chapters00:00 - Introduction01:12 - The AI Skills Gap in Marketing04:21 - Understanding Marketing Fundamentals07:47 - The Effectiveness Say-Do Gap11:54 - Dynamic Marketing Mix Modelling18:52 - The Future of AI in Marketing24:18 - Ad of the Week: Brahma's World Cup CampaignNews LinksThree-quarters of CMOs are grappling with AI skills gapLink: https://www.marketingweek.com/cmos-grappling-ai-skills-gap/WARC - The Multiplier Playbook for CMO's looking to integrate brand & performanceLink: https://www.warc.com/en/the-multiplier-playbook-2026How H&R Block rethought attribution and modelling – and found more confidence in brand and business outcomesLink: https://www.mi-3.com.au/01-06-2026/when-marketing-mix-modelling-isnt-working-how-hr-block-rethought-attribution-andRobo-dogs, driverless cabs, AI perfume & the GTM singularity: Forrester B2B Summit 2026Link: https://www.thedrum.com/news/robo-dogs-driverless-cabs-ai-perfume-and-the-gtm-singularity-forrester-b2b-summit-2026

¡Ración semanal de tu podcast favorito! Con la participación de: ✔️ Juan Pedro Prat · @JuanpePrat_ ✔️ Ramiro Díez · @Ramisfactions ✔️ Javier Bello · @Javi_B_C ✔️ Dan Puerta al Sótano · @dan_chaos Intro musical de GTM Restart creada por Pitypob · @pitypob2 ✌ Cuña publicitaria cortesía de Ana de Castro · @anadecastronow ⚙️ Edición y Montaje: Javier Bello · @Javi_B_C GTM LINKTREE: https://linktr.ee/gtmediciones ‼️Consigue nuestros libros‼️ · Sombras de Raccoon City https://www.gtm-store.com/product/libro-resident-evil-sombras-raccoon-city-limitada-numerada/ · El Arte de Clair Obscur: Expedition 33 https://www.gtm-store.com/product/libro-arte-clair-obscur-expedition-33-artbook-coleccionista-alicia/ · Almas Oscuras: Berserk contra Dark Souls https://www.gtm-store.com/product/libro-almas-oscuras-berserk-dark-souls-estandar/ · Generación PKMN https://www.gtm-store.com/product/libro-generacion-pkmn-ampliado-hierba-alta/ · El Libro Hueco: Las páginas oscuras del vacío https://www.gtm-store.com/product/el-libro-hueco-las-paginas-oscuras-del-vacio/ Canal de Yugita-chan: https://www.youtube.com/@YugitaChan Canal de Dan: https://www.youtube.com/@Dan-PuertaAlSotano Music promoted by No Copyright: https://bit.ly/33JkJQc Video provided by: warmlightmusic: https://www.youtube.com/@warmlightmusic9137 ================ ACTUALIDAD - La celebración del 40º aniversario de Dragon Quest por parte de Square Enix nos ha dejado bastantes novedades y algo de frío en nuestros corazones. Comentaremos todas las novedades sobre una de las sagas clásicas e IPs más importantes del RPG nipón. - Nuestro querido Juanpe McCloud se ha sumado al equipo Starfox y ha surcado los cielos de Corneria en su Arwing y nos comentará sus sensaciones tras poder probar el próximo exclusivo de Nintendo: StarFox. - Lo posible y lo imposible del NoE3. Con motivo de que ya entramos en la semana "gorda" de los videojuegos, vamos a hacer nuestra porra y también, por qué no, nuestros deseos más locos. DEVTALK - Hoy vuelven unos antiguos amigos de GTM y es que hemos podido charlar con José Manuel Camacho y Marcos Neila de Tessera Studios sobre su nuevo juego: The Zibbo Show. https://www.tesserastudios.com/ ✔️ @tesserastudios ✔️ The Zibbo Show: https://store.steampowered.com/app/972450/The_Zibbo_Show/ RECTA FINAL - Como siempre, cerraremos hablando de los juegos que nos han ocupado esta semana junto con la ración de caspa habitual que tanto os gusta de postre. ================ 0:00 CUÑA PUBLICITARIA 0:51 INTRO 14:20 DRAGON QUEST 40 ANIVERSARIO 47:58 STAR FOX IMPRESIONES 1:11:40 QUINIELAS PRECONFERENCIAS 1:48:13 THE ZIBBO SHOW 2:47:02 RECTA FINAL Y GRUMO #dragonquest #starfox #thezibboshow #tesserastudios #conferencias ================ GTM (Games Tribune Magazine) 2026 @GamesTribune www.gamestribune.com

Marketing leaders are being asked to drive more growth with less budget, fewer resources, tighter timelines, and more pressure from every direction while AI is being treated like the shortcut to replace entire marketing teams. But AI will not fix bad strategy, weak alignment, poor customer understanding, or broken marketing fundamentals. In part two of this master class conversation with Matt Hummel, CMO of Pipeline360, the focus moves into what it really takes to become the kind of CMO AI cannot replace. Not by chasing every new tool, adding more MarTech, or hiding behind automation, but by understanding the business as a whole, building trust across departments, speaking the language of revenue, and creating alignment between marketing, sales, product, leadership, and the customer. To lead marketing in a volatile market where expectations keep rising and the old playbook is no longer enough, you need to know how to: • Make sales an ally instead of your bitter rival • Build shared pipeline ownership across marketing and sales  • Communicate risk without becoming defensive • Connect marketing decisions to the larger goals of the business • Set clearer expectations with your team and leadership • Understand resource constraints without using them as excuses • Stay close to customers while leading strategy • Create momentum without pretending there is an easy button The best marketing leaders are not just managing campaigns, tools, reports, and dashboards. They are translating complexity into strategy the business can trust. The reminder is clear: AI will not fix bad strategy. More MarTech will not fix bad marketing. The CMO AI cannot replace is the one who understands the business, earns trust, aligns with sales, leads the team, knows the customer, and gets back to real marketing when everyone else is hiding behind tools. (P.S. If you haven't, listen to Ep. 149 for part one of this masterclass episode) Beyond The Episode Gems: Connect With Matt Hummel on LinkedIn Listen To Troy On Matt's Podcast, Pipeline Brew: The Evolving Role of CMOs & Community Building Visit Pipeline360 website to learn more about how they solve B2B marketers' biggest headaches Buy Troy's Book, Strategize Up: The Blueprint To Scale Your Business StrategizeUpBook.com Discover All Podcasts On The HubSpot Podcast Network Get Free HubSpot Marketing Tools To Help You Grow Your Business Grow Your Business Faster Using HubSpot's CRM Platform Support The Podcast & Connect With Troy:  Rate & Review iDigress: iDigress.fm/Reviews Follow Troy's Socials @FindTroy: LinkedIn, Instagram, Threads, TikTok Subscribe to Troy's YouTube Channel For Strategy Videos & See Masterclass Episodes Need Growth Strategy, A Keynote Speaker, Or Want To Sponsor The Podcast? Go To FindTroy.com  

Brendan Foody is the Founder and CEO @ Mercor, one of the leading data providers to the largest labs on the planet including OpenAI. In the last two years, Brendan has scaled the company to $1.5BN in ARR and a valuation of $10BN.  AGENDA:  True or False: Mercor lost Meta and OpenAI as a customer with the hack? Mercor has been poaching competitor talent, paying them millions?  Mercor revenue is not real revenue and is only GMV? 12:56 Would Brendan sell Mercor for $30 billion?  14:23 Why everyone is wrong that AI will lead to labor displacement? 15:59 We will create many new jobs that do not exist with AI.  16:59 Why training agents will be a massive labor category that does not exist today  19:51 Will we see the data provider market unbundle and specialize into verticals?  22:24 Is the stated revenue really revenue or is it really GMV?  27:55 How a 1 million ARR company secured one of the best investors in the world with a helicopter ride  29:41 How Felicis secured the deal of the decade with a race track and a set of Ferraris  32:59 Which investment round felt like the highest price to grow into?  34:49 Why will value accrue to the infrastructure layer, not the application layer, in the next 12 months?  35:46 Why the model is the product and why application layer companies should be scared as a result  37:22 Why network effects will be the determinant of value creation  38:46 Why the forward-deployed motion, not the GTM motion, will determine true value creation.  41:59 Why token spend within organizations is going to continue to increase  43:54 Why agent evaluation to commoditize the model layer will be a massive business for enterprises?  51:13 Why we should have increased capital gains tax  01:01:31 How to compete with $20 million a year from Meta?  01:08:49 Will Mercor go public and when?  

Great software companies often come from understanding pain points at a very deep level.On Grit, Aman Narang shares how Toast built trust with 171,000+ restaurant operators by helping restaurants manage everything from payments and online orders to staff scheduling and daily operations.He also reflects on lessons around product-market fit and scaling a company before it's fully ready.Guest: Aman Narang, co-founder and CEO, ToastConnect with Aman NarangLinkedIn: https://www.linkedin.com/in/aman-narang-155628/Connect with ToastLinkedIn: https://www.linkedin.com/company/toast-inc/Instagram: https://www.instagram.com/toasttab/X: https://x.com/ToastTab?lang=enConnect with Joubin:X: https://x.com/JoubinmirLinkedIn: https://www.linkedin.com/in/joubin-mirzadegan-66186854/Email: grit@kleinerperkins.comFollow on LinkedIn:https://www.linkedin.com/company/kpgritFollow on X:https://x.com/KPGrit​Learn more about Kleiner Perkins:https://www.kleinerperkins.com/

Text us your thoughts on the episode or the show!Today, most teams aren't just struggling to build their AI strategies. The real struggle begins when they try to execute their strategies. In this episode of Ops Cast, host Michael Hartmann sits down with David York, Chief AI and Innovation Officer at Helix CXM, to get practical answers about what it really takes for GTM organizations to move from talking about AI to operationalizing it.David has spent years working at the intersection of marketing operations, RevOps, automation, and AI transformation. Together, he and Michael discovered an uncomfortable truth about how most teams are already overwhelmed by manual work, fragmented processes, shadow systems, and operational debt. Piling "figure out AI" on top of all that creates more chaos. In this conversation, you'll hear:Why the gap between AI strategy and implementation is so hard to closeWhat operational excellence actually looks like in practice, and why it has to come firstWhy mapping how work gets done today is the critical first step before introducing AIThe real difference between automation and "automation plus intelligence"How to identify low-risk, high-value AI use cases (like partially manual lead routing) versus harder onesThe hidden costs teams underestimate: tooling, LLM costs, maintenance, and human monitoringWhere human judgment is still absolutely requiredPractical advice on where to start if you're feeling overwhelmed by AI pressure right nowWhether you lead a scrappy SMB or a specialized team inside a large enterprise, this is a grounded discussion about the reality of AI in modern GTM, beyond the hype and the LinkedIn hot takes.David also published a new book this week, AI-Powered Growth: A 7-Step Adoption and Transformation Framework, which goes deeper into how Marketing Ops leaders can systematically prioritize and operationalize AI initiatives. Grab a copy here: https://www.amazon.com/AI-Powered-Growth-7-Step-Adoption-Transformation/dp/B0H2QCZG5M/Enjoy the episode!Episode Brought to You By MO Pros The #1 Community for Marketing Operations Professionals MarketingOps.com is curating the GTM Ops Track at Demand & Expand (May 19-20, San Francisco) - the premier B2B marketing event featuring 600+ practitioners sharing real solutions to real problems. Use code MOPS20 for 20% off tickets, or get 35-50% off as a MarketingOps.com member. Learn more at demandandexpand.com.Support the show

Craig Rosenberg, Chief Platform Officer at Scale Venture Partners and co-founder of Topo, joins AJ Bruno and Asad Zaman to take on the question every founder is wrestling with: can you still build a world-class sales team when OpenAI and Anthropic are handing individual contributors $10 million equity packages? Craig argues you do not have to compete head-on, then lays out the hiring profile to chase instead, the quota-to-comp discipline that keeps packages sane, and why founder brand has become the most reliable pipeline play left as CAC keeps climbing. Topics include enterprise AE compensation, where private equity is still winning the GTM talent war, the Topo playbook for events and data-as-moat, and a bull-versus-bear debate on whether Gong goes public in the next 36 months. Plus, a Quiz Pro Quo on the real customer counts behind Salesforce, HubSpot, and ZoomInfo. Key Takeaways: - Rather than try to outbid OpenAI and Anthropic for talent, build your own farm system and develop people into the role. As Craig Rosenberg, Chief Platform Officer at Scale Venture Partners, put it: "You have to change your hiring profile to a unique profile that's unique to your business, but then you gotta coach 'em up." - A resume from a hot AI lab is not a guarantee of success at your company. As Craig Rosenberg noted, "The person that is going to do well at Anthropic may not do well at Series B," so hire for the stage and the hunger rather than the logo. - On compensation, Craig anchors the package to the role's real value: "you pay for what your wedge costs… if you feel like you have to pay $10 million, then you have a huge problem and you gotta go back to the drawing board." If the number runs away from you, the model is broken. - With CAC climbing and most channels breaking down, founder brand has become the highest-leverage pipeline play. As Craig Rosenberg said, "The value of building a founder brand, when you look at the data, it's amazing," pointing to gains in both pipeline and deal size. Connect with the Hosts & Guests: Host: AJ Bruno, CEO at QuotaPath - https://www.linkedin.com/in/ajbruno3/ Host: Asad Zaman, CEO at Sales Talent Agency - https://www.linkedin.com/in/azaman1/ Guest: Craig Rosenberg, Chief Platform Officer at Scale Venture Partners - https://www.linkedin.com/in/craigrosenberg/ Topline is more than a YouTube Channel: Subscribe to Topline Newsletter: https://toplinemedia.substack.com/ Tune into Topline Podcast, the #1 podcast for founders, operators, and investors in B2B tech: https://www.joinpavilion.com/topline-podcast Join the free Topline Slack channel to connect with 600+ revenue leaders to keep the conversation going beyond the podcast: https://www.joinpavilion.com/topline-slack Chapters: 00:00 Introducing Craig Rosenberg 02:34 Can Anyone Out-Hire The AI Labs? 04:33 Why Craig Isn't Worried 06:52 Enterprise AE Comp Is Climbing 08:21 Founders Overpay For Star CROs 10:53 Why AI Reps Struggle At Series B 14:00 Hire The Slighted CRO 14:42 Quota-To-Comp And Attainment 18:45 Can AI Labs Sustain Growth? 22:20 Where PE Still Wins GTM Talent 27:17 Major Runs Reshape GTM 32:36 The Topo GTM Playbook 37:55 Quiz Pro Quo 47:45 Founder Brand And Rising CAC 58:42 Bulls and Bears

Marketing leadership has become one of the most volatile seats in business. CMOs and marketing leaders are often expected to create immediate pipeline, prove instant ROI, fix deeper business issues they did not create, defend brand investment, align sales, understand customers, translate strategy across the organization, and still become one of the first functions questioned, blamed, or cut when growth slows. In part one of this master class conversation, Matt Hummel, CMO of Pipeline360, brings a clear reminder back to the table: great marketing starts with trusting the buyer, knowing the customer, and simplifying how you market. In a market obsessed with performance data, attribution, automation, dark social, buyer signals, and immediate results, more complexity does not automatically create better customer understanding. For aspiring CMOs, current CMOs, marketing leaders, founders, and business owners, this conversation is a valuable look at how to lead marketing without getting trapped in the pressure cooker. It challenges you to rethink what it really means to put the customer at the center, not as a tagline, not as another automation workflow, and not as another dashboard filled with signals, but as a deeper responsibility to understand the person, pressure, timing, risk, and decision behind the purchase. The conversation moves through buyer trust, brand versus demand, customer empathy, attribution, sales alignment, CMO pressure, market timing, and the difference between chasing pipeline and building LTV. It is also a reminder to get out of your lane, understand product, spend time with sales, listen to customers, and learn how the whole business works. Because the best CMOs are not just campaign operators. They are translators, mediators, trust builders, and business leaders who know how to connect marketing to revenue, customer experience, and long term growth. Beyond The Episode Gems: Connect With Matt Hummel on LinkedIn Listen To Troy On Matt's Podcast, Pipeline Brew: The Evolving Role of CMOs & Community Building Visit Pipeline360 website to learn more about how they solve B2B marketers' biggest headaches Buy Troy's Book, Strategize Up: The Blueprint To Scale Your Business StrategizeUpBook.com Discover All Podcasts On The HubSpot Podcast Network Get Free HubSpot Marketing Tools To Help You Grow Your Business Grow Your Business Faster Using HubSpot's CRM Platform Support The Podcast & Connect With Troy:  Rate & Review iDigress: iDigress.fm/Reviews Follow Troy's Socials @FindTroy: LinkedIn, Instagram, Threads, TikTok Subscribe to Troy's YouTube Channel For Strategy Videos & See Masterclass Episodes Need Growth Strategy, A Keynote Speaker, Or Want To Sponsor The Podcast? Go To FindTroy.com  

You can't be a great marketer if your nervous system is stuck in survival mode.If you're a marketing leader, you know the feeling. The 12AM Slack from the CEO. The “where's-the-pipeline?” question that never goes away. The low-grade anxiety running underneath every campaign, every board deck, every quarter. In this episode, Carolyn and Amber connect the inner game to the measurement problem. The anxiety marketers carry isn't a personal failing. It's the tax you pay for being judged by numbers that miss your real contribution: shaping how the market perceives you, long before anyone fills out a form.And here's what nobody in GTM is actually talking about: you cannot build the future you want while your body is locked in defending the present. Your brain chemistry doesn't know the difference between the meeting you're dreading and the one that's already over — it reacts the same way to both. And when you live in that state, you can't create. You can only react.What this episode covers:The breakthroughs from both Amber and Carolyn's recent vacationsWhy so many B2B marketers are operating from low-grade panic, and why it's costing them their best workHow belief and brain chemistry shape what you're able to create, and why you have to embody the outcome before it shows upWhy marketing's real job is shaping brand perception in-market, and why traditional KPIs can't see that workHow to use zero-party data — what customers tell you directly — to inform the customer journey instead of guessing from last-touch behaviorThree books that reshaped how Carolyn thinks about wealth, awareness, and building a future you can't yet see: Happy Pocket Full of Money, The Power of Awareness, and Dr. Joe Dispenza's Becoming SupernaturalIf you're a marketing leader tired of doing your best work from a place of panic, and tired of watching it disappear into numbers that can't measure it, this one's for you.-----------------------------------------------------Want answers now?

Howdy y'all! We're back at analysis ranch where our final Patreon farmhand has offered up their darling for the show: Hannah Montana: The Movie. It will be your pleasure to hear three middle aged men discuss this Disney Channel Icon. We give the dual roled heroine our best though as we tease out ideas of Lynchian proportion, while looking at the child star industry, the return to simpler times, and much, much more. So, grab your favorite disguise, jump on the jet plane, and listen to us analyze Hannah Montana: The Movie. Thank you, thank you, thank you to our Patreon supporters for making this marathon happen. For information on Patreon, go to Patreon.com/GTM. 

Send us Fan MailIn this episode of the B2B Go-To-Market Leaders Podcast, Vijay Damojipurapu sits down with PV Bóccasam, advisor to private equity firms and veteran operator across enterprise software, venture-backed startups, and category-defining companies, to explore a radically different way of thinking about go-to-market.PV argues that go-to-market is not about sales motions, pipeline generation, or even positioning frameworks—it's about one thing: reducing buyer anxiety and lowering the perceived risk of change.Drawing from decades of experience building and scaling enterprise software companies across identity governance, GRC, enterprise risk management, and private equity-backed transformations, PV shares how the best GTM leaders think less about “selling” and more about helping customers justify, adopt, and communicate measurable value internally.They dive into:Why GTM should focus on reducing customer risk, not maximizing seller activity.The difference between customer convictions and customer incentives—and why both matter.Why measurable proof is the only reliable way to break buyer inertia.How enterprise software companies should rethink value delivery in the AI era.Why AI should reduce operational uncertainty—not create more chaos.The evolution from product-led to sales-led to partner-led GTM motions.Why “platform” messaging fails for most enterprise SaaS companies.How modern AI-native SaaS products are becoming systems of orchestration, not systems of record.The importance of helping customers retell your value proposition internally.Why enterprise GTM leaders must become the clearest thinkers during periods of uncertainty.How private equity firms should approach AI adoption through organizational redesign, not just cost-cutting.And why long-term impact matters more than short-term velocity in building a career and a company.PV's central insight is simple but powerful:Customers don't buy software—they buy reduced uncertainty, measurable outcomes, and confidence in the future state.This episode is a deep philosophical and operational masterclass on enterprise go-to-market strategy, AI adoption, organizational design, and what it truly means to build trust at scale.Connect with Vijay Damojipurapu on LinkedInConnect with PV Boccasam on LinkedInBrought to you by: stratyve.com

On this episode, Pete and Julie unpack the top 10 learnings and key takeaways from the recent Workday Innovation Summit for industry analysts in Napa, California.  Each year, Workday gathers analysts for a peek behind the Workday curtain, its GTM, roadmap, customer stories, and big bets on what comes next for the ERP platform and its innovation, its customers, partners, and broader ecosystem.  Connect with the show: LinkedIn:  http://linkedin.com/company/hr-payroll-2-0 X: @HRPayroll2_0  X: @PeteTiliakos  X: @JulieFer_HR BlueSky: https://bsky.app/profile/hrpayroll2o.bsky.social  YouTube: https://www.youtube.com/@HRPAYROLL2_0    WRKDefined Podcast Network: https://wrkdefined.com/podcast/hr-payroll-20  Thank you to our marquee sponsors for powering the HR & Payroll 2.0 podcast forward!  G-P ‘Globalization Partners': https://www.globalization-partners.com/ OneSource Virtual: https://hubs.ly/Q03YFNR90 Zoho: https://www.zoho.com/press.html Thank you to our ‘wizard behind the curtain' and show producer Ryan Kielma: https://www.linkedin.com/in/ryan-kielma/

80% of deals require at least 5 follow ups. 44% of salespeople quit after the first attempt.That gap is where your pipeline is bleeding — and most reps don't even know it's happening.In this episode, Dave and Regan break down the follow up epidemic, why sequences die after touch two, and the exact multi-channel framework that closes deals other reps have already abandoned.What you'll learn:→ Why following up once already puts you ahead of 44% of your competitors→ The 18-touch multi-channel sequence that gets responses (email + LinkedIn + call + SMS)→ Why the "7-minute deal" in your CRM is actually a 12-month relationship in disguise→ How to build a follow up cadence that doesn't feel like a template to the buyer→ The two parallel tracks top sellers run — outreach cadence AND relationship building→ How to follow up on a live deal without creating scarcity or desperation→ The jab jab hook follow up ratio that mirrors how social media serves content→ How to write a breakup email that actually keeps the door open→ Why a weak pipeline is the real reason reps push deals too hard too fastIf you're an AE or BDR and your follow ups are going silent after touch two or three — this conversation is for you.

How does a growth operator who built and managed large teams at Twitch and Discord work today as a solo fractional CMO?In this episode of Intelligent Artifice, Shamanth Rao sits down with Justin Gerrard, fractional CMO and former growth lead at Twitch and Discord, to talk about how he uses Claude day to day to plan his week, build GTM plans, and deliver work for multiple startups at once. Justin shares where AI genuinely saves time, where human judgment is still the difference maker, and why letting AI run on autopilot is a trap most teams fall into.If you work in growth, GTM, or performance marketing, this is a practical and honest look at what working with AI actually looks like in 2026.Chapters:00:00 Intro: Justin's background at Twitch and Discord01:21 Building performance marketing at Twitch03:22 AI Speed: Weeks of work vs. a few hours05:54 AI as an Operating System06:33 Justin's Monday Morning AI routine08:05 The 60/70 Rule for AI productivity09:12 Why 100% AI work is a hiring red flag10:11 Case Study: A localized GTM plan14:52 Creative Strategy: Automated UGC at scale16:30 The Trap of Autonomy in AI content18:13 Brand Safety: The 18 Curses story19:15 The Librarian vs. The Author21:35 Human Creators vs. AI Avatars A/B Test24:55 Where to find Justin onlineKey things we cover in this episode:✅ How Justin structures his Monday morning using Claude Desktop and MCP connectors✅ Why he caps AI contribution at 60 to 70% on every GTM plan✅ Why AI is a fast librarian but not an author✅ How he A/B tested AI creators vs human creators in a paid ad campaign and what the results showed✅ Why fully automating content output does not drive real results✅ What experienced operators still bring to the table that AI cannot replicate

Owner.com is approaching $100M ARR selling to independent restaurants and their GTM team is producing numbers that shouldn't be possible. $150K AEs closing $2M+ ARR per year. Outbound BDRs generating $100K in closed-won ARR per BDR per month. 4X the ARR per rep compared to direct competitors. None of that happens by accident.  In this session, Kyle Norton, CRO at Owner.com, breaks down the exact AI-driven GTM playbook that got them there, including 5 decisions he believes every SaaS company needs to make right now before the gap between AI-native and AI-curious companies becomes impossible to close. What you'll learn: 1. Centralized vs. decentralized AI: why letting a thousand flowers bloom is probably killing your results 2. Build vs. buy: the 5-question framework (hint: buy your infrastructure, build your intelligence) 3. The AI sophistication ladder — Levels 0 through 4, where most companies are stuck, and exactly how to move up 4. The "5 P" prioritization framework for deciding which AI projects to tackle first 5. Agentic vs. assistive: how to think about human-in-the-loop and why chaining too many generative steps is the #1 cause of AI slop 6. Why your personal compounding AI stack is your most underrated competitive asset This isn't theory. This is what $100M ARR in a notoriously difficult SMB market actually looks like when you go all-in on applied AI.

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

Wade Foster, CEO & Co-Founder of Zapier, reveals how a $5B company transformed overnight when GPT-4 launched. Learn his "Code Red" strategy, AI adoption secrets, and why the future of software belongs to AI agents. When GPT-4 launched, Wade Foster didn't wait. He called a code red. In this conversation, he shares exactly how Zapier went from 10% to 97% company-wide AI adoption in months, why he focuses on building "AI fluency" over quick wins, and what he believes every CEO needs to know about the AI-driven future. We cover: - The "Code Red" moment: Why and how Zapier decided to transform everything - From 10% to 97%: The real drivers behind achieving company-wide AI adoption - The AI Fluency Rubric: Building institutional AI capability vs. tactical AI use - The future of software: Why agents (not humans) will write and use software - The 3% that didn't adopt: Why some teams resist and how to bring them along - Advice for late-moving CEOs: What to do if you haven't started yet - Buzzwords Wade would ban from meetings - Why "context" and "agent" are overloaded terms in AI discussions TIMESTAMPS: 00:00 - Intro 02:08 - How calling Code Red transformed the company 03:02 - The immediate impact: From 10% to 50% AI adoption 05:09 - Building the AI Fluency Rubric and institutional change 07:18 - How Zapier achieved 97% adoption (and what about the 3%?) 09:08 - The difference between adoption vs. sustained usage 12:37 - Making employees retry AI tools even when first attempts failed 15:59 - Infrastructure for embedding AI into workflows 19:25 - The future of software: Agents as creators and users 31:58 - What will Zapier look like in 3 years? Agent-native platforms 34:04 - Setting up governance for AI agents to write code safely 39:17 - What beliefs about AI building today won't make sense by 2028? 42:02 - Buzzwords Wade wants to ban from AI conversations 43:18 - Final advice for CEOs who feel they're late to AI KEY INSIGHTS: - The quality gap between GPT-3.5 and GPT-4 was so vast it rewritten everything for Zapier - A company-wide hackathon forced everyone to play with AI (80%+ participation was key) - Building the muscle for AI adoption matters even when the initial output isn't directly used - Entrenched workflows and quality concerns are the biggest blockers to adoption - Support and encouragement to "try again in 3 months" is critical - The shift isn't just more software, it's that agents will write that software - Governance and guardrails for agents are the new infrastructure CEOs need to set up - AI maximalism paired with realistic change management is the winning approach PERFECT FOR: - Product leaders and GTM professionals navigating AI transformation - CEOs and C-suite executives feeling pressure to move faster on AI - Engineering leaders building AI-fluent organizations - SaaS founders and entrepreneurs building AI-native products - Anyone curious about how category-defining companies adapt Wade Foster brings the strategic clarity and operational rigor that made Zapier the automation standard. This episode cuts through the AI hype to the real work of transforming organizations. #WadeFoster #Zapier #AI #CEOAdvice #AIAdoption #ArtificialIntelligence #SoftwareDevelopment #ProductStrategy #Automation #AIAgents #Leadership #GPT4 #Technology #BusinessStrategy #GTM ABOUT WADE FOSTER: Wade is the Co-Founder and CEO of Zapier, the #1 automation platform connecting 7000+ apps. He's been building Zapier since 2011 and has grown it into a $5 billion company used by millions of businesses to automate daily workflows. Wade is deeply focused on AI's impact on software development and organizational productivity. DISCLAIMER: This conversation reflects Wade Foster's views and experiences. Viewers should consult their own experts before making strategic or business decisions.

Andy Mowat has run RevOps at four unicorns, and between every role he's gone out and built something new. His latest is Whispered, an AI platform that helps top GTM execs build their careers through shared insights, warm introductions, and access to the roles that never get posted.In this episode, Andy and Justin get into why the entire GTM tech stack is heading for a two-year rebuild, what the move to a data-warehouse-first model means for RevOps teams, and why smaller companies are suddenly more nimble than the enterprise. Andy shares hard-won lessons from scaling Culture Amp, including the speed-to-lead system he built around time-critical leads and the market-maturity question he buried in the win-loss forms. They also dig into whether the fractional exec wave is real, why thoughtful gifting and distinctive events still beat automated outbound, and what it actually feels like to run a proactive job search at the VP level for the first time.A candid conversation between two friends about category strategy, positioning, and building a career in go-to-market when the ground keeps shifting.Buzzsprout Chapter Markers00:00 — Intro: how Justin and Andy met around Culture Amp 01:40 — Andy's background: RevOps at four unicorns, and why he keeps building 03:30 — Founders as a hiring profile, and the trap of "ambiguous roles" 05:30 — Rebuilding the GTM tech stack: data warehouse first, the semantic layer, and two years of turmoil 07:00 — The give-to-get ratio and why AI-era outbound still comes down to better emails 08:30 — Strategic gifting, distinctive events, and the Wisconsin business school cold open 11:00 — Podcasts as relationship engines, not just content 11:30 — Inside Culture Amp: 180 events a year and the speed-to-lead system 13:00 — The multi-prospect demo experiment, and the market-maturity question that predicts a category's breakout14:45 — The fractional exec wave: real shift or euphemism for "between jobs"16:15 — Why early-stage marketing needs people to "grok what you do" before top of funnel 18:00 — What's next for Whispered: the network, the community, and the roles that get whispered

In the latest episode of Executive Function, Brett sits down with Graham Moreno, Head of GTM at Parallel Web Systems. Before Parallel, Graham scaled Windsurf's GTM organization from three sellers to seventy-five in under a year, served as President through the Cognition acquisition, and earlier built and led enterprise sales teams at Grafana Labs and MongoDB. In this conversation, he unpacks why the AI-era backlash against structured enterprise sales misreads the data, how to design a process that raises the floor for ordinary reps without capping the ceiling for stars, and why selling to AI-native customers compresses an eight-week cycle into five business days. In today's episode, we discuss: Why in-person enterprise rollouts still beat product-led motions Building a robust sales process that still leaves room for unscripted moments Why the three highest-leverage early sales hires aren't sellers at all The case for outsized commission accelerators for star sellers — and the kind of person they attract Why most AI companies are skipping the in-person sales work that enterprise customers actually want References: Ahead: https://www.ahead.com Amazon: https://www.amazon.com Anthropic: https://www.anthropic.com Attio: https://www.attio.com Augment Code: https://www.augmentcode.com/ Cognition: https://cognition.ai Cursor: https://cursor.com Dani McCabe: https://www.linkedin.com/in/danielle-mccabe/ Datadog: https://www.datadoghq.com GitHub Copilot: https://github.com/features/copilot HubSpot: https://www.hubspot.com Jeremy Powers: https://www.linkedin.com/in/jeremypowers/ JPMorgan: https://www.jpmorgan.com Matt McClernan: https://www.linkedin.com/in/mattmcclernan/ MongoDB: https://www.mongodb.com Nicole Rettinger: https://www.linkedin.com/in/nicole-rettinger-23b20465/ Notion: https://www.notion.com OpenAI: https://openai.com Parag Agrawal: https://www.linkedin.com/in/paragagr/ Parallel: https://parallel.ai Snowflake: https://www.snowflake.com University of Chicago: https://www.uchicago.edu Windsurf: https://windsurf.com Where to find Graham: LinkedIn: https://www.linkedin.com/in/grahammoreno/ Where to find Brett: LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Twitter/X: https://twitter.com/brettberson Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast Timestamps: 00:00 Introduction 00:32 Has the sales playbook changed in the AI era? 02:13 Why "showing up" beats letting the marketplace decide 06:50 Why great salespeople sell to engineers and executives in one motion 11:37 Selling to AI-native buyers who grew up on ChatGPT 13:49 Same seller, different tempo: 8 weeks vs. 8 business days 15:57 How AI-native buyers handle build vs. buy decisions 17:48 The rep who taught a champion's son guitar over Zoom 19:03 Raising the floor without capping the ceiling 22:09 Why too much process narrows the kind of seller you attract 25:46 The three pillars of GTM excellence 31:00 Building peers who are 80% aligned, not 100% 38:03 Whether AI is changing what good enablement looks like 41:35 Selling against direct and implied competitors at once 42:45 Instrumenting the funnel from stage zero to close 45:57 Why post-sales should always roll up to the revenue leader 48:19 The case for outsized commissions 52:02 The 96 hours of panic before Cognition acquired Windsurf 53:04 How far out should a GTM leader be planning? 57:53 What a normal week looks like in hypergrowth

Yahtze-wait! Wrong game! Hello listeners! Grab your dice and player mats because our darling kill-a-thon continues with a pick from Michael! Our day one ride or die is checking in with a classic from his youth, Jumanji. We're going board, not video, games here as we look back at the Robin Williams vehicle. Arthur finally, finally, gets to nerd out about board games and conversations of representation, while we also talk guns in media, imperialism, and much, much more! The pieces are moving us closer to the analysis table as we discuss Jumanji! Tune in now! Thank you again to our Patreon supporters, if you would like to know more head over to Patreon.com/GTM. 

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!On the product side, everyone is getting Computer - Perplexity, Manus, Cursor, and so on. Meanwhile on the research side, agentic evals like TerminalBench and GDPVal are also assuming computer (Harbor). On both ends, the consolidating LLM OS stack has become a standard toolkit, and Daytona is one of a small set of AI Infra companies that are booming because of it.“The end of localhost” has been Ivan Burazin's obsession for more than a decade.Something that is all too familiar…Long before agents became the default way people talked about software development, Ivan was already chasing the idea that development should not depend on a fragile local machine. CodeAnywhere, one of the first browser-based IDEs, was an early attempt at that future: move the development environment into the cloud, make setup reproducible, and free developers from the endless “works on my machine” tax.The thesis was directionally right, but the market wasn't ready yet.However, agents changed that. They do not care about a laptop, desk setup, or favorite editor. They need a computer they can access through an API: something stateful enough to keep working, fast enough to spin up instantly, flexible enough to resize, isolated enough to be safe, and composable enough to run the messy real-world workflows that real software engineering actually requires.Daytona isn't just selling “sandboxes” in the narrow code-execution sense. It is the latest version of Ivan's original localhost thesis.In this episode, Daytona's CEO joins swyx to explain why AI agents need more than code execution boxes: they need composable computers, stateful sandboxes, instant startup, dynamic resources, and infrastructure that can survive workloads going from zero to 100,000 CPUs.We go deep on the new agent compute market: Daytona's hard pivot from human dev environments to AI sandboxes, the New Year's Eve MVP that customers begged for, why Daytona runs on bare metal with its own scheduler, how one customer runs almost 850,000 sandboxes a day, and why RL/eval workloads went from 0% to roughly 50% of usage in just months. Ivan also explains why agents need Windows and macOS machines, why CLI may matter more than MCP, why Kubernetes is painful for this workload, and why the future AI cloud may look more like Stripe than AWS.We discuss:* How Daytona grew out of CodeAnywhere, Shift, and the “end of localhost” thesis* Why Daytona pivoted from human dev environments to AI sandboxes* Why agents need composable computers instead of disposable code execution boxes* The New Year's Eve MVP that customers chased API keys for* Why Daytona chose bare metal, stateful snapshots, and its own scheduler* How Daytona spins up one sandbox in ~60ms and 50,000 sandboxes in ~75 seconds* Why Daytona's biggest customer runs ~850,000 sandboxes a day* How RL/eval workloads create zero-to-100,000 CPU spikes* Why RL workloads went from 0% to roughly 50% of Daytona usage* Why customers compare Daytona against EKS/GKS and say they're “never going back”* Why every AI agent may need a computer, including Windows and macOS environments* The Apple licensing constraints that make macOS sandboxes hard* Why CLI gives agents more power than MCP* How open source helps agents integrate Daytona* Why agent-generated PRs may break today's CI/CD assumptions* Why AI SaaS companies reselling tokens may face a cold shower* Why the AI cloud may look more like Stripe than AWSIvan Burazin* LinkedIn: https://www.linkedin.com/in/ivanburazin* X: https://x.com/ivanburazinDaytona* Website: https://www.daytona.io* X: https://x.com/daytonaioTimestamps* 00:00:00 Hook* 00:01:12 Introduction* 00:03:15 CodeAnywhere, Shift, and the end of localhost* 00:05:58 What Daytona is: composable computers for AI agents* 00:08:07 The pivot from dev environments to AI sandboxes* 00:10:17 The New Year's Eve MVP and customers begging for API keys* 00:12:56 Bare metal, stateful sandboxes, and Daytona's scheduler* 00:17:28 60ms startup, 50,000 sandboxes, and 850K daily runs* 00:21:53 Spiky RL/eval workloads and the new agent infra problem* 00:28:12 RL workloads, Kubernetes pain, and dynamic resizing* 00:33:31 Why every AI agent needs a computer* 00:38:48 macOS sandboxes and Apple's licensing problem* 00:44:28 Why CLI may matter more than MCP* 00:48:11 Open source, GitHub stars, and agent integration* 00:53:11 Git, CI/CD, and agent collaboration bottlenecks* 00:58:15 Founder life and building a 25-person infra company* 01:02:44 AI SaaS, token resale, and API-first business models* 01:06:10 GPU sandboxes, data centers, and compute growth* 01:09:48 Why the AI cloud may look more like Stripe than AWS* 01:11:26 Closing thoughtsTranscriptIntroduction: Daytona, CodeAnywhere, and the End of LocalhostSwyx [00:00:02]: Okay, we're in the studio with Ivan Burazin, CEO of Daytona. Welcome.Ivan [00:00:07]: Thanks for having me, man.Swyx [00:00:08]: Ivan, you and I go back.Ivan [00:00:10]: Way back.Swyx [00:00:11]: How I don't even know how, you found, did you reach out or, for Shift.Ivan [00:00:17]: I reached out to you. The reason was you - we were just - we were thinking about I was one of the co-founders of CodeAnywhere, the first browser-based IDE, and so we were thinking a long time of, localhost should die. And you had this article.Swyx [00:00:29]: End of localhost.Ivan [00:00:30]: Then I reached out to you because of that, and then we talked, and I was actually at a different job and learning about I was the head of, developer experience, and you were quite well-versed in that, and I actually reached out to you, among other people, how do we go about that? What are the key things and whatnot at this point in time? And you were nice enough to take the call, and I remember I was late on your call with you.Swyx [00:00:51]: I don't remember.Ivan [00:00:52]: I remember because I was with my then I'm thinking of a girlfriend or wife at that point in time, I'm not sure. It's the same person, so that's great, and I was late ‘cause we were, in, Italy on, vacation, and then I was late for something. I felt so bad, and you were so nice to be, good about.Swyx [00:01:10]: The reason I'm nice is because I'm also late to other people, so it's like, who's, who's without sin here, yeah, so I have to, for those who don't know, InfoBip Shift, there's this whole thing that, you did in the past, and, and that was basically one of the inspirations for me starting AI Engineer, which is like, I have to thank you for giving me that push to be like, “Oh, you can, you can build and sell conferences?”Ivan [00:01:34]: I remember you asked you asked me at the beginning to give me advisory shares, and I was so focused on what we were doing, I said no, and I should've took the advisory shares. So I'm sorry, dude. But anyway.Swyx [00:01:43]: We're not, we're not venture backed.Ivan [00:01:44]: No, it doesn't matter.Swyx [00:01:45]: It's Yeah, anyway, so I think what's impressive about you is that CodeAnywhere is the thing that you've been trying to build, and, you kind of put it on hold and then came back after InfoBip. Just give us the story, do you - the story and the origin story, going into Daytona.From CodeAnywhere and Shift to DaytonaIvan [00:02:05]: Sure. Like, really way back, me and my co-founder have been together. I say this, I've said this multiple times, it's like we were married and divorced and married. Some people actually ask me is my co-founder my partner. they thought it literally. It's not literally, but we have done multiple companies together, and to your point, we had this shift where we went from the CodeAnywhere to the conference called Shift, and then back to, Daytona. We originally started stacking servers, doing like virtualization in the early 2000s and, routers and doing basically all these things, at a foundational level, and that was a services company which we sold to focus on what my co-founder actually invented, which was the very first browser-based IDE, right, I say the first. Before us was actually Heroku. They did it for a very short time until they became Heroku. But outside of them, we were the only one, and it was called.Swyx [00:02:55]: There was Cloud9.Ivan [00:02:57]: Cloud9 came out slightly after us. There was Replit, which came out when we stopped doing it, Replit came out, and they have been successful since then, which is great. There was Nitrous.io. There was quite a few that existed at the time, but it was like too early. But the interesting part is that we, at that point in time, because there was no VS Code, there was no Kubernetes, and Docker had just started when we Or I'm not sure if it was even public at that point in time. And so we had to build everything to the whole stack ourselves and that was the key learning that we brought into and that we've been using in Daytona today. So it was super early. There's about 3 million people used CodeAnywhere. It was slightly, it was angel-backed more than venture-backed. We ended up paying everyone back because it didn't have that sort of scale. But, three years ago, we started something similar with Daytona, which is not what we are today, but it was automating dev environments for human engineers, the basically the underlying stack of CodeAnywhere. And then we did a hard pivot last January to sandboxes. And so here we are.Swyx [00:04:01]: Historic pivot, yeah, and, it's one of those things where, I had independently invested in CodeAnywhere, but also in E2B, and then both of you pivoted into the same thing, and I'm like, “F**k.”Ivan [00:04:12]: You invested, you invested in Daytona. You invested in Daytona. But you were the first If we had not got your check, we wouldn't have done it.Swyx [00:04:18]: No way.Ivan [00:04:19]: No, it was like, “We have to get him on board first,” and you were that kicker that we, that got us off the ground.Swyx [00:04:23]: No, because you were putting me on your pitch deck, man. I was like, “Man, this is like a good trip if I don't invest.”Ivan [00:04:29]: That's because it was your quote. It's like we.Swyx [00:04:30]: Yeah. It's the end of localhost.Ivan [00:04:31]: Did a bunch of research about end of localhost and who was interested in that,.Swyx [00:04:34]: No, that's like, I put, I wrote that blog post, and every single company in that field reached out to me, and then every VC who was receiving those pitches then also had to call me and, talk it, talk through it with me.Ivan [00:04:47]: It's finally happening though.Swyx [00:04:48]: It was really super interesting.Ivan [00:04:48]: It's finally happening.Swyx [00:04:49]: It's finally happening.Ivan [00:04:49]: Yeah, it's finally.Swyx [00:04:49]: It's finally happening, with maybe sort of non-human users. Yeah, so what is Daytona today? Let's get like a quick description. I'm wearing the shirt.What Daytona Is Today: Composable Computers for AI AgentsIvan [00:04:58]: You're wearing the shirt. Yes,.Swyx [00:04:59]: It says, I think your branding is very good. Like, it's very consistent. It runs AI code. Like, it cannot be simpler.Ivan [00:05:05]: Exactly, but we're gonna probably have to change that.Swyx [00:05:07]: Oh, s**t.Ivan [00:05:07]: It's also a subset of what we do. Unfortunately, we really love this, Run AI Code is super simple. People interpret it different ways. I think we've given out 5,000, 6,000 of these shirts. People wear them with pride because it doesn't really market about us.Swyx [00:05:21]: Yeah, Daytona's on the back.Ivan [00:05:22]: It markets the back. It markets to the person itself, so I think we did a really good job on that one. But it is also a subset of what we do, because people, when they think about Run AI Code, they just think about these small, let's call it isolates, code execution boxes that, you send some code, you get an output. Whereas what Daytona is today is essentially composable computers for AI agents. It is, the market calls them sandboxes which can be misleading.Swyx [00:05:44]: All these things. All these things on.Ivan [00:05:45]: Yeah, exactly, ‘cause it can be misleading ‘cause people usually think about sandboxes as a demo or a test environment versus a production-grade environment. But what Daytona does, if you think of the laptop that you have in front of you or the computer that's over there, or, my wife is an architect, so she has like a Windows with a 3D graphics card inside to do 3D rendering. Like, as humans, we have different computers or different compositions of computers. And our belief is strongly that agents today and going forward will need all these different compositions of computers to do different types of tasks. And so we offer that basically through an API.Swyx [00:06:19]: Yeah, to give people - I'm trying to sort of front-load all the aha moments or the wow moments so that people can, stay engaged and click like and subscribe. the market is exploding, right? Like, you have been reporting 74% month-on-month growth, and it also, it's just been growing for a while. Like, it's been going like this. And every single - It's not just you guys. It's every single.Ivan [00:06:41]: Everyone, yeah.Swyx [00:06:42]: Sort of, compute provider. I don't know if you agree with me saying compute provider or not.Ivan [00:06:48]: It's fine.Swyx [00:06:48]: Yeah. So like organically PLG-driven growth, but also enterprise is doing super well, I think I wanna rewind to January of last year when you did the pivot. Like, so you obviously called this market early, and you were positioned for it, and you are now one of the market leaders. But what was the insight that made you do the pivot?The Pivot: From Human Dev Environments to Agent SandboxesIvan [00:07:06]: The insight that made us do this pivot is the quarter before that, so end of 2024, when we had - Basically, we did a demo with - I don't I think we discussed this as well, Devin was not public. You actually gave me access to Devin at that time. So Devin.Swyx [00:07:25]: I did?Ivan [00:07:26]: Yeah, you gave me access.Swyx [00:07:26]: I don't think I was supposed.Ivan [00:07:27]: Yeah, exactly.Swyx [00:07:28]: Yeah, I.Ivan [00:07:28]: So it doesn't matter. You.Swyx [00:07:29]: Yeah. I gave like three friends access.Ivan [00:07:31]: Yeah, or it was a call and you showed it to me. It doesn't matter. but OpenDevin was available, which is now called OpenHands. And so we're like, “Oh, this seems to be a thing. This is not public. Let's take our for human automation of dev environments and take, OpenDevin and launch that as a SaaS.” And we did that. Not very many people signed up and used it, but a lot of people reached out that were building agents, and they were like, “Hey, my agent needs a compute sandbox runtime,” whatever you wanna call it. I forgot what it was called at that point. And then we were like, “Oh, amazing. This is a new market. Here is our infrastructure. Here's our product, and go.” And what we found really fast, soon, was that people did not like what we had built. It didn't work. And I remember talking to people at the beginning when we're doing this, the sandbox we're building for agents. People were like, “Oh, why is it different? It's the same thing. We have like EC2, we have VMs, we have all these things.” But we saw that everyone we gave it to, it was like 20, 30 people, they all said, “No.” Like, “This is not what we need. This sort of breaks.” And basically, me and my co-founder not knowing a lot about - ‘cause we're infra people. We're not AI people. So I basically took it upon myself to like watch every single podcast that exists, including all of, all of these and all that, and sort of get up to date, read all the blogs, like get, understand what's going on.Swyx [00:08:45]: Do you wanna shout out who else was useful, just in case people are also looking.Ivan [00:08:49]: Generally we -, I looked at There's a few of podcast, different segments and different types. So there's you guys, No Priors, Bill Gurley's was great while.Swyx [00:09:04]: VG2, yeah.Ivan [00:09:05]: Yeah, while it was around. So there's a few. 20VC is interesting from a different dynamic, and some are different dynamic. But there was, also Red Points.Swyx [00:09:14]: We're not really about the compute market.Ivan [00:09:15]: It was also already - Sorry?Swyx [00:09:16]: You're, you want - You're looking at the agent infra market.Ivan [00:09:19]: I was looking at the agent market and the AI market in general and sort of understanding who are the players, what the perception, and how that goes. And like obviously you complement this with like going to conferences, going to events, going to meetups, reading white papers, like doing all the things that you have to do to understand what's happening. And so when we figured, when we sort of had an idea of what we had to build, literally over the New Year's Eve, literally on New Year's Eve, I half vibe coded the first MVP, first minimal viable product of what Daytona is today. And I went to sleep at like 3:00 AM or something like that. I was doing - I just put my like baby daughter and wife to sleep and, Happy New Year's, and go back to just, doing this. And I sent it to my co-founder, my CTO, and he saw it in the morning. He's like, “This is absolute garbage.” “Do not show this to anybody at all, but the idea is good.” And so he took two weeks, and he rebuilt it.Swyx [00:10:09]: Did it like look like that? Listen, I - It was rough idea.Ivan [00:10:12]: Oh, not even, not even close. Like it was it was way worse. But it was like a very - It was a simplistic view of what it should be. Like, it worked, but it was not ideal. And so he went, we went down the whole, which is his job as CTO, to go, and he came back with this version. We then called all the people that had said like, “This is garbage,” a quarter ago. And we set up these calls, and we gave it to - We just demoed it to everyone. And all the calls went long, every single one. They were 15-minute calls, and they all went to like 25, 30 minutes or whatnot. And everyone said, “We need, we want access.” There was no login, just an API key, ‘cause it was just a beta or an alpha. And they said, “Oh, we want access.” And we're like, “Sure, yeah. Okay, thank you very much.” But after like the next day, if we'd not send it, every single one, like every call that we did, everyone came back, “Where is my API key?” Like everyone wanted it. We're like, “S**t.” Like this is it. Like I've never felt So one, the understanding to your point was like most people thought it was the same infrastructure for humans and agents. We understood a quarter ago it's not. We just didn't know what was the right primitive. And then when we came, and we can talk about what that is, and we gave it to these people, I've never seen, I've never experienced - I've done multiple companies in my life. I've never experienced this, that people literally call you if you do not give them access. Like they want access right now. And so it's like, okay, they don't want this. the thing that they want doesn't seem to exist, or they have not found it, and they really want what we want. And then when we understood that we're onto something, and then when you think about the size of the market, like the market for human engineers and enterprise is a very large market, so think GitLab or whatnot. But the market for every single agent that will exist ever in the future is just like, what is that market? How big is that? And we're like, “We are all in on this.” And so that is where we made sort of the cut between the old product and the new one.Bare Metal, Stateful Sandboxes, and the Lambda + EC2 ModelSwyx [00:12:02]: Yeah. But it wasn't composable at the time?Ivan [00:12:05]: It was very - It was basically just a Linux box that you could change, that you could define number of CPUs, disk, and RAM. Like that is what you could do, but you couldn't have multiple operating systems, you couldn't resize it on the fly, you couldn't add a GPU, you couldn't do like all the things. It was just the, just the first sort of variation of that, yeah.Swyx [00:12:22]: Was it bare metal from the start?Ivan [00:12:24]: It was bare metal from the start. And so the interesting thing that we thought about right away, so our.Swyx [00:12:29]: Which, give people the background, what is the normal path?Ivan [00:12:32]: Yeah, so, basically most providers run this on top of VMs. And also.Swyx [00:12:37]: Firecracker.Ivan [00:12:38]: Yeah, they run on Firecracker and VM. And so we also fire - We can get - We have multiple isolation layers and we can do that. But the common way to do it is that they, one, that the state of the machine, or the hard disk is not part of the sandbox itself. And the other thing is they're not meant to last forever. So most of them are preemptible, like they can There's a time that they can live. And so our thought was when we were going into this is, agents will be like humans in the sense of you don't want your laptop to be shut down until you're done with work. Like, and you want to close the lid and open the lid, it's the same state. So you - Agents would want that, like the pause and come back. They want those two things. But also agents really want speed, right? Can they get it? So when we thought about it's like we need something insanely fast, how to make it fast, how to make it long-running, and stateful. And so those two things, it's like combining a Lambda and an EC2, right? Those two things together. And so we didn't have an idea how others did it, ‘cause we didn't know too that there was a market around this. It was more like, okay, this is what we need, what they need. And we looked at Kubernetes, it wasn't wasn't good enough for that. We looked at Nomad, it didn't enable that. And so our history in rewriting our own scheduler at CodeAnywhere is basically what my CTO came up with. Like, he's like, “Oh, the learnings from there,” and he brought it. And the funny thing is, our third co-founder, when he saw it, he's like, “Dude, what is this? This is like 2008.” Like, we went back in time, and he's like, “Exactly.” And so the reason why Daytona is like super fast, and you see this on benchmarks, is we essentially, we run on bare metal. We have our own scheduler, we use the underlying, disk, CPU, and RAM of the underlying machine, which means your IOPS are insanely fast because there's no, there's no network between an EBS or something like that. But also the snapshot, the point in time, the templates, are also preloaded on the bare metal machines. So when you fire off a sandbox from a template or a snapshot, you're essentially directed to the bare metal machine where that snapshot is based on that NVMe drive, and then it literally just turns on that machine, and it's local. There's no network latency, anything on there. And so that is sort of the specificities that we, when we're thinking from first principles, what a computer would look like for an agent, that is what we came up with, and that's what we created.Benchmarks, 60ms Startup, and 50,000 SandboxesSwyx [00:15:02]: Yeah. I should maybe, I don't know if you endorse this, but there's someone that does compute SDK, you guys do very well on there, with like the TTI, right? I. is this a, is this a is this a relevant benchmark for you guys? I don't know.Ivan [00:15:16]: I don't know, and it changes every day. So today RKL is.Swyx [00:15:18]: I don't know what RKL is. Never heard of it.Ivan [00:15:20]: Yeah. RK, yeah, so it is there.Swyx [00:15:22]: You are, at least a third of the next tier of performance, and then, there's a lot of other better-known names that are very slow to start.Ivan [00:15:31]: Yeah. We've been the number one by far for a long time, and now there's different, there's different definitions also of sandboxes, different isolation patterns, different other things. So RKL runs it literally on the S3, the data, so it's very different, and they spin up a sandbox, spin up a container for that, so it's a different type of thing. So the definition of a sandbox is something that we can all, we all need to get along with. But yeah, we're insanely fast on getting these things, up and running. And so you can see even there that it's a zero point 0.10 to 0.11, so.Swyx [00:16:03]: Close enough. Yeah. what else do you need, right?Ivan [00:16:05]: Yeah. So the benchmarks itself, so, in this, in I don't think the benchmarks equate to market ownership or revenue or anything like that. and I've seen this with multiple benchmarks, not just in sandboxes, but in general benchmarks around.Swyx [00:16:20]: It's table stakes. It's just like.Ivan [00:16:21]: Exactly. But it doesn't hurt.Swyx [00:16:22]: Just roughly check.Ivan [00:16:22]: Like you definitely have to be up there and you have to be competing so that people know that, oh, this is definitely one of the top. Because this is only one dimension of what customers look for. There's other things like how many can you spin up consecutively? There's a feature set, there's support, there's like all different things that people look at, but you definitely have to be there, on the benchmarks.Swyx [00:16:40]: How many people do people spin up consecutively?Ivan [00:16:43]: So we have.Swyx [00:16:43]: Or concurrently, is the Concurrency, right?Ivan [00:16:45]: There's three metrics that we look at. And so one is like time to spin up one, and so our time to spin up one is 60 milliseconds with network latency. So request, spin up, reply, 60, the whole thing, 60 milliseconds. That is one. But if you wanna spin up 50,000 at once, we are now at about 75 seconds. So it takes about 75 seconds to spin up concurrently 50,000. Some others, there's public data around this, like take 2,000 seconds, which is 30 minutes. Like there's different variations of that. And then there is the so it is speed of one, speed of like multiple, and then how many can you consistently have up and running. And so we basically have right now no limit to how much we can add because we basically own our own metal. But the biggest customer of ours does like about 850,000 every single day is sort of where they're, where they're just shy of a million every single day that they're running, we do have a request for half a million concurrent, which is literally half a million CPUs somewhere running. So that's an interesting.Swyx [00:17:44]: They pay by like vCPU seconds.Ivan [00:17:47]: By seconds, yeah.Swyx [00:17:47]: Or whatever. Yeah. Okay, and so and then, and the other thing is, the sleeping and the resuming, ‘cause it's all the stateful resumption of all these things, how, what kind of workload are people putting through this, right? Like how is it Do we measure by gigabytes in memory, gigabytes in storage? I don't In like network attached storage. I, what are the costly ones of, out of all these features?Workload Economics: CPU, RAM, Network, and StorageIvan [00:18:15]: The most expensive thing are CPU.Swyx [00:18:18]: Okay. Yeah, of course.Ivan [00:18:18]: The second one, yeah Then it's RAM, then it's disk. We actually don't charge.Swyx [00:18:22]: Which is snapshotting, right?Ivan [00:18:23]: No, it's actually the, snapshotting's part of it, but basically the size of your hard disk, of your machine. So do you have 10 gigabytes, do you have 20, do you have 50, do you have whatever? And then the transference of that. Right now, currently we don't charge for, network at all at Polychron.Swyx [00:18:37]: Oh, you gotta, yeah, you gotta fix.Ivan [00:18:38]: Yeah. It is very much a it's a larger and larger part of our bill, so we're working around, that part there. Obviously, that is the least, expensive, so the hard disk is the least expensive, so it's basically CPU, RAM, for us network, ‘cause we don't charge the customer, and then hard disk, is how it's split up. But there's also different types of workloads, so we basically split it up into two types of workloads in Daytona. One is what we call background agents or long-running agents. and the other is, basically RLs and evals, which I put sort of together. And so they have very different patterns of usage, and if you look at the usage of a background And I'll just name names of companies, not specifically.Background Agents vs. RL/Evals: Two Usage ShapesSwyx [00:19:21]: Yeah, open, all hands.Ivan [00:19:23]: Yeah. So like a background agent's a Cognition, a Lovable, a like all these things are Harvey. These are all long-running, background agents. And so if you look at their usage patterns, their usage patterns are similar to human, which is like follow the sun. Basically, the usage patterns of that is like noon is probably the highest, and the midnight is the lowest, and then weekends are lower. weekday is higher.Swyx [00:19:42]: Yeah, that's a fun question. How global is it? Is it very US-centric or?Ivan [00:19:46]: The US is a large part, but we have currently, we have Asia, Europe, and the US regions.Swyx [00:19:52]: So it's quite global.Ivan [00:19:53]: Yeah, it's quite global. We have it all over. It's interesting that our I talked to you a bit about this. Our number one city by user.Swyx [00:20:01]: Hmm.Ivan [00:20:02]: Is Singapore.Swyx [00:20:04]: Oh, wow. Amazing.Ivan [00:20:05]: Which is an interesting one, right? Not by revenue, just by just like by individual head count.Swyx [00:20:09]: Really?Ivan [00:20:09]: Just like an interesting thing.Swyx [00:20:10]: Singapore is, Singapore is weirdly high in the adoption charts of AI for the population. It's like an, seven, eight million population. And it's like keeps showing up.Ivan [00:20:20]: No, it's quite interesting. We were quite shocked, and I was like, “Oh, this is interesting.” And also one that's up there.Swyx [00:20:24]: There's a reason I'm doing AI using Singapore. it's because I'm from there.Ivan [00:20:27]: We're there. We're gonna, we're gonna be there as well. and it's interesting that Japan is in the top or like Tokyo's in the top, which is in all the tech cycles it has never been. It has never been, so it's quite interesting that they're.Swyx [00:20:39]: I think the Japanese just love AI. Yeah. It's that, and then it's Brazil. That's it.Ivan [00:20:44]: Brazil has always been in.Swyx [00:20:45]: I think.Ivan [00:20:46]: Even when I look, if you look at like GitHub's data and ask historically with CodeAnywhere, it was always like US, Western Europe, and then you'd have like India, Brazil, China, like that would be there. But like Singapore was not in, specifically Japan was never in sort of that top, that top.Swyx [00:21:01]: Yeah. Weird pockets.Ivan [00:21:01]: Weird. Yeah, so it's very global.Swyx [00:21:02]: Okay, so actually that, but that's helps you to distribute your load through, all time?Ivan [00:21:08]: The interesting thing is like we have those kind of loads, but if you look at the researcher loads, they're quite different. So what they are is like if you give them concurrency of 10,000 or 50,000 or 100,000 CPUs at ARMb, when they fire off a run, it's just 100%. And then it just runs, and then it stops. So it's very, the usage pattern is squares basically, right? And it's also not follow the sun, because people will fire it off at midnight before they go to sleep but then wake up and so it's very unpredictable, so you don't know where that is. So the shapes of the usage are quite different than we have had before. And also what's interesting is when it's sort of a follow the sun, even if you have a high growth company, you can sort of predict your usage patterns and have enough capacity for that, because it's sort of, it grows in a, in a way you can project. When you have companies doing sort of like evals and RL, they're super spiky. So they're gonna come in, it's like, “We're gonna use nothing, then can we have 100,000?” Right? And then go back down. And then 100,000, go back down. So it's very different, right? And.Swyx [00:22:09]: Do you want to lock them into commits so.Ivan [00:22:11]: Yeah, we do.Swyx [00:22:12]: Yeah, okay.Ivan [00:22:12]: We so we have to lock them into some sort of commits to have that capacity, because we have to have, basically we have to have the capacity for peak. Right? And so right now, Daytona's mean utilization is 15%, 1-5.Swyx [00:22:25]: Oh my God.Ivan [00:22:26]: So it's very low.Swyx [00:22:27]: Because it's very spiky.Ivan [00:22:27]: It's very spiky, but we get up to 90%. so we have these things. And so what we're, what we're looking at right now as a company is similar to Cloudflare where you can like geo move things around, but that works really well for basically the background agent where it's follow the sun. But this, it's not. Like it's a very different shape. Obviously with scale you figure these things out, but that's an interesting new problem that we have, as a compute provider in the agent space. And when we were doing the conference recently, and so we talked to like Nikita from Neon and.Swyx [00:22:57]: I should bring it up.Ivan [00:22:58]: Parag from Parallel and whatnot, everyone has the same problem. Whereas the usage is super spiky, and this is something that has not happened before, that you have these types of like it was always, it the amplitudes were not this high, right? So it's quite interesting use case and problem solve.Compute Conference and Spiky Agent InfrastructureSwyx [00:23:12]: Yeah, I don't know if we're gonna bring this up again, but let's just talk about the conference, you had like 1,000 something people at the Warriors game, at the Sorry, where is it? What's.Ivan [00:23:22]: Chase Center.Swyx [00:23:23]: Chase Center.Ivan [00:23:23]: Chase Center.Swyx [00:23:24]: I went. It was, it was very impressive. Obviously, you can, how to throw a conference, what did you learn? you put, you pulled together all these impressive names.Ivan [00:23:33]: What I.Swyx [00:23:34]: What were you looking for?Ivan [00:23:35]: My thesis behind the Compute Conference was let's bring together people that are building infrastructure for AI agents. Because when I think of what we're building, it is the agent is the primary user, what are the ergonomics and usage patterns of agents, and so we can do that. And what I found, this was a theory, it wasn't proven, is that we all have these problems, as I touched onto. And I was, as I was talking on stage, it was like we all have the same underlying infra problems, which is this spiky workloads, unpredictable workloads that we've never had before, in human, compute or human infrastructure. And it's, again, it's the same when I was talking to Parag or when I was talking.Swyx [00:24:20]: Lynn. Nikita.Ivan [00:24:21]: Lynn, Nikita. Lynn especially, I was talking to her the other day as well. Like the It is a very interesting type of problem to solve because I can touch on Cloudflare because there's a lot of like talk about that recently as to how they solve that, which is they have a bunch of geos, and basically, as users work in different places, and depending on your tier, they can move you around the geos. And so that how, that's how they get the higher utilization. But you can sort of predict these, and it's If it's something in You'll rarely get a spike that is 10 orders of magnitude. Like you'll get a like let's say one of your customers has some like an exponential curve. What is that to I'm using Cloudflare as an example. 10%, 20%, whatever it is. I don't, I don't have this data, I'm just assessing. It's surely not 10x, right? It's surely not something there. And so how do you go out and solve this problem? And we're all solving this in different ways. So we have.Swyx [00:25:11]: She also has the same thing.Ivan [00:25:12]: Yeah, I know specifically that like Neon had that issue as well. Like how are we solving these spiky loads and things like that ‘cause we talked about it. And so the interesting thing for me to actually internalize was, yes, everyone that's building for agents first is going through this, and we're all solving similar problems, which is quite.Swyx [00:25:28]: Let me let me double-click on this. Okay. So for example, Neon, I happen to know that they're very sort of S3 oriented, right? so they're just like fully bet on S3. And you get to benefit from S3's distribution and infrastructure. So I would imagine that Neon doesn't have to care, whereas Lynn maybe has to care a bit more because obviously she's doing GPU inference. And, for listeners, we did an episode with her, one and a half years ago. And you have to care. But like, right?Ivan [00:25:54]: Parag cares for sure, and Nikita.Swyx [00:25:58]: And Parag is C of, Parallel.Ivan [00:25:59]: Parallel, yeah.Swyx [00:26:00]: Former CTO of Twitter.Ivan [00:26:01]: Twitter, yeah.Swyx [00:26:02]: They are the search.Ivan [00:26:03]: Yeah, they're search, yeah.Swyx [00:26:03]: I You and I know but the listeners don't know.Ivan [00:26:08]: Yeah, we can put it down in the screen, and so ‘cause we, when we were talking.Swyx [00:26:11]: I'll put it up on the, on the screen.Ivan [00:26:12]: Yeah, right.Swyx [00:26:12]: People can look it up if they need.Ivan [00:26:14]: Look it up. And, yes, but they still have CPU and RAM, allocation that you have to have up and running. And so CPU and RAM, you have to allocate that and have that ready. And so there's basically two ways to do it. One is you either over-provision and you can handle the bursts, or two, you basically have, I don't know if this is a term, just-in-time compute, which is like as your load becomes, as your usage comes in, you can fire off requests for VMs or bare metals at other cloud providers and then get them up and running.Swyx [00:26:43]: This is if you go above 100%, right?Ivan [00:26:45]: Yeah, this is.Swyx [00:26:46]: Like your overflow.Ivan [00:26:46]: If your overflow, like spillage or whatever you do.Swyx [00:26:48]: You probably lose money on it, but it doesn't matter, right?Ivan [00:26:50]: It, not Well, you might, you might not That is a more cost-effective way to do it but it's a slower way to do it. Because basically what you have to do is you have to like queue your requests, spin up these just-in-time compute, get it all ready, provision it, and then get your workload there. And so if the time isn't important that much, that's fine, and you can do that. But if your customer, and especially for, let's say, the RL training runs, the reason why a lot of people come to us is because GPUs are more expensive than CPUs, right? So you want your GPU running at, what, 100% the entire time. And so when you're running runs on CPUs, when the when the CPU cycle is like down and spinning up the next one, you want that to be instantaneous so that your GPU doesn't go down, right? And if you then have to like go out and provision machines, you're essentially telling the GPU that it has to wait, and that's incurring our cost. So there's things that you have to try to solve for there.RL Workloads, Declarative Images, and Kubernetes ReplacementSwyx [00:27:43]: Yeah, let's talk about the different workload, right? You said that, what was it? A few months ago, you had zero RL workload and now it's 50%.Ivan [00:27:52]: It will be this one, 50%, yeah.Swyx [00:27:54]: Let's talk about how different it is, right? Like I imagine, for example, a lot less dynamic code generation of like arbitrary code. Like here, it's probably all the same code. You're just doing parallel runs or something, I don't know.Ivan [00:28:05]: Yeah. So you'll have multiple Depends on the like for each run, you'll have a snapshot. And they, for the most part, they actually do use our declarative image builder, which is like, “Oh, we, the agent wants these dependencies, these env vars.”Swyx [00:28:17]: These ones, yeah.Ivan [00:28:18]: Yeah, the declarative image builder, it.Swyx [00:28:20]: Which is a very modal like thing that they.Ivan [00:28:22]: Yeah. And so we build it on the fly and then we propagate that snapshot, and you can spin up as many sandboxes as you want against that snapshot. And then if you have to do changes, the model can, or like it could be also be automated. It's like, “Oh, now for the next run, we need to install these things or remove these things or whatever to get, a task done,” and then it goes off and runs that. So yes, that is something that it seems that they prefer. The number one reason I found, or should I say, let's take a step back. What we are competing against in that environment is essentially managed Kubernetes. So EKS, GKE, whatever. That is what the vast majority run on. And anyone that has tried Daytona versus GKE, EKS is like, “I'm never going back.” That has always been. There's a few reasons. One is the ergonomics. So if you have, if you're using Kubernetes to spin that up, you have to essentially manage the interface interactions with that. Daytona, although as a compute provider, it's more akin to a Twilio and Stripe from a consumption perspective than it is an AWS. Like you have an API, an SDK, it's quite like easy and seamless to get these things up and running, that's one. The other is the speed to which we spin up, which we mentioned earlier, which is much faster, and the scale to which we can go to. We haven't got into features, but an interesting feature is that it's very hard to OOM, or out of memory, our sandboxes, because we can dynamically on the fly.Swyx [00:29:48]: Resize.Ivan [00:29:49]: Resize, which is like impossible on almost any other thing. There are some technologies that enable you to do that, but it's like a very hard thing. And so we actually saw this when, the Terminal Revenge team is, brought us actually. So thank you, Alex and the team, that brought us into this whole space.Swyx [00:30:05]: It's just very rare that, a framework would just say, “Guys, just use Daytona.”Ivan [00:30:11]: Yeah, I think it says it somewhere. Yeah.Swyx [00:30:13]: Yeah. I was like, “What is this?”Ivan [00:30:15]: There's all, there's multiple there, but they also mention a few other places. and so Daytona specifically-We have, the, just jumping on themes here We, I don't know where it says Data Center.Swyx [00:30:27]: I, there.Ivan [00:30:27]: Doesn't matter.Swyx [00:30:28]: There's a very strong recommendation, which is, very unusual. Which is, it's.Ivan [00:30:33]: We do not pay them for this, just.Swyx [00:30:34]: I know, yeah. They just like you.Ivan [00:30:35]: Yeah, they like us. yeah, and also a thing, so, Data Center has multiple isolation sets underneath. The customer doesn't have to know what they are. But basically we have Docker, which is a container, that's hardened with Sysbox. So it's Docker's, isolation that is a security equivalent to a VM, but it's still a container. And that is the default, and they, especially in these training workloads, really like that as an interface to be able to use just a basic Docker container, and we enable Docker and Docker. Which for these RL runs, if you need to do a Docker compose or Kubernetes, you can spin up a K3S inside of these things, which unlocks a huge amount of workloads that you can do that you cannot do on other providers. So just on that part is much more interesting. And so we went that, through that. We showed them that we could do that, and they enjoyed that quite a bit. They being the general venture people.Swyx [00:31:28]: Those people, yeah.Ivan [00:31:29]: And Harbor people.Swyx [00:31:29]: Harbor people, do are they, are they a company yet?Ivan [00:31:33]: As far, I do not know.Customer Pull, Slack Connect, and the Computer Use BetSwyx [00:31:35]: Okay. All right. Yeah. It's like super obvious that like, there's a lot of excitement and success around these things, okay, so yeah, tell us more, right? Like, this is an exploding workload, Harbor adopted you, which helped speed things along. But what are you learning as this new workload comes online?Ivan [00:31:53]: There's a couple things that we learned, which we chat about in the beginning. We, and this has led our story, as we mentioned, we like talked to a lot of customers along the way, and we add more features and more tool sets as we talk to customers. And it's interesting that And I think it's that the ecosystem is so small and/or the models get smarter, where when we see one user come with a request, we know it goes on a roadmap if like three to five customers come with the same request in that week. It's like very bizarre. It happens so many times, which is.Swyx [00:32:27]: Because they're all friends.Ivan [00:32:28]: Sorry?Swyx [00:32:28]: They all, they're all friends. They're all in the same group chat.Ivan [00:32:30]: Yeah, probably, yeah. ‘Cause and they're like, “Oh, can you do this?” And I'm like, “Okay, this is interesting. We'll put it on a feature request.” And then the next one's like, “Oh, can you do this?” “Okay.” It's all the same, right? It's always the same. And so what we try to do, and I personally try to do, I try to be on as many call, quote-unquote “sales calls” I can. I'm in every Slack channel. We literally have about 1,000 Slack Connect channels, something like that. It's an interesting, there's so many interesting things you find out when you have all the Slack channels. You can also see where people, transfer between companies. You see leave Slack channel, enter Slack channel. It's an interesting thing. Also, just I digress, I feel that Slack Connect is literally LinkedIn what it should be. You have a list.Swyx [00:33:08]: LinkedIn charges you to, use your own connections, but Slack doesn't, right? Slack is like, do it for free. It's more lock-in. It's great.Ivan [00:33:15]: Yeah. It's amazing. Yeah. It's one of the reasons.Swyx [00:33:17]: You're gonna pay Slack for life.Ivan [00:33:18]: Exactly. You're there for life. So that's interesting. And so one of the things, the newer things we were talking about earlier is we made a big bet and put a lot of investment on computer use. that is not seen publicly the light of day. We haven't GA'd that yet, but we have.Swyx [00:33:32]: Is there a thing I can pull up?Ivan [00:33:33]: There is computer use there. It's right up a bit.Swyx [00:33:36]: Oh, yeah. Okay.Ivan [00:33:38]: What we have, what we talked about and what we've seen publicly is there's this theme now about, the human emulator where And Elon from XAI has talked about this publicly, and if you think about the models today, they're actually quite sophisticated and they can do a lot of work, but they still don't have access to all the tools. Like, I'm a strong believer that the most efficient way for an agent to work is essentially headless or through, terminal or whatnot. But if we, if we look at knowledge work in general, there's about 100 million knowledge workers in the US, about a billion in the world, and knowledge workers, and the salaries of them aggregate to 10 trillion in the US 50 trillion worldwide.Swyx [00:34:24]: Wow.Ivan [00:34:25]: Something like that. And if we look at, the five most important sectors of that, so like healthcare and government and financial services and whatnot, that's about 56% of that. So let's say it's about half of that. So in the US it's about 25 trillion, and most of them, most of that work is actually still locked into legacy apps inside of Windows, which is not going anywhere for a very long time. Like, people just won't invest in that. How much of it? our assumption is the following: if, in the RPA market, which is similar market, well, not the same 25% of, these white collar, workers', work is automated. If an agent is more sophisticated, can go through more runs, figure stuff out, let's say it's, 40%, right? And so if you take 40% of that, you get to essentially, $10 trillion a year.Swyx [00:35:17]: That's a TAM.Ivan [00:35:18]: That is a that is a TAM. So that's the TAM of the models, right? That's not our, essentially ours. But you get to that size, and to be able to do that, you essentially have to give agents these computers with the legacy. So computer use, either Mac or Windows or Linux. Linux we also obviously have and others have. But Windows specifically is something very new, and the only option right now is an EC2 with, Windows or on Azure. Both of them take anywhere from three to five minutes to spin up. We've created an actual sandbox, so it's a second instead of milliseconds, but you have, point in time snapshots, you have, forking, you have all the things that you have from a sandbox, but essentially enables you to hopefully unlock all this value. And so that's been our big push and bet, but we've sort of, kept our ear to the ground. What is sort of the next things in the market?RPA Returns: Why Agents Still Need ComputersSwyx [00:36:06]: Yeah, knowledge work, and building, and sort of RPA, the next wave of RPA. I got very excited about RPA kind of during COVID times. The UI path was IPO-ing. And it was, a very hot Isn't it, Eastern European?Ivan [00:36:20]: It is, Romanian.Swyx [00:36:21]: Romanian?Yeah, it might be the only Romanian, big unicorn okay, yeah. This I don't I don't, I don't have like a I think there's, I think there's a stage being set for the resurgence of RPA, ‘cause everyone understands that, yeah, no one wants to deal with these shitty apps and no one's gonna rewrite them. Like, you just have to do, a remote operation and programmatic operation of them.Ivan [00:36:45]: If you wanna unlock it, my own setup was basically the following. So I was doing a board deck recently, last month, whatever, and I'm like, “Okay, let's just, let's just do automated.” So, all our data's in, ClickHouse and PostHog and QuickBooks, where everyone else's is, and I'm basically, connected that all to, my Cloud code, like go off and go Cloud code whatever. Go off and, here's the integrations, go do that. It pulled out the first report, which was great. It connected to Brex and all these things, pulled it, which was great, and then I say, “Okay, now pull out this, and this,” and I kept getting, really well McKinsey-style design reports, but the data said partial data. all the missing data, partial data. Like, it can't access all the things, and I got so frustrated, and so I got, I got, my Mac Mini virtual sandbox with OpenClaw. I gave it its own account in our company, and then I went to all these services and created a read-only account, so literally like an intern in your company. And so I would say, “Now go and do this report,” and it would get the same, or like, “I can't via the MCP or the API or whatever. I can't get all the information.” I'm like, “Go log in.” And it will log into the website, then go in, export the data. It'll export the data and do the thing end to end. So even for things that have today APIs, not all of it is exposed, and I to get value, I get immense value right now, but it has to be a computer usage, unfortunately, and so I spend a bunch of tokens just on that, but I get the job done. And so if even a startup like ours, and using all the hottest tools, still needs a computer agent what hope does, Goldman have to have a headless, right?Swyx [00:38:22]: Yeah, what a - Why isn't Microsoft doing this?Ivan [00:38:27]: I'm pretty sure, Satya had a post yesterday.Swyx [00:38:29]: Oh, okay. I see.Ivan [00:38:29]: Which was like, “Every agent needs a computer.”Swyx [00:38:31]: I see, I see.Ivan [00:38:32]: So they have launched something recently.Swyx [00:38:34]: Yeah, they have Microsoft Power Automate, I'm sure, I'm sure, they're gonna have their version.macOS Sandboxes, Apple Constraints, and the Windows OpportunityIvan [00:38:39]: Version of that, yeah.Swyx [00:38:39]: You're gonna try to do yours, and it - I always know there's always demand for Mac, but I know it's, tricky to host, macOS sandboxes.Ivan [00:38:49]: We will have macOS sandboxes fairly soon. The problem with macOS, OS sandboxes is, I'm deep in this, I don't know how much interesting is.Swyx [00:38:55]: No, it's.Ivan [00:38:56]: MacOS has this problem.Swyx [00:38:57]: It's a licensing thing, right?Ivan [00:38:58]: Licensing thing. So one, you're allowed to run only two parallel VMs per machine, so that's one. Two, you can only license to a different user every 24 hours. So if you come in and theoretically, if I wanna charge you per second and I charge you one second, I have to have it idle for the rest of the day. I can't have anyone else doing that. So the pricing will be different in the sense that I will have to - we would have to charge for 24 hours, and that's not even, that's not even the most difficult thing. But the, thing above that is, from a security perspective, they enable you to do memory snapshot, pause, resume, but only on the same physical drive, physical machine. And so what you can do in, Windows world or Linux world is that I can move in the background, your snapshot from one to the other and manage load, right? Here, if you wanna do that, you essentially have to have your.Swyx [00:39:49]: Yeah, snapshots. Yeah.Ivan [00:39:50]: Your.Swyx [00:39:51]: It's like.Ivan [00:39:51]: Physical machine.Swyx [00:39:52]: You can't break it up.Ivan [00:39:53]: You can't, you can't move things around that, and all of that is, that part is, from a security standpoint, if it is written. Like, I understand the security aspect of that, but it disables you from doing these agentic, like really scalable agentic workloads.Swyx [00:40:08]: You need to do a vibe-coded, clean room implementation on macOS that you can then - That's like Clean OS or something. I don't know.Ivan [00:40:17]: So. We have.Swyx [00:40:18]: ‘cause like Linux was originally like a clean room rewrite of Unix.Ivan [00:40:21]: Okay. Yeah.Swyx [00:40:21]: Or something like that, right? Like same thing to macOS. Someone needs to do it.Ivan [00:40:25]: Someone will do that, and someone will have some long-running agents for a few days to figure this stuff out. But yeah. So definitely we - we're really close to offering something ‘cause people do want it, but the pricing will be different, and the feature set will be sort of stringent.Swyx [00:40:38]: Yeah, nobody's gonna use this. like, the labs, the labs will because they want to automate macOS.Ivan [00:40:42]: They have to do RL. They have to do RL again. But even if you The - So the point is with the RL part, if you, if you do RL on macOS, then the next iteration of the model comes out, it will be able to use these tools significantly. Then you actually need to run those, that somewhere. So you're gonna have to have that, later on. And from, if anyone at Apple is listening, I very much feel that they are shooting themselves in the foot of the scale of the revenue of compute or licensing they could get if they would just enable a concurrency model similar to what you can get on a Windows and a, and Linux.Swyx [00:41:17]: Yeah. Yeah. And I'm sure they've heard this before. They just don't care. Yeah, it's And maybe they will change their mind with the new CEO.Ivan [00:41:24]: Yeah. We'll see.Swyx [00:41:25]: We'll see.Ivan [00:41:25]: High hopes.Swyx [00:41:26]: High hopes.Ivan [00:41:26]: High hopes.Swyx [00:41:27]: Okay. But I, it's very clear the market opportunity is huge in Windows, and you can go for a long time on just Windows, but your customers are gonna want both. and I think, it is interesting to me that, this is the sort of God application of agents, right? Like, I don't It was - How big was OpenClaw for you guys? Like, was it, was there, a significant bump.OpenClaw, Agent Labs, and the B2B2C Sandbox MarketIvan [00:41:54]: Not for us because we.Swyx [00:41:54]: Because you already.Ivan [00:41:55]: We're kind of positioned differently. Whereas although it's completely PLG and we have individual developers that use it, most of the users that use Daytona are sort of a B2B2C. Sort of it's either B2B or B2B2C. So, in the researcher world, it's B2B, so you're selling to, labs and neo labs and things like that. But on the long-running agents, it's mostly, from a scale revenue perspective, it's mostly B2B2C, where you have a app layer agent that uses you at a big scale.Swyx [00:42:26]: Like a Manus. Yeah.Ivan [00:42:28]: Like a Manus Lovable type of thing.Swyx [00:42:31]: Yeah. I think that's the question of, well how, um-Uh, yeah, B2B to C is basically to me what I've been calling an agent lab, which is kind of like you're not in a model lab, but you're making a very good wrapper that is a platform that other people can sign up so they don't have to code those things. Yeah, it sound, it sounds like a much better market than the direct OpenClaw market.Ivan [00:42:56]: I've like - We I've done multiple things. So the CodeAnywhere's part of our career path R in the calendar, was very much an end user developer product. And so that is great. It You can get a lot of developer love, and I feel that we do as a company have a bunch of developer love. But it's a different type, where it's people building these things. Again, it's more akin to a Twilio because you don't really run - As a person, you wouldn't run Twilio. I don't know how many people remember. It was like ask your developer billboard and whatnot. And people really love Twilio, but they only used it inside of like, “Oh, I'm building this app or service for thing.” And so we're very much directly to that. And you also know that I used to work for a competitor for Twilio, so it's kind of ingrained, in my DNA.Swyx [00:43:35]: People don't know InfoBip is that big.Ivan [00:43:38]: Yeah, it's.Swyx [00:43:39]: Because.Ivan [00:43:40]: It's a billion euro.Swyx [00:43:40]: They're all American. They're like, “Whatever's in Europe doesn't matter to me.” But like it's the, it's the same size or bigger? Same size?Ivan [00:43:46]: It's about half the size.Swyx [00:43:47]: Half the size?Ivan [00:43:48]: Yeah, about half the size.Swyx [00:43:48]: It's like, yeah.Ivan [00:43:48]: Still huge. Multiple billions a year. Yes.Swyx [00:43:51]: That's crazy.Ivan [00:43:51]: Exactly, and so that - These are like really interesting and large revenue-generating, very sticky businesses. Whereas when you're selling to the - When your focus is the end developer, it is a very hard sell because they're very price sensitive, very price conscious, very around that. And there's very It's very hard to scale. Your cap is the number of people that are willing to spin up - First of all, wanna spin that up, and then spin up multiple of these. Whereas if you're in the enterprise one, like we know everyone's talking about like how many tokens they're spending, I'm spending. Like a lot of companies today are like, “If this is our company, spend as much as you can.” Like basically that is where we're going. And so if you think about that paradigm, where you're selling to companies that say, “Spend as much as you can to generate, productivity,” versus, “Oh, I'm a single person. I have this much budget, and I'm doing this thing because it's fun or it's helping me out or whatever.” Like it is a different, it's a different go-to-market, I think, strategy.MCP, CLIs, and Sandboxes as the Agent RuntimeSwyx [00:44:50]: Yeah, there's a lot of discussion. I'm just kind of going through like the mental list of things that are in your favor, which is, for example, MCP versus CLI. Like obviously you want CLI. It's been very good for you. I feel like it's maybe a drop in the bucket or maybe it's huge. I'm just checking whether it's like these are big trends.Ivan [00:45:10]: Those things you - work well in our favor, to your point just because every.Swyx [00:45:13]: They're kind of drop in the bucket, right?Ivan [00:45:15]: I think it's like sort of all the things come together. And so there's so many things that impact that. To your point, like OpenClaw wasn't huge for us, but like having the agent SDK, from Anthropic, so or Cloud Claude Code was very interesting. The reason why it was interesting is that a lot of, let's call them app I don't know what to call them, app layer agent companies, essentially they are like, “Oh, I can create this new app, this new agent. All I need, I just use Claude Code, and I throw it into a sandbox, and then I have my interface to the human to that.” And so that enabled so many more companies to actually offer this, and then they would pull on sandbox. So that was, that was interesting. And to your point, like MCP, versus the CLI, the MCP is an interface against an API, whereas the CLI is like you can actually go do things. Like this is it. The difference between integrations and actually running scripts or data or analysis against a thing. So being able to use a CLI very well enables the agent to do more things, and it's because that people will invoke a sandbox, they'll run it in the CLI, and but it'll do anal-analysis on that data and then give you an actual result versus just, pulling data from an API source.Swyx [00:46:29]: Yeah, it's a layer of indirection basically, it's the same thing as agentic search versus RAG, which where you're.Ivan [00:46:34]: Exactly, yeah.Swyx [00:46:34]: Just like you just win whenever people put more agents into their workflow. And so like it doesn't really matter, but I'm just kinda teasing out like what else have people heard about that like it's sort of, “Oh yeah, this is another sandbox use case. Oh yeah, that's another one.” Am I, am I missing any big ones?Ivan [00:46:51]: The thing, the thing that people, which is the computer use stuff, which I think is probably the most interesting one, is, and to your point, we've talked to so many people over the last year. It's like, “Oh, like why do you need a sandbox? Why do you need this? Why this?” And to your point, it's like, “Oh, I need sandbox for this. I need sandbox for that. I need sandbox-” It's like, “Oh, I need it for every single thing.” And so basically what I, what I - and it sounds like a broken record, it's like you use a laptop every single day, right? And you are n of one. It's just you. But now imagine how And by the way, the laptop, the computer PC market, the PC market is about equal to the cloud market in total. So it's about 150, 180 billion a year. Something like that. It's about roughly the three cloud hyperscalers is about equal to like Apple, HP, Lenovo, whatever, It's a little bit less, but it's sort of like that. And now imagine And that's just like, so how big is the addressable market? What, how many people are there in the world now? What's the last data?Swyx [00:47:45]: Let's call it eight billion.Ivan [00:47:46]: Eight billion. And so let's say you can have two computer, like you have one personal and one business, whatever. Like so it's double that, right? and so that's 16 billion, right? How many agents are gonna be running in two years, in 10 years, in 100 years? Like And for every single task, they will need one of these. And so how big is that? That market is essentially quote unquote “infinite”. You will get to the point, and Dylan Patel was at the conference talking about, from SemiAnalysis, that talks usually about GPUs, was also talking about how CPUs will now be a bottleneck because it will be the constraint. You won't be able to grow, or we won't be able to have enough of these because there won't be enough CPUs to basically do.Swyx [00:48:23]: Yeah. Well, I actually had a really good podcast with Doug Oliphant, who, which was his president at SemiAnalysis, where they've basically been like, yeah, it's been a GPU shortage first, but then it's cascaded down to memory and now to CPUs.Ivan [00:48:35]: CPU, yeah.Swyx [00:48:35]: It-What's next? So networking. So, networking actually has been in shortage for a while if you're looking at, just GPU networking. But, yeah, it's really crazy the amount of computer use that's going on, yeah, cool. I, other questions are, just the one very big part is the open sourceness which you didn't have to do, your competitors don't do, like it's not, a lot of people are worried about keeping their projects open source because some competitor can just slot fork it. I don't know if there's any reflections on just being an open source company.Open Source, Trust, and Enterprise ProcurementIvan [00:49:15]: Yeah. There's a bunch. So we the original product that we did was open source.Swyx [00:49:19]: Yeah. CodeAnywhere.Ivan [00:49:20]: So doing that was actually very good for us. There's basically a saying of, What's the saying? Like, companies that are, that are doing really well, measure themselves against, free cashflow, that are kinda okay, it's EBITDA, then, it's, it goes all the way down.Swyx [00:49:36]: The worst is like GitHub stars.Ivan [00:49:37]: GitHub stars. GitHub stars are the worst, yeah. So you go all the way down to GitHub stars. And so our original one was GitHub stars. That's what we talked about, we're at the point we're talking about revenue, so we're we've gone up the stack on that. And so we started.Swyx [00:49:47]: No, profit.Ivan [00:49:48]: Yeah. We haven't, we're, we'll get there. We'll get there. But basically at that point we did stars and GitHub and it was useful, and the original variation that we did, it we split the core into its own repo and it was Apache 2.0, so very, permissive. And then we basically would bundl

Fresh out of 2 recent events, Charlie and Crissy are excited to share some tangible AI Use Cases.After a quick review of events and the current state of GTM content, Charlie and Crissy discuss how good data is foundational to how AI works. Then they dive into 6 concrete AI use cases: 3 that we've implemented for our clients, and 3 that we've been using in our own internal operations.This week: 00:00 Intro05:37 Does AI Need Good Data?15:31 3 Client AI Use Cases34:51 3 Internal Ops AI Use CasesHear more from us: Subscribe to us on Youtube: https://www.youtube.com/channel/UCN-x5u0G03LWmU0Ds_4zR8wSubscribe to our newsletter here: https://www.cs2marketing.com/revenue-growth-architects#subscribe-to-newsletterFollow Crissy on LinkedIn: https://www.linkedin.com/in/crveteresaunders/Follow Charlie on LinkedIn: https://www.linkedin.com/in/charliesaunders/

Mary Price grew up as a first-generation American, learning early lessons in grit, integrity, and trust from her family and her uncle's shoe repair shop. Today, as the founder of Penfield Talent, she brings that same people-first philosophy to talent leadership, executive search, and helping companies build high-trust teams. In this conversation, she shares how she balances empathy with high standards, what AI can and can't replace in recruiting, and the inside story of leading GTM hiring at Slack through the Salesforce acquisition, when her team doubled the sales organization in one year during COVID.Connect with host James Mackey on LinkedIn!Intro (00:00)Background (00:46)Career (09:02)TA (37:38) Thank you to our sponsor, SecureVision, for making this show possible!  Follow us:https://www.linkedin.com/company/82436841/SecureVision: #1 Rated Embedded Recruitment Firm on G2!https://www.g2.com/products/securevision/reviewsThanks for listening!

Ever been on the receiving end of a terrible sales pitch? Dave and Regan have — and in this episode, they're calling all of it out.As buyers who get sold to constantly, they rate the sales tactics that give them the ick, break down exactly why each one doesn't work, and give the remedy for every single one.The icks they cover:→ "Just following up" — the laziest email in sales and what to send instead→ The pitch slap — accepting a LinkedIn connection and getting sold to 3 seconds later→ Automated email cadences with zero personalization — and why they're killing your domain reputation→ Outreach without insight — product flogging with no relevance, no value, no reason to reply→ Jumping straight to demo without understanding who you're selling to→ End of quarter pressure tactics — "sign by Monday for a discount" and why it backfires every time→ The post-sale drop-off — why the best sellers never stop selling after the closePlus the outreach that actually worked on them — and won the job.Whether you're a founder, sales leader, or account executive — if any of these tactics sound familiar, this episode is your wake-up call.

Episode Summary Fractional CMO Javier Lozano Jr. helps B2B tech and tech-enabled services build predictable GTM engines. Former startup CMO, scaled $1M to $20M. Now installs repeatable revenue systems. Who's your ideal client and what's the biggest challenge they face? What are the common mistakes people make when trying to solve that problem? What is one valuable free action that our audience can implement that will help with that issue? What is one valuable free resource that you can direct people to that will help with that issue? What's the one question I should have asked you that would be of great value to our audience? When was the last time you experienced Goosebumps with your family and why? The Predictable Pipeline Diagnostic Discovery Call w/Javier Get in touch with Javier: Website, LinkedIn, Facebook, Instagram Stakeholder Confidence Focus Turn board skepticism into enthusiastic alignment with the KAIROS assessment system. Book your 30-minute KAIROS Strategic Assessment (€147) and receive frameworks that build unwavering stakeholder trust in your strategic timing. Only 5 spots are available this week. https://www.uwedockhorn.com/research

Founder quality becomes more important as startups become easier to build.Trae Stephens, co-founder of Anduril and partner at Founders Fund, has spent years backing founders with strong conviction, including most recently at Roadrunner.He shares why too much capital too early can hurt startups, and why the best companies are built by teams with complementary strengths.Guest: Trae Stephens, co-founder, Anduril and Partner, Founders FundConnect with Trae StephensXLinkedInConnect with Joubin:XLinkedInEmail: grit@kleinerperkins.comFollow Grit on LinkedInFollow Grit on X​Learn more about Kleiner Perkins

The sales playbook is being rewritten and product-led growth is at the center of it.In this episode, John sits down with Adam Carr, CRO at Apollo.io, to dig into how PLG has evolved, what AI is doing to the sales rep's role, and why the best sellers today need to think less like closers and more like go-to-market architects. From hiring smarter to building SDR programs that actually stick, this one's packed with real talk from two people who've lived it.If you're in sales leadership, trying to figure out how to build and develop a team in this new world, this episode will give you a framework to work with.Want to level up your team before the market moves on without you? Visit www.jbarrows.com and learn how you can Make It Happen.What You'll Learn:Why people don't buy from people they like but from people they trustHow to hire intentionally and slow down when everyone's pushing you to scale fastThe difference between PLG, product-led sales, and sales-led growthWhat product signals actually matter when deciding when to bring in a sales repThe three skills every modern sales rep needs to stay relevant in an AI-first worldAdam Carr is the CRO of Apollo, where he leads the company's go-to-market strategy and revenue growth initiatives. Since joining in 2025, he has focused on scaling a modern GTM engine that blends product-led and sales-led growth, aligning sales, marketing, product, and customer success into a unified customer journey. Under his leadership, Apollo continues to support millions of users and hundreds of thousands of businesses in accelerating growth through a scalable, customer-centric platform.LinkedIn: https://www.linkedin.com/in/adamhcarr/John 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: https://www.linkedin.com/in/johnbarrows/ Instagram: https://www.instagram.com/johnmbarrows/TikTok: https://www.tiktok.com/@johnmbarrowsCheck out John's Membership: https://go.jbarrows.com/Join John's Newsletter: https://www.jbarrows.com/newsletter