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.comhttps://www.fluint.comIn this episode of the GTM AI Podcast, I sit down with Nate and John, co-founders of Fluint, for a kitchen-deep look at how they built Ollie, an omni-agent that captures tacit sales knowledge and surfaces it across an entire enterprise sales org. We cover:The tacit knowledge problemWhy a small pocket of reps wins 50-60% of pipeline while everyone else sits at 17-19%, why "clone your top rep" has been said for 15 years and never delivered, and what happens to your $30M deal architect when they take a new job two weeks from now.The implicit signal exampleA real story of two reps, two POC readouts, two procurement follow-ups (one at 9pm Friday, one Wednesday during business hours), and why top reps will negotiate completely differently on the same data while average reps miss the signal entirely.The photo vs. video architectureWhy most AI tools treat data as a snapshot (LLM context = one photo), and why Fluint's event-driven, time-series architecture treats it as a video. The 10-Second Tom analogy from Fifty First Dates that explains why LLMs alone cannot solve this problem.The ML + LLM stackJohn walks through the architectural decision: ML for pattern recognition and judgment layer, LLM for human-to-human interaction. "Using the right tool for the right job." This is the most underrated decision in enterprise AI right now.Ollie's omni-agent designWhy one AI teammate beats 130 task-specific agents. The 75% of users who gender their AI. The trust dynamics that make a sales rep follow an agent's advice when it runs counter to the playbook.The racehorse modelHow Fluint runs a global baseline model and a customer-specific model in parallel, evaluates them nightly, and promotes the winner. Continuous evaluation as the moat.The "data is a product of people" answerJohn on why perfect data is a logical fallacy and what to do instead. The single line that changes how you approach AI readiness.Real outcomes+$28K added to ACV per team per year. 32 days off the median sales cycle. The maturity curve from Q1 (win existing deals with less discount) through year-end (win deals you would have lost).GUESTSNate, Co-founder & CEO, Fluint (the "second brain")Repeat enterprise sales leader and repeat founder. Built Fluint from a problem he could not solve as a sales leader. Author of two books on tacit knowledge and executive sound-bite communication.John, Co-founder & CTO, Fluint (the "first brain")Technical co-founder. Builds the systems that turn Nate's crazy ideas into shipping product. Specialty: event-driven architectures and ML-as-judgment-layer for enterprise sales.LinkedIn Nate:https://www.linkedin.com/in/natenasralla/Linkedin John:https://www.linkedin.com/in/jon-crawley-3797a8100/Blog: fluint.io/blogJohn's recent technical guide: building enterprise AI agents (just published on the Fluint blog)GitHub repo (DIY resources for time-series-data agent architecture): linked from blog

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Most AI agents are "good enough.. until they're not." Julia MacDonald, SVP of GTM at Haize Labs, shows how she sells 99% reliable AI agents to banks, insurers, immigration lawyers, and healthcare orgs with a 2-person GTM team. Plus the use-case filter that decides what's worth building in the first place.

Deep dive:In this episode of the GTM AI Academy Podcast, I sit down with Julia MacDonald, SVP of GTM & Solutions at Haize Labs, the only company I have seen solve the AI agent reliability problem at the enterprise level. We cover:

The "Mostly Fine" trap that kills enterprise AI deals

Why your agent telling Customer A "no discount" and Customer B "20% off" is a GTM problem, not a tech problem, and why the enterprise buyer is pricing the worst 1% of conversations, not the average.

The hypothesis-first use case filter Julia uses to pick where to play

Document-heavy. Cannot be outsourced cheaply. Right vs. wrong answers. High stakes. Human accountability. The almost-mathematical criteria she runs every use case through before her 2-person GTM team touches it.

The Code of Conduct + Supervisor Models + Adversarial Red Teaming stack

The actual methodology Haize Labs uses to engineer 99% reliability into voice debt collection agents, immigration paralegal agents, and insurance claims agents. Includes how their red teaming hit 15,000 adversarial queries on a single agent.

The 2-person AI-native GTM motion

Julia's full prospecting flow: hypothesis → use cases → companies → executives → enrichment in Clay and OpenAI → outreach in Dripify. Plus the pre/post-call brief workflow that makes her "smarter today than I was six months ago" on every call.

The honest gaps Julia named

What Haize Labs is not yet doing on closed-loop attribution, and why that's a real tradeoff for any 2-person GTM team.

Sneak peek of "Lossless RAG"Julia teases their new hallucination-free retrieval product (we go deeper on this in next week's episode).

GUESTJulia MacDonald, SVP of GTM & Solutions, Haize LabsLinkedIn: https://www.linkedin.com/in/juliamacd/Haize Labs: https://haizelabs.com

https://www.gtmaipodcast.com for free goodies!

https://www.wrench.ai

To connect with Dan-- https://www.linkedin.com/in/danbaird/

Most "personalized" AI outreach isn't personal at all. It's a name swap on a template.And that's why your reply rates are flat even though your stack tripled.

This week on the GTM AI Podcast, I sat down with Dan Baird, the founder and CEO of Wrench.ai (and one of the original co-founders of Lovesac). Dan has been building in AI for nine years, and he runs one of the most quietly impressive ML+LLM stacks I've seen in the GTM space. He calls it the "RoboCMO."

We got into:

→ Why personalization and relevance are not the same thing (and why most teams confuse them)

→ Why most sales personas are built in conference rooms, not from data

→ The 27-million-person study on what actually predicts a top-performing seller

→ Why LLMs alone will never solve your outbound conversion problem

→ How the "early adopter vs. late adopter" split changes your messaging completely

→ The 2-axis "Relevance Map" that shows you what your buyers want to hear (that you're not saying)

→ What he thinks about Open Claw, agent-to-agent commerce, and where this is all heading

If you run revenue, marketing, or sales enablement in 2026, this one will change howyou think about your AI stack and your outbound playbook.

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📥 FREE LEAD MAGNET INSPIRED BY THIS EPISODEThe Personalization-vs-Relevance Audit: 12 questions that score whether your AIoutreach is built to convert or just to send. Takes 6 minutes.👉www.gtmaipodcast.com

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00:00 Cold open + Open Claw take ("I'll let other people make the mistakes at scale")

04:09 Who is Dan Baird? (Lovesac, ConAgra, and the 3D-printed Allosaurus skull)

07:31 What problem Wrench.ai solves (and why it won't go away)

11:42 How Wrench is different from Clay and the AI SDR category

15:30 LLMs vs. machine learning: the cake recipe analogy

19:01 Live demo: the Relevance Map and how it scores you in real time

26:31 Personalization is not relevance (the core distinction)

27:31 The 27-million-person study on top-performing sellers

30:50 Why most personas are BS, and what to build instead

35:00 Where AI-to-AI commerce is heading (and what to do about it)

39:25 The "AI slop" problem and how to actually differentiate

41:30 Wrap💬

SUBSCRIBE for weekly GTM AI breakdowns, frameworks, and tool reviews from insidethe room with the operators actually shipping this stuff.#GTMAI #AIOutreach #SalesEnablement #RevenueLeadership #AIStrategy #DanBaird #Wrench

www.gtmaipodcast.comwww.fullcast.comMost marketers are using AI like a typewriter. Amy Osmond Cook is using it like a band.In this episode of the GTM AI Podcast, I sit down with Amy Osmond Cook, CMO of Fullcast and one of the sharpest marketing leaders in the AI-native SaaS world. Amy walks us through how Fullcast's acquisition of Copy.ai rewired her entire go-to-market motion in 90 days. We talk about how her team is producing more, spending less, and getting found by the LLMs that are quietly replacing Google search.A few of the stats Amy dropped that you do not want to miss:5X productivity increase after standing up Copy.ai as the AI agency layerHundreds of thousands of dollars in salary savings, redeployed into growth10 points of AI visibility gained in a single monthCopy.ai's own site: zero to 84 domain authority in 3 years with ZERO human-written contentWe also get into Amy's incredible origin story (single mom in her parents' basement, a $10K tax-return bet against her husband, four acquisitions later, now co-founding the first AI-native GTM platform), the death of "Hi {first_name}" marketing, FAQs as the AI visibility cheat code, and where agentic GTM is heading next.If you lead marketing, run go-to-market, or are wondering whether your agency contract is about to become a line item you cut, this one is for you.⏱️ Chapters00:00 Cold open and introductions01:00 Inside the Fullcast acquisition strategy (Copy.ai, Ebsta, Atrium, Commissionly)03:00 Amy's origin story and the $10K bet that started a 15-year career06:00 Fullcast's three-pillar bet on AI-native GTM08:00 Fire your agency: the AI agency thesis11:00 Why FAQs and EEAT are the secret sauce of AI visibility14:00 Personalized landing pages, demo environments built in one hour17:00 Writing workflows that actually capture the mind of a sophisticated writer20:00 Pressure-testing the Fullcast blog live on the show23:00 The Copy + Ebsta + LLM context stack24:00 A day in the life: marketing four years ago vs. today25:00 Why "Hi {first_name}" is finally dead28:00 The new rule: if it is AI, say it is AI30:00 The agent-to-agent future and how marketers must adapt32:00 WebMCP, agentic search, and writing content for the bots🔗 About AmyAmy Osmond Cook is the CMO and co-founder of Fullcast, the first AI-native go-to-market platform. She has scaled marketing through three acquisitions (Simplus to Infosys, PathologyWatch, Onboard to Conservice) and previously ran Stage Marketing for 15 years. She holds a PhD in organizational rhetoric and taught writing and rhetoric at the university level for 25 years.🔗 ConnectFollow Amy: https://www.linkedin.com/in/amyosmondcook/Subscribe to the GTM AI Newsletter on SubstackLearn more about Fullcast: https://fullcast.com#GTMAI #AIMarketing #CopyAI #Fullcast #AIAgents #AIVisibility #RevenueOperations #SalesPerformance #CMO #MarketingLeadership

www.gtmaipodcast.com For more from Ryan: https://superhumanrevenue.beehiiv.com/p/hiring-an-ai-transformation-leadAnd Ryans Linkedin: https://www.linkedin.com/in/ryan-staley/Ryan Staley built a division from zero to $30M with four salespeople and no marketing budget. He's taught 800+ CROs how to use AI. On this episode, he pulled back the curtain on the agentic operating system he built to run his entire business — from CEO decision-making to content creation to pipeline management — using Claude Code, Obsidian, and API-connected tools like Fathom and HubSpot.This wasn't theory. Ryan screen-shared his actual system, showed real outputs, and walked through the folder structures, memory layers, and agent orchestration that let him operate "at the speed of thought."

www.gtmaipodcast.comArup Linkedin: https://www.linkedin.com/in/arupchakravarti/Most Clay users spend $2K/month to build prettier ZoomInfo clones. Arup Chakravarti is doing something different.Arup is a 20-year RevOps and Enablement veteran, Fellow at the Institute of Sales Professionals, and one of the sharpest operators in the UK GTM space. He spent the last two months going deep on Clay, not as a GTM engineer, but as an enablement brain. The result is a psychographic prospecting system that identifies sales leaders who actually care about developing their teams, not just ones who match a firmographic ICP.In this episode, Arup shares his live Clay build on screen. You'll see:How he built a UK Healthcare Providers list with confidence-scored strategic priority analysis (green/amber/red), pulled from the last 10 articles per company, parsed in JSON, and filtered into meaningful themes.The "PDP Advocacy" column. A psychographic classifier that scores every sales leader as a Strong / Moderate / Weak advocate for professional development based on their LinkedIn profile, posts, comments, and likes. This is the column most Clay users never build because they don't have the enablement lens to know it exists.The iteration that unlocked it. Arup initially scoped the prompt too narrowly ("advocate for the sales function") and broadened it to "advocate for professional development." One word change. Massively bigger qualified pool.Clay's hidden edge: the Google Maps integration that finds mom-and-pop businesses (lawyers, solicitors, local firms) who aren't on LinkedIn at all. If you sell to local SMBs, this is the unlock.Honest data: Clay vs. LinkedIn for employee count accuracy. Spoiler: Clay is closer to actual reported figures than LinkedIn for private companies, because LinkedIn inflates headcount through tagged resellers and influencers.Arup also shares his Clay difficulty rating (middle of the pack, "a little fiddly"), what he had to learn on the fly (JSON structures), and why Clay University is the free onramp most people skip.The throughline of the whole episode: the quality of your Clay output is capped by the domain expertise behind your prompts. A GTM engineer can build a bigger list. An enablement vet, a CS leader, or a product marketer can build a smarter one, because they know which soft signals matter.Connect with Arup: https://www.linkedin.com/in/arupchakravarti/Connect with Coach K: https://www.linkedin.com/in/jonathankvarfordt/CHAPTERS:00:00 — Intro and reunion with an old enablement friend02:25 — Arup's background: 20 years in RevOps, enablement, and the North London pivot04:05 — What you'll learn: Clay for GTM outreach from an enablement lens06:50 — How Arup describes Clay: the online spreadsheet that operates on itself09:35 — The Google Maps integration nobody talks about (mom-and-pop targeting)11:40 — The use case: UK Healthcare Providers + the ISP case study12:45 — The psychographic targeting breakthrough15:45 — Future trend: LinkedIn political drift and prospecting risk18:10 — LIVE: Walking through the UK Healthcare table19:20 — Pre-built Clay AI columns (the ones with the tiny hat logo)20:50 — JSON parsing and pulling thematic insights from the last 10 articles22:30 — Strategic Priorities with confidence scoring (green/amber/red)23:35 — Building the Sales Leaders sub-table24:00 — Data accuracy: Clay vs LinkedIn for private companies25:30 — The PDP Advocacy column (the one nobody builds)26:00 — Structured prompting inside Clay27:30 — Coach's take on context-in-prompt vs prompt bloat30:25 — Live email generation from the full signal stack31:10 — Email walkthrough: "Strengthening talent via strategic partnerships"31:40 — The honest answer on results (Arup hasn't operationalized yet)32:30 — Clay difficulty rating on a 1-10 scale33:55 — Wrap-up, next roles, and the 6-month follow-up pact

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www.spara.co

To connect with David: https://www.linkedin.com/in/davidwalker4/

David Walker is the co-founder and CEO of Spara, the conversational GTM agent platform powering inbound motions for some of the fastest-growing B2B companies.

In this episode, he makes a case most GTM leaders are dodging: for the last 20 years, your revenue was capped by how many humans you could put on a phone.

That constraint is gone. And if your front door is still a static website with a "Contact Us" form, buyers are already treating you like a dead end.

David breaks down:

Why the old front door (your website plus an SDR team) no longer matches how buyers actually buy

The "10X Horsepower" thought exercise that reframes AI strategy from "automate a task" to "redesign the motion

"Why 99% of Spara customers now lean INTO telling prospects it's AI (up from 50/50 at launch) and why buyers get MORE direct, not less

Live demos: inbound web form to phone call in 2 seconds, agentic email that replies back at 11pm, prompt tuning with an AI that tunes your agent

The Kayak vs Wedding Planner filter for deciding which GTM moments should be human and which should be AI

Real customer results: 3X MQL rate, 80% drop in unqualified leads, doubled sales team headcount BECAUSE the agent worked

Why narrow point-solution demos look sexier but platforms are what actually move KPIs (A/B testing tone and prompt down to conversion)

Where the roadmap goes next: agent-to-agent selling, co-pilot reps, and browser/computer-use agents that actually run the demoIf you are rethinking inbound, debating whether to replace or augment your SDR team, or trying to figure out what a "GTM AI motion" actually looks like in 2026, this is the most direct, founder-grade breakdown you'll get.

Timestamps:

00:00 — Intro and who David is beyond the resume

02:00 — The one takeaway: if you're not using conversational agents, you're missing the boat

03:00 — The core problem: GTM has been capped by sales team capacity for 20 years

04:15 — Why the old front door is no longer enough

04:45 — What Spara is and who it's built for

05:00 — Clay vs n8n vs Spara: how the market splits up

06:00 — Agent-to-agent selling and the new top of funnel

07:00 — LLMs as the new discovery layer and what that means for your website

08:30 — Live demo: web form fills to phone call in 2 seconds

09:45 — Why 99% of customers now say "this is AI" (up from 50/50)

11:00 — Agentic email: replying at 11pm and progressing the deal overnight

13:00 — Knowledge retrieval (RAG), prompting inside the product, and why "answering questions" is the easy part

15:00 — Optimizing agents: prompt tuning, red-teaming, simulated personas

16:00 — Post-sale and PLG upsell workflows across chat, email, and voice

19:30 — The roadmap: sales rep assist, co-pilot agents, and browser/computer use for live demos

22:30 — What Spara, Clay, and Agency do for David internally

24:00 — The mistake David sees most: "I just want to automate one small piece"

25:00 — The 10X Horsepower thought exercise

28:00 — Kayak vs wedding planner: the hybrid motion filter

31:00 — Case study: 3X MQLs, 80% drop in junk

32:00 — Case study: doubled sales team BECAUSE the agent worked

33:00 — Why point-solution demos mislead and platforms move KPIs

34:45 — Close and where to find David