GTM AI Podcast with Coach K and Jonathan Moss

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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

www.gtmaipodcast.com

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.

https://www.gtmaipodcast.comJoel Horwitz ran product-led growth for all of IBM, led growth at Sourcegraph, and now runs Synter, an ad platform operated by AI agents. In June he ran a LinkedIn campaign that came back at $1,800 per lead. In this episode he shares his screen and shows exactly why that happens, why it took him six months to figure out, and the workflow he uses now instead.

You'll see the one-prompt "demand capture" workflow that turns your zero-click organic keywords into exact-match paid campaigns, the LinkedIn API tier problem that quietly sends your budget to Walmart employees and BDRs, the organic-first playbook that took Synter from 0 to 2,500 followers with no ad spend, how to scope permissions for agents that can spend money, and a warning about malicious skill files.

Real screen share. Real numbers. No theory.

🎙️ GTM AI PODCAST More episodes, the newsletter, and the community: https://gtmaipodcast.com

👤 GUEST: JOEL HORWITZ Synter: https://syntermedia.ai/ Joel's DMs are open on X

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https://www.unionsquareconsulting.com

Coach K (Jonathan Kvarfordt) sits down with Eddie Reynolds, founder and CEO of Union Square Consulting and host of the Go-To-Market Science podcast, for a masterclass in go-to-market efficiency in the age of AI.

Eddie has carried a bag his entire career, spent 3 years inside Salesforce, and has now spent a decade helping CROs and RevOps leaders find the quick wins hiding in their revenue engine. In this conversation he shares the Go-To-Market Efficiency Pyramid, the exact model his firm uses to figure out what to fix first, and why AI only works when the foundation underneath it is solid.

If you're a CRO, VP of Sales, RevOps or GTM leader trying to figure out where AI actually fits, this one is for you.

What you'll learn:

• Why "what should we do with AI?" is the wrong question, and the question to ask instead
• The 4 layers of the Go-To-Market Efficiency Pyramid (fundamentals, adoption, optimization, acceleration)
• The pipeline truth test: how deals older than 2x your sales cycle are lying to your forecast
• Why you should fix your pipeline before you generate more of it
• The capacity-planning math behind the "magic number" of 200 accounts per rep
• Why AI tools fail without a defined sales process (and how to fix it)
• Centralized vs. decentralized AI, and the trap of every rep vibe-coding their own agent
• Why adoption, not the tool, is the real competitive moat

CHAPTERS00:00 Intro: meet Eddie Reynolds01:00 Eddie's story: sales, RevOps, and Union Square Consulting03:25 Why great RevOps needs sales empathy05:41 Why go-to-market is "an unscientific endeavor"07:16 The Go-To-Market Efficiency Pyramid (overview)09:13 Layer 1: Fundamentals (ICP, personas, process, data)10:34 Layer 2: Adoption (the hardest layer)11:01 Layers 3 & 4: Optimization and Acceleration (where AI lives)12:05 The pipeline truth test: deals 2x your sales cycle are dead14:14 Fix your pipeline before you generate more15:13 Capacity planning and the "magic number" of 200 accounts16:00 Why AI tools fail without a defined sales process19:02 Running a go-to-market / pipeline council24:46 The #1 pattern CROs miss: focus26:41 Why AI strategy takes time (no magic button)30:55 Centralized vs. decentralized AI (the vibe-coding trap)32:15 Professional vs. tinkerer: the "3 hours vs. 25 hours" story33:35 The wrong question every leader asks about AI36:27 Where to find Eddie and his frameworks37:01 The Salesforce story that went viral (260K views)40:00 Wrap

CONNECT WITH EDDIE REYNOLDSFrameworks: https://www.unionsquareconsulting.com/frameworksPodcast: https://unionsquareconsulting.com/podcast/LinkedIn: https://www.linkedin.com/in/edwardreynolds/

#GTMAI #RevOps #AIinSales #GoToMarket #RevenueLeadership #Salesforce #PipelineManagement #SalesStrategy #CRO #ArtificialIntelligence

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Most companies bolted AI onto their old systems and got more noise, not more intelligence. Maddie Bell figured out why, and what to build instead.

Maddie is the co-founder and CEO of Synapsa, and she came to AI the long way: from running billion-dollar brands as a brand director at Procter & Gamble. "From Soap to SaaS," as she puts it.

In this episode she opens up the actual repo and walks Coach K through the AI go-to-market system her team built, including a brand site that is now 100% AI-coded.

We get into why buyers eye-roll the second they smell AI marketing, the three-layer pyramid that decides whether your AI actually works, the five layers of an AI GTM system you can copy, and the build-versus-buy line that 40% of AI products are getting fired for crossing.If you lead go-to-market and you are tired of "we're an AI-first company" meaning nothing, this one is for you.

⏱️ CHAPTERS

00:00 Intro: the husband-and-wife AI startup

02:14 From Procter & Gamble to AI CEO ("Soap to SaaS")

03:49 The big idea: meetings are the atomic unit of business growth

05:46 The thesis: instant, intelligent, and invisible to buyers

06:10 Why brand-building fundamentals just 10x'd in value

08:18 The Frankenstack problem: AI created more noise, not intelligence

08:47 Why your humans are the real moat

09:56 Why hallucinations stopped being the real problem 12 months ago

10:43 The Pyramid of AI Effectiveness: Protocol, Personality, Persuasion

11:54 The personality nobody admits buyers want

12:54 Persuasion: moving a buyer from need to decision

14:51 The messy middle where the real work lives

17:00 What this means for you if you are not a builder

21:13 Why buyers eye-roll AI marketing (and the data behind it)

23:55 Anatomy of an agentic system

24:46 "It's a folder. We call it a repo to sound cool."

25:28 Load-bearing content and progressive disclosure

33:39 Where to actually start (an empty folder)

34:42 The 5 layers of an AI GTM system

35:45 The 100% AI-coded brand site

37:57 The content rubric that rejects off-brand work before a human sees it

38:17 The surprise final layer: you become the bottleneck39:16 The sales side: signals, guided selling, and prep

41:09 Build vs. buy: how to pick your hard

45:50 Why 40% of AI products are getting fired

47:20 How to pressure-test any AI vendor before you buy

TAKEAWAYS

1. Good AI for go-to-market is three things: instant, intelligent, and damn near invisible to buyers. If buyers can feel the AI, you built it wrong.

2. AI effectiveness is a pyramid: Protocol (can it follow your playbook), Personality (people hate no personality more than a bad one, so mirror the buyer), and Persuasion (move them from need to decision). Most teams stop at protocol.

3. There are five layers to a real AI GTM system: a load-bearing knowledge base, a design system, a content engine, a self-improving idea layer, and a sales complement. You build them in order.

4. Single-player output with human approval (decks, docs, one-pagers) is now buildable yourself. Multiplayer, buyer-facing, real-time orchestration is where you should still buy.

5. 40% of AI products are getting fired for being unreliable, insecure, or unable to bring humans in at the right moment. You are hiring a system. Don't hire one you'll have to fire.

Quotes

"Meetings are the atomic unit of business growth." — Maddie Bell"

In our excitement to scale AI, many companies overlaid it onto traditional Frankenstack systems. And rather than creating more intelligence, we've created more noise." — Maddie Bell

"It's a folder. We call it a repo because it makes us sound cool in front of our friends." — Maddie Bell

Maddie Bell, Co-founder & CEO, SynapsaWebsite: https://synapsa.aiLinkedIn: https://www.linkedin.com/in/maddiebell/#GTMAI #AIStrategy #RevenueLeadership #AIagents #GoToMarket

https://www.gtmaipodcast.comStop running siloed GTM experiments. Start building a flywheel.Dan Rosenthal went from a failed Cambridge PhD application to $5M in biotech sales with zero systems — and that frustration became the fuel behind Workflows.io. In this episode, Dan walks me through two complete playbooks his team uses with YC companies, Fortune 500 logos, and everyone in between.We cover the GTM Flywheel (how to connect every channel so they feed each other) and the ABM Playbook (what to do when you have fewer than 10,000 target accounts and can't afford to miss). This is one of the most tactical, systems-level conversations I've had on this show.What we get into:Why AI is raising the bar for GTM — and what to do if you're below itThe GTM Flywheel: traffic, lead capture, nurture, and conversion all connectedWhy your best-performing content should become your ads (and vice versa)The ABM infrastructure that helped 3 reps book 6 meetings in one dayICP modeling done right: tiering based on concentric circles, not point systemsSignals: first-party, second-party, third-party — and why most people use them wrongAwareness scoring from "Identified" to "Selecting" — and why it changes everythingClaude Code and the future of GTM automation (real talk on what's hype vs. ready)Why old-school sellers with new-school systems are the most dangerous combo in B2BTimestamps:00:00 — Intro & Dan's background (biology master's to sales to Workflows.io)03:20 — What makes Workflows.io different from Clay agencies07:30 — The GTM Flywheel Playbook walkthrough19:00 — AI's real role: web research agent for lead qualification22:50 — Website conversion and de-anonymized visit tools25:00 — When the GTM Flywheel doesn't fit: entering ABM mode33:00 — Building the ICP model the right way37:00 — TAM mapping: why Clay alone isn't enough42:00 — Signal tracking: 3 categories and which ones actually move the needle45:00 — Awareness scoring and the client story that says it all49:00 — Infrastructure vs. quick wins: you need both51:00 — Workflows.io services breakdownConnect with Dan:Website: https://www.workflows.io/LinkedIn: https://www.linkedin.com/in/dan-m-rosenthal/More from Coach K:GTM AI Podcast: https://www.gtmaipodcast.com

https://www.gtmaipodcast.comYour AI conversations are sitting in someone else's vault. In this episode, 20-year GTM operator John Williams shows exactly how he took his back: archiving every chat locally, running AI models offline on his own laptop, and setting hard guardrails on what his agents can buy and agree to without him.This is the GTM and AI Podcast, where real operators show the receipts. One rule in this kitchen: you actually have to cook.WHAT JOHN SHOWS LIVE ON SCREEN:• Chat Archive: a free, open-source browser extension that exports any AI conversation (Claude, ChatGPT, Gemini, Groq, Perplexity) to JSON or markdown, with zero outbound calls. Nothing leaves your machine.• Exporting a full Claude conversation and continuing it inside Groq with full context intact• Why he runs local models with Ollama, and how open-source models let you switch models mid-conversation without losing context• Agent Commerce: an open spec for what your AI agents can spend and what terms they can accept on your behalf• The AI Acceptable Use Policy: an open-source starting point so shadow AI doesn't run your company• How to vet any open-source AI tool before you trust it (the one question: would your security director approve?)TIMESTAMPS:00:00 - Welcome to the kitchen: the one rule of this podcast01:00 - Who is John Williams? 20 years in GTM, 5 as an independent operator02:30 - Why your GitHub repo is the new resume04:00 - The AI Acceptable Use Policy: fixing the shadow AI problem05:30 - Agent Commerce: spending limits and guardrails for your AI agents09:00 - Chat Archive: why owning your conversation history matters14:00 - Building a digital twin from a year of AI conversations16:00 - Local models 101: moving past being a "prompt jockey"19:00 - GPU brownouts and token authority: why local inference is your backup plan22:00 - LIVE DEMO: exporting a Claude conversation locally25:00 - Porting full context from Claude into Groq, zero loss28:00 - Token economics: the W-2 cost didn't disappear, it moved31:00 - Data portability use cases: audits, regulated industries, federated intelligence36:00 - OpenClaw: the power and the security risks of autonomous agents39:30 - Where to find John + why he'd apply for his next job in publicFIND JOHN WILLIAMS:GitHub: github.com/fxops-aiHugging Face: huggingface.co (johnwilliamsatl)If this episode saved you from losing a year of AI conversations, subscribe and come cook with us next week.#GTMAI #AIAgents #LocalAI #DataOwnership #Ollama #GoToMarket

https://www.gtmaipodcast.com

He stopped hiring for RevOps tasks and started building Perplexity skills instead. Here's exactly how.

In this episode of the GTM AI Podcast, Coach K sits down with Nate Follen, Head of Enterprise Ops and Systems at Perplexity, to walk through the three AI skills his team actually runs every day and every week. Nate helped scale RevOps at Ramp, advised teams at Momentum, and now leads the internal go-to-market systems engine at Perplexity. He doesn't talk theory. He shares his screen and shows the real workflows.

Key topics covered:

• Why Nate now asks "do I have to hire for this?" before adding headcount, and where the answer is still yes (hint: the Salesforce admin role isn't dead)

• The Voice of Customer dashboard he built with two prompts, an API key, and live-refreshing call transcripts, that tells the product team not just what customers said, but what to do about it

• The weekly RevOps deck-prep skill that pings the sales team in Slack, pulls live data from Snowflake via Claude Code, checks Linear, and assembles a deck that used to take an hour

• The CRM hygiene system that cleans account ownership and Salesforce hierarchies nightly, builds Polytomic data models without logging into the tool, and DMs Nate every error with a severity score and a fix

• Why orchestration across 400+ connectors beats single-model lock-in, and where Perplexity sits next to Momentum and Salesforce instead of replacing them

• The mindset shift: from "find the two things to focus on" to running dozens of projects in parallel with a team of agents

If you lead revenue operations, enablement, sales, or marketing, this is the tactical breakdown of what AI-run GTM systems actually look like inside one of the fastest-growing AI companies in the world.

Resources mentioned:

• Nate Follen on LinkedIn: https://www.linkedin.com/in/follen/

• Perplexity: https://www.perplexity.ai

• Momentum.io (call recording and transcript analysis)

• Salesforce, Slack, Linear, Snowflake, Claude Code, Polytomic, Hightouch

• GTM AI Podcast & Newsletter: www.gtmaipodcast.com

Get the free lead magnet from this episode, The Perplexity RevOps Skill Stack, with the exact prompts and build steps: www.gtmaipodcast.com