GTM AI Podcast with Coach K

www.gtmaipodcast.comThe Swarm built over $10M in pipeline in 12 months by getting warm intros from its own investors, advisors, and team. Co-founder and CEO David Connors shows exactly how they do it with Clay, Claude, and their own relationship graph.In this episode, David walks through the full warm intro motion: mapping your company's network without forcing everyone to sign up, scoring which relationships are real, carving warm accounts out of your target list, and the once-a-month batched ask that keeps investors and advisors happy to help. We also get into the Sequoia origin story, the Claude MCP demo, and why trust becomes more valuable as AI floods every inbox. Built for founders, CROs, and GTM teams selling into complex, high-ACV deals.What you'll learn:- How to carve warm-intro accounts out of a 10,000-account list, then route the rest to cold and ads- The three intro plays (direct, indirect, mention) and which one drives the most pipeline- Why batching 10 intro asks per connector per month protects social capital and trains your cap table on your ICP- Why the top 20% of your people hold 80 to 90% of your best relationships- How to recruit advisors for their networks and turn them into a repeatable channel- How to use suppression lists and a "top 1%" vouch exercise to capture what's in people's headsChapters:00:00 Intro00:43 Who is David Connors02:04 What The Swarm does and the Sequoia origin story04:26 The $10M+ pipeline play and the tech stack05:35 Connectors and passive network mapping08:05 Warm vs in-market: carving out target accounts10:14 Finding warm intro paths inside Clay14:09 Building a hit list with the Claude MCP15:52 Attributing pipeline to network intros17:42 Why the top 20% of contributors is enough19:15 Chrome extension: ranking 300 mutual connections20:36 How relationship strength scoring works22:21 What sets The Swarm apart24:50 The three intro plays: direct, indirect, mention27:51 Lessons: batch asks monthly, train your cap table30:16 Network-led GTM: recruiting advisors for their networks32:11 Stacking warm intros with ads and outbound34:03 Suppression lists and Sequoia's top 1% vouch exercise36:10 Using network data for recruiting37:10 Will AI agents replace relationship selling?40:36 Wrap-upAbout David ConnorsCo-founder and CEO of The Swarm. Previously founded Automately (acquired by Sequoia Capital), then built network-mapping tools inside Sequoia that became The Swarm.The Swarm: https://www.theswarm.comSwarm MCP for Claude: https://www.theswarm.com/integrations/mcpLinkedIn: https://www.linkedin.com/in/connorsdavid/About the hostJonathan "Coach K" Kvarfordt, GTM AI strategist and host of the GTM AI Podcast.GTMAIpodcast.comTools mentioned: The Swarm, Clay, Claude, Claude MCP, Attio, Google Workspace, HubSpot, Salesforce, Clarify, ZoomInfoFree resource: Get the Warm Intro Engine, a copy-paste Claude skill that runs David's monthly warm intro play with your own LinkedIn exports. Link at GTMAIpodcast.com#GTMAI #WarmIntros #RevenueAI

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Marko Ladis runs an engineering team full time, consults on decision ownership, and hosts the Decision Load podcast. He is a solo operator, so he built three AI setups that handle the research, sorting, and production prep he refuses to do by hand. The rule across all of them: agents prepare, Marko decides. In this episode he walks through the real workflows, with anonymized outputs, and Coach K breaks down what GTM teams can copy.

Links and resources

Marko on LinkedIn: https://www.linkedin.com/in/marko-ladis/

Decision Stability: https://decisionstability.co

Decision Load podcast (YouTube, new episode every Wednesday morning, European time): https://decisionstability.co/decision-load

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Same customer. Same data. Same prompt. High value on Monday, low value on Tuesday. Elin Nguyen hit that wall trying to vibe-code a sales intelligence tool, and it turned her into an accidental AI researcher. She explains interpretation drift, why "don't hallucinate" and "think step by step" barely move results, and how substrate engineering got GPT, Claude, Gemini, and Grok to return byte-identical output. If you run AI inside a revenue team, this one changes how you write prompts.

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https://www.gtmaipodcast.comMark Fershteyn calls himself "a dumb sales guy with too many ideas." He ran sales at App Academy, founded Recapped, raised $8M, built a 26-person team, reached seven figures, and eventually shut it down and returned money to investors.Then AI changed the math. For his whole career, building an idea meant raising money and hiring engineers. Now he builds it himself.In this episode, Mark shares his screen and walks through:How to keep up with AI without burning out (filter for step changes)Why he starts almost every session in plan modeThe "one agent per task" rule and his 6-agent content pipelineA skill built from 300 LinkedIn posts that interviews him, drafts, and scores 5 hooksHow he structures folders and a personal wiki so AI can find everythingZealos: 5 calendars, every inbox, every DM, website visitor identification, and sequences in one appThe revenue: a $500K run rate in 6 months, solo, part-timeBuild vs. buy vs. hire, and where the software market goes nextIf you are not technical and you think this stuff is out of reach, watch this one.Chapters00:00 Intro: Mark's second time on the show01:11 From VP of Sales to an $8M-raised founder02:10 Founder tip: don't create a new category02:40 Why AI changed everything for a non-technical builder04:03 How to keep up without drowning05:14 Filter for step changes, then build something05:58 AI as playtime07:00 What you'll learn: skills and a real build08:15 Claude Code in the terminal vs. the app09:24 Use AI to use AI: start in plan mode10:26 One agent per task11:31 The prompt that builds your content agent system12:20 Coach K's rule: four rounds of feedback13:07 Treat your agent like an intern13:30 The 300-post LinkedIn skill14:08 Make every skill improve itself14:49 Building an AI CMO (plus a video editing tool tip)16:00 Folders, GitHub, and the /clients folder17:08 Hierarchy vs. graph: building a personal wiki18:58 Zealos: the super app20:39 From 20 tabs to one view21:58 Identifying website visitors and enrolling them in sequences23:05 From personal tool to product24:20 The revenue impact25:16 4 years and 26 people vs. 6 months solo26:00 UI vs. chat: everyone gets a Jarvis29:20 Build vs. buy vs. hire30:41 SMBs build, enterprises buy, the middle gets squeezed32:00 The build-happy pendulum32:37 Where to find MarkLinksMark on LinkedIn: https://www.linkedin.com/in/markfer/Mark's links: markfersh.comMastering AI: masteringai.ioZealos: zealos.ioPalmier (video editing via Claude Code, mentioned by Coach): palmier.ioRemotion (video editing, mentioned by Mark): remotion.devFree Content Engine skill: https://www.therevenueaireport.com/agent-skills/content-engine-suite

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Eli Portnoy has spent 15 years in AI across three companies. ThinkNear was acquired by Telenav, Sense360 by Medallia, and BackEngine.ai is the one he says he is not planning to sell. The premise of BackEngine: AI needs context, most company data is siloed and inaccessible, so BackEngine structures, permissions, and indexes private company data so every AI instance can use it.

On this episode Eli shares his screen and walks through how BackEngine runs 90% of its go-to-market inside Claude.

What you'll hear:

• The connector stack. BackEngine, Fireflies, Gmail, Calendar, Granola, HubSpot, Notion, Slack, Superhuman, Zoom, plus a few vibe-coded MCP servers (website stats). The SaaS tools didn't go away; they moved inside Claude.
• The scheduled jobs that survived. Founder Sales Daily Pulse (4pm outreach scan), end-of-day follow-up audit from transcripts, product-market fit review every 20 prospect calls, and weekly deal/customer health change alerts.
• Why a direct CRM connector misses deals. Context windows force the model to sample. Eli's estimate: about 30% of the relevant data gets read. Joining systems, building an index, and permissioning at the data layer fixes it.
• How to measure AI ROI when it's hard to attribute. The "30% smarter pill" thought experiment, and why scheduling turns AI from single-player into multiplayer mode.
• Three mistakes. Capabilities weren't ready (jobs needed a computer on and permissions every run), running 200 jobs nobody read, and treating adoption as a technology problem instead of change management.
• Two change-management rules. Fewer jobs with owners, and no job without a workflow attached.
• Artifacts as dashboards. Rebuilding the siloed SaaS views (customer health by tier, account health, case studies) as shareable Claude artifacts.
• Prompt vs. project vs. skill vs. plugin. Eli's 20-second taxonomy.
• State of the market. 5-10% of employees in most companies are power users; 90% use AI lightly or for emails. Causes: fear, habit, immature tooling, no formula.
• Advice for individuals. Play with it, give it context like you would a new hire, review every output, tell it to be concise.
• The next 12 months. Chat becomes the central work surface; expect non-model-provider layers (Manus, Instinct) to compete on the machinery around the model; voice and other interfaces are coming. Eli's analogy: early TV filmed radio booths.