GTM Engineer School Podcast

About GTM Engineer School Podcast

In GTM Engineer School Pod, Jared and Matteo interview seasoned GTM Engineering operators to identify first principles, best practices, tooling, and challenges of building AI-driven workflows. gtmengineerschool.substack.com (https://gtmengineerschool.substack.com?utm_medium=podcast)

Publisher

Jared & Matteo

Category

business · technology

Language

en

Explicit

No

First episode

9 Jun 2025

Latest episode

3 Jun 2026

Latest episodes

18 episodes in the feed.

1. 

   3 Jun 2026

   S2E7: "Be the Olympist of Your Field" | Guillaume Cabane

   Listen now | The founder of HyperGrowth Partners unpacks why hybrid GTM teams beat full automation, why true AI adoption stays under 1%, and why the next decade belongs to the Olympist of every field. About our guest — Guillaume Cabane Guillaume “G” Cabane is the founder and general partner of HyperGrowth Partners, where he and his team work hands-on with companies like Ramp, Neon, Zapier, AirOps, n8n, and Attention. He’s the recognized godfather of GTM engineering — pioneering the Clearbit reveal loop, personalized outbound, and the experimentation-as-system playbook before any of it was standard practice. Before HyperGrowth, G was VP Growth at Drift, Segment, and Gorgias, helping each one scale through multiple step-changes in revenue, and most recently served as interim CMO at Ramp through their run from $10M to over $1B in ARR. Core Takeaways * Hybrid GTM Beats Full Automation: G has yet to see a single scale-up successfully operate without a GTM team. He tested full automation against a hybrid setup (humans + AI) at Ramp himself, and failed to beat the hybrid baseline. Other people brought him their prompts and also failed. The AI-replaces-your-SDR-team posts are hype; SDR and BDR teams are still in active hiring across his entire portfolio. * True AI Adoption Stays Under 1%: Most marketing teams have not done any meaningful AI work beyond a GPT prompt or two. When G builds something for them and shows the output, they’re happy with the output but don’t want to learn how to build it themselves. The technologists who actually compound value with AI are a tiny fraction — G estimates less than 1% of the population — and the gap between them and everyone else is going to widen every quarter. * SaaS-for-SaaS Is the Risky Bet — Tech-for-Non-Tech Stays Safe: Build versus buy is shifting back to build. Niche subscriptions are getting harder to justify when you can vibe-code the same thing in an hour. SaaS sold to other software companies is the riskiest position — either LLMs start buying for them or enterprises start building in-house. Selling technology to non-tech companies (Ramp is the canonical example) stays safe; those buyers won’t suddenly start coding. * Be the Olympist of Your Field: There won’t be hundreds of millions of Python developers in the future. There will be a few thousand master-level specialists who can beat the best LLM in their narrow domain. The path for younger talent is to pick a niche, dedicate ten years from age 15 to 25, and become the world-class expert. Most Olympians are out of college by 25. Why would tech be different. Top Quotes “I tried beating that hybrid setup with a full auto and I failed. And other people have come to me and gee, there must be a way to, we can improve the prompts. And they have failed.” “True AI adoption, in the sense that you guys in this audience thinks about it, is going to be limited to a very small percentage of the population. I wouldn’t be surprised if it’s less than 1%.” “I think SaaS for SaaS is extremely risky because either like LLMs are going to buy from that land or because large enterprise and mid markets are going to stop buying and like just start building.” “You’ve got to be the Olympist of your field. That is how you survive. That is how you win.” Referenced Tools and Resources * AI & Building: Claude Code (referenced throughout as “Open Claude” and “Cloud Code”), GPT * Workflow Automation: Zapier, n8n * Content Production: AirOps * Data Infrastructure: Salesforce (via MCP), vector databases * Case Studies: Ramp (tech-for-non-tech archetype), Manifest (legal AI), Netic (applied AI in real-world spaces) Timestamps * (00:00) Cold open and intros * (04:05) What G’s three-and-a-half years at Ramp changed about GTM engineering * (06:45) What has and hasn’t changed about the growth team in 2026 * (08:30) The AI-replaced-our-GTM-team posts are hype * (10:30) Hybrid wins, full automation loses — and G tested it himself * (11:35) Is the GTM Engineer role being diluted, or is it the same person with a new title * (14:30) Why true AI adoption stays under 1% of the population * (17:45) The two societies splitting around AI and how fast the gap is widening * (20:40) When everyone’s bot acts in their name, response rates collapse across every channel * (24:30) The effective craziness framework — tech love plus rigor plus creativity plus psychology * (28:10) What happens when LLMs start buying from LLMs * (31:50) Have you given Claude Code your credit card yet * (35:30) Are skills the new IP — and where defensibility goes * (40:00) Build versus buy is shifting back to build * (43:35) Why specialist agents still need specialists * (46:30) 2026 is the year you either build or step off the progress train * (50:30) SaaS for SaaS is the risky bet; tech for non-tech stays safe * (52:55) Trust the model output instead of needing to know everything * (54:30) Be the Olympist of your field * (56:50) What G would tell college graduates today Where to Find Guillaume * LinkedIn (https://www.linkedin.com/in/cabane/) * HyperGrowth Partners (https://www.hypergrowthpartners.com/) Where to Connect with Jared & Matteo * Jared Waxman, GTM Engineer School Co-founder: LinkedIn (https://www.linkedin.com/in/jwaxman/) * Matteo Tittarelli, GTM Engineer School Co-founder: LinkedIn (https://www.linkedin.com/in/matteo-titta/), X (https://x.com/matteo_titta), Website (https://genesysgrowth.com/), Newsletter (https://newsletter.genesysgrowth.com/) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gtmengineerschool.substack.com (https://gtmengineerschool.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

2. 

   3 Jun 2026

   S2E8: "Inbox Full, Mailbox Empty" | Kris Rudeegraap

   Listen now | The co-founder and co-CEO of Sendoso unpacks the full ZIRP-to-agents arc, why physical mail still cuts through in an AI-saturated world, and how to plan teams when capability replaces headcount. About our guest — Kris Rudeegraap Kris Rudeegraap is the co-founder and co-CEO of Sendoso, the sending platform that lets revenue teams trigger physical gifts, direct mail, and branded swag right from their CRM, enrichment tools, and automation workflows. Sendoso has raised over $150 million (including a $100 million Series C from SoftBank Vision Fund), operates in 14 countries, and is trusted by revenue teams at Gong, Verkada, and Meta. Before founding Sendoso in 2016, Kris spent about a decade in software sales as an account executive at Talkdesk and other startups — where he was already hand-packing swag boxes and going to the post office on lunch breaks. The aha moment came when he saw the gap between CRM data and physical sending, and built the platform to close it. In February 2026, Sendoso’s Clay integration went live, making personalized physical sends as composable as any other Clay action. Core Takeaways The Full ZIRP Arc, Lived From Both Sides: Sendoso scaled the headcount-heavy model during ZIRP — over $150M raised, around 400 employees, a 50-person SDR floor. Then the market corrected and Kris had to rebuild with a fraction of the team, an agency layer for outbound, and AI agents handling what dozens of people used to do. Kris is one of the few founders who scaled the old way first, then publicly rebuilt — so the playbook he shares is earned, not theoretical. Ask “Who Would You Hire?” Instead Of “What Should We Automate?”: When Sendoso started building internal agents, the framing of “what should we automate?” produced a workflow list, not an investment-grade priority list. Reframing it as “if you could hire somebody today, what would they do?” turned the same conversation into employee-led role design. Field marketing wanted an associate to run 50 events — Sendoso shipped a field marketing agent. Account management wanted contract renewal busy-work eliminated — Sendoso shipped a contract analyst that pre-populates the next renewal from the last. Leadership wanted real-time win-loss instead of quarterly spreadsheets — Sendoso shipped a clickable analyst built on Gong, email, and Salesforce data. Capability Count Replaces Headcount As The Planning Unit: The old math was head-count × ramp-time × quota = pipeline. The new math is tasks × capability per agent + the humans who run them. Kris’s framing: capability count, not headcount. Forecasting changed too — instead of multiplying SDR seats by meeting targets, the new model accounts for how many Clay tables and plays are generating pipeline, which is harder to model but more honest to how work actually happens now. Inbox Full, Mailbox Empty — Why Direct Mail Becomes The Anti-Saturation Channel: Email saturation is structural and accelerating. Kris’s framing: he’s not anti-email, he’s anti-email-only. The omni-channel buyers’ journey includes email, social, ads, direct mail, phone, and field — and direct mail is the physical channel that AI can’t cheaply replicate. Sendoso’s smart-delivery agent already hits 95%+ confidence on whether to ship to home or office, and the Clay integration that just launched makes personalized sends a button-press inside any GTM engineer’s table. Top Quotes “I am not anti-email, but I’m anti-email only.” “if you could hire somebody today, what would they do? And that’s unlocked a lot of ahas for us because they’re like, ooh, I’d hire another field marketing associate for the 50 field events.” “you really need to think about like, what is the capabilities and the capacity of, of these agents, but also the employees that are kind of running them or reporting into them.” “the contract effort was like a hour long busy work and some of these come managers were doing, know, dozens and dozens of these renewals a week.” Referenced Tools and Resources Outbound + orchestration: Clay (https://www.clay.com/) (with the new Sendoso integration (https://www.clay.com/integrations/data-provider/sendoso)), UserGems Workflow automation: N8N, Cloud Code Agent infrastructure: Databricks, LangChain, AWS Bedrock CRM and data: Salesforce, Gong (call transcripts ingested by the win-loss analyst) Internal interaction: Slack (primary surface for non-builders to talk to agents) Sending platform: Sendoso (https://sendoso.com/) — Kris’s company, the gifting and direct mail platform behind the entire conversation Timestamps (03:07) Welcome to S2E8 — Matteo’s intro to Kris and the full transformation arc at Sendoso (05:36) Jared opens — what did the first version of Sendoso look like, and who was the first customer (05:59) Coffee Sender — the $5K Upwork project that became Sendoso (07:47) What’s stayed the same in GTM over the last decade (09:56) Hype vs reality — where GTM engineering is genuinely a step change (10:29) The Sendoso SDR redesign with Clay and UserGems — taking one step back to take ten forward (12:17) What changed when you went from 50 SDRs to AI-augmented hybrid roles (14:06) How forecasting changed when capability replaced headcount in the model (16:16) Where Sendoso’s narrative still holds — direct mail as the anti-saturation channel (16:46) “I am not anti-email, but I’m anti-email only” — the omni-channel thesis (18:05) Peak Sendoso — what the GTM motion looked like at 400 employees (20:55) The hardest decisions during the downsizing — and advice for founders sitting where Kris was in 2022 (23:00) How the company structure transformed — GTM engineering as the new core (24:25) Early wins — the contract analyst that saves hours per renewal, the real-time win-loss agent (28:19) The backlog of agents — and the shift from “automation” to “digital colleague” (30:27) The current stack — Cloud Code, Databricks, LangChain, AWS Bedrock (32:26) Slack as the primary interface for digital colleagues (33:00) Are we cutting edge — and does it even matter (34:15) Will employees fear being AI’d away? What Kris is hearing from the team (36:10) The case for direct mail in every GTM stack — no more excuses (38:36) Where to send the gift — Sendoso’s smart-delivery agent at 95%+ confidence (42:43) The Clay integration that just went live (44:40) How the GTM org reports up — and why GTM engineering sits close to the CEO (48:08) Capability count, not headcount — the planning shift (49:51) Agency model and fractional experts — when to lean external (52:23) Closing — advice for VPs of marketing who haven’t started with gifting yet Where to Find Kris LinkedIn (https://www.linkedin.com/in/krisrudeegraap/) Sendoso (https://sendoso.com/) Email: kris@sendoso.com (mailto:kris@sendoso.com) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gtmengineerschool.substack.com (https://gtmengineerschool.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

3. 

   19 May 2026

   S2E6: "Your List Is the Strategy" | Benjamin Douablin

   Listen now | The CEO of FullEnrich unpacks why one data vendor is never enough, how cold calling still wins when the list is right, and why audience-first beats GTM-engineering-first. Brought to you by This could be your logo! Reach out to Jared (https://cal.com/jaredwax) to sponsor the next episode of this podcast and reach 5000+ modern GTM operator and founders. About our guest — Benjamin Douablin Benjamin Douablin is the CEO and co-founder of FullEnrich, the waterfall enrichment platform serving 3,000+ customers across the US, Europe, APAC, MENA and Latam. FullEnrich aggregates 20+ data vendors and queries them sequentially until you get the email or mobile number you actually need — then plugs into the CRM, data warehouse, automation tool, or MCP server you already run. Ben came up through pure sales tech with stints at Sales Ramp and Jellyfish before building an internal data-aggregation tool that became FullEnrich two and a half years ago. Core Takeaways * One Data Provider Is Never Enough: A generalist returns roughly a 30% enrichment rate against most addressable markets. Coverage breaks down by region, by vertical, and by segment. The winning shape is a managed orchestration layer that picks the right provider per query in real time, not a stack of disconnected vendors that operators have to manually benchmark. * Your List Is The Strategy: FullEnrich’s first 40 customers came through cold calls Ben placed himself. The unlock wasn’t the script. It was the prep — convincing yourself the call is a blessing for the prospect before you dial. If there’s no real issue to solve, don’t book the meeting. Open with a “because” that names a problem peers in their industry face, never their own performance, and let them choose the one that resonates. * Audience-First Beats GTM-Engineering-First: The discipline is a means; the audience is the end. FullEnrich serves three distinct audiences (GTM teams, lead-gen and talent agencies, product builders embedding the API). Each one has different acquisition motion, product needs, contract structure. Build squads around the audience, not the tooling — and let GTM-engineering capacity serve the squads where it actually moves the metric. * Stop Building 25-Slide Decks: The bar to execute has dropped; the bar to convince has stayed high. That asymmetry is the lever. If you’re a VP with an idea, hire someone to ship the zero-to-one in days, demo the result, then politick. Pitching internally before you’ve proven anything is the slow path that closes your learning window. Enjoying this episode so far? Subscribe for free to new posts. Top Quotes “I don’t know anyone that is really happy with their data providers, even if they multiply them.” “If you don’t know who you want to talk to, just don’t even think about it, don’t start, because you will get rejected, it will be hard.” “I never start with the tools or with the skills. I always start from my audience or end users.” “Having people that know how great look like is very important because the danger of AI is just very well articulated MBA type of intern that know how to write down very articulated way. But when you go into details, you need to be picky.” Referenced Tools and Resources * Data and enrichment: FullEnrich (https://fullenrich.com/) (aggregating 20+ vendors) * CRM and data infrastructure: Salesforce, Snowflake * Workflow automation: Clay (https://www.clay.com/), n8n, Zapier * AI and agents: Claude Code, MCP server (FullEnrich), HubSpot MCP * Movement origins: “Forward-deployed engineer” (Palantir), GTM engineer (Clay) Timestamps * (02:37) Welcome to S2E6 — the data infrastructure layer of GTM engineering, Ben’s bio (Sales Ramp, Jellyfish, FullEnrich) * (04:13) Ben’s intro — FullEnrich origin, 20+ data vendors, usage-based model, integrations everywhere * (07:13) What’s changed in GTM engineering — the Palantir comparison, why Clay launched the movement * (09:12) Beyond GTM — recruiter and talent acquisition use cases, Snowflake as system of record * (12:07) Where the movement is being oversold — ex-spray-and-prey agencies rebranding as engineers * (14:50) Does GTM engineering 10x the rest of the sales org? The hire profile question * (16:22) Claude Code hype — siloed adoption, FullEnrich’s MCP server, the chat-as-blank-page problem * (20:36) Why one data provider isn’t enough — coverage gaps, the benchmarking pain * (23:08) The orchestration layer — managed waterfall by industry, region, segment * (26:02) Cold calling as a growth lever — your list is the strategy * (28:36) Tone of voice on the call — don’t push, don’t sound salesy, ask permission * (29:51) Have a reason to call — the “because” frame that earns attention * (30:37) Don’t touch the prospect’s ego — name the problems peers face, let them pick * (31:50) What’s next for the enrichment industry — global coverage, verification, decision layer * (34:28) Hiring for GTM in the AI era — what separates good from great * (42:47) AI as the well-articulated MBA intern — polish hides shallow work * (44:20) How FullEnrich structures its GTM org — three audiences, three squads * (50:00) Agencies vs in-house — main motion in-house, $20–50K budget for new motions * (53:41) Stop-doing for VPs — demo the zero-to-one before building the deck * (55:56) Closing — audience first, list is strategy, AI with growth mindset Where to Find Benjamin * LinkedIn (https://www.linkedin.com/in/benjamin-douablin/) * FullEnrich (https://fullenrich.com/) Where to Connect with Jared & Matteo * Matteo Tittarelli, GTM Engineer School Co-founder: LinkedIn (https://www.linkedin.com/in/matteo-titta/), X (https://x.com/matteo_titta), Website (https://genesysgrowth.com/), Newsletter (https://newsletter.genesysgrowth.com/) * Jared Waxman, GTM Engineer School Co-founder: LinkedIn (https://www.linkedin.com/in/jwaxman/) Thanks for reading GTM Engineer School! Subscribe for free to receive new posts and support my work. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gtmengineerschool.substack.com (https://gtmengineerschool.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

4. 

   14 May 2026

   S2E5: "Foundations Before Automation" | Mario Moscatiello

   Listen now | The VP of Marketing at Airbyte unpacks why every company is now a go-to-market company, the warm outbound playbook that doubled pipeline, and what separates the operators worth hiring in 2026. Brought to you by This could be your logo! Reach out to Jared (https://cal.com/jaredwax) to sponsor the next episode of this podcast and reach 5000+ modern GTM operator and founders. About our guest — Mario Moscatiello Mario Moscatiello is the VP of Marketing at Airbyte, the open source data integration platform with 500+ connectors that moves data between sources and warehouses, CRMs, or activation tools. If you are syncing product signals, stitching together enrichment sources, or moving data between systems at scale, Airbyte is probably already somewhere in your stack. Mario came up the developer-tools path: growth at Pusher in London, then GitBook, then a stint as Principal at Flex Capital and board observer at Strapi, then Head of Growth at FluteStack. He had been advising Airbyte since 2020, four years before joining full-time, so when he stepped into the VP role he already knew the product, the community, and the motion cold. Since joining, Mario has built what he calls a warm-outbound motion that triangulates GitHub signals, product usage, and pricing-page intent through Common Room, Octave, and Clay. That approach doubled Airbyte’s pipeline growth rate. Core Takeaways * Foundations Before Automation Is The Real Step Change: GTM engineering is a step change only when product market fit is in place and revops data is clean. AI is a multiplier of whatever foundation exists. Bad data equals bad signal equals bad results, and AI in the mix is a multiplier effect. Pre-PMF teams hacking GTM with AI just create more damage faster. The two foundations are the PMM hat (personas, competitor, market, what works) and the revops hat (clean fields, clean enrichments, clean attribution). Without those two, more activity equals more noise. * Owning Workflows End-To-End Is The Leverage Unlock: Every team member can now own a workflow from idea to live. Paid SEO goes from idea to keyword research to blog post with assets and published in an hour. The SDR manager goes from prospect list to email copy to launched campaign in the same hour. AI compresses the wait between specialists. The right framing is force multiplier per role, not headcount replacement. Anthropic and OpenAI are hiring engineers AND account executives at the same time they ship tools that supposedly replace both. Five strong people doing the work of fifteen, not one star doing the work of ten — because that one star carries unrepeatable key-man risk. * Warm Outbound Is Signal Triangulation, Not Message Volume: The doubling of Airbyte’s pipeline growth rate did not come from blasting cold sequences. Two specific plays. First, events: dump the post-event lead list into Common Room, rank by who has signed up for the product or used the open source repo, and let SDRs only call the warm subset. Second, PLG signups: when an engineer signs up, outreach to them, AND prospect for decision-makers in the same org, AND warm those decision-makers with ads BEFORE the SDR call. The Twilio “Ask Your Developer” campaign at scale. Even when a motion has to be cold, the question is how to warm it up. ABM at Series B is now defensible if you know your audience. * Hire Barrels, Not Ammunition. Then Outsource The Deep Expertise: Mario hires generalists who can take a project end-to-end without a playbook over narrow specialists. The best SDR he ever hired was selling pest control door to door. Four traits to look for. Agency: just do the thing, do not tell me you will plan to do the thing. Curiosity: the playbooks that worked five years ago do not work now. Taste: AI brings the cost of writing copy and code to zero, and taste is what differentiates. Chip on the shoulder: something to prove. Then complement the in-house team with agencies for deliverability, paid media, and scaled outbound, so the team focuses on managing agents and workflows rather than becoming a CPM expert. Enjoying this episode so far? Subscribe for free to new posts. Top Quotes “Bad data equals bad signal equals bad results... When AI is in the mix, that’s a multiplier effect.” “For me, it’s really about drive over experience, agency over experience. I don’t care if you’ve never done this job. The best SDRs I hired was a person that was selling pest control products like door to door.” “AI brings the cost of writing copy, writing code, and everything to zero. What is going to differentiate is your taste and deep understanding of who you’re selling to.” “The biggest thing you should stop doing is just doom scrolling social media and sending your team everything that other people are doing... Just start listening to what your customers are saying.” Referenced Tools and Resources * Data infrastructure: Airbyte (https://airbyte.com/), Salesforce, Supabase * Signal hub: Common Room (https://www.commonroom.io/) * Outbound orchestration and messaging: Octave (https://www.octavehq.com/), Clay (https://www.clay.com/), Instantly (https://instantly.ai/), Outreach (https://www.outreach.io/) * Audience sync and ads: Vector * Sales intelligence: Gong * AI assistants and dev: Claude Code (https://www.anthropic.com/), Claude, Cursor, ChatGPT, Lovable * Documentation and written culture: GitBook, GitHub * Frameworks and references: Barrels vs Ammunition (Keith Rabois, Founders Fund and Vinod Khosla), Twilio “Ask Your Developer” campaign Timestamps * (05:11) Welcome to S2E5 — Matteo’s intro on data infrastructure, Mario’s bio, the warm-outbound motion that doubled pipeline at Airbyte * (08:18) Investor to operator return — every company is a go-to-market company when software costs go to zero * (09:55) Workflow ownership end-to-end — paid SEO, SDR manager going idea to live in an hour, technology no longer the blocker * (11:28) Dev tools historical context — Auth0, Pusher, segment plus APIs, GTM engineering not new for dev tools * (13:33) Step change vs oversold — PMM and revops as the two foundations, AI as multiplier of garbage too * (16:50) The brand and product marketing comeback in the AI slop era * (17:48) Force multiplier vs headcount replacement — Anthropic and OpenAI hiring engineers AND AEs, key-man risk * (19:30) Markdown files in a GitHub repo vs notebooks on laptops — knowledge that survives the people * (23:14) Build vs buy and stitching — Vercel and Ramp can build, most teams should stitch via MCP * (25:00) MCP server unlock — making any tool talk to any tool, free from vendor integration roadmaps * (28:02) Mario’s overnight cron job that cleans his daily notes plus a CLAUDE.md file knowing his priorities * (28:43) Stack walkthrough — Common Room, Octave, Clay, Instantly, Vector, Gong, Outreach * (32:32) Warm outbound play one — events leads ranked through Common Room before any SDR call * (33:30) Warm outbound play two — PLG signups plus decision-maker prospecting plus warming via ads (Twilio at scale) * (35:33) ABM at Series B is now defensible if you know your audience * (36:41) Barrels vs ammunition (Keith Rabois) — drive over experience, the door-to-door pest-control SDR * (41:57) Outsourcing the deep expertise — deliverability agency, paid media agency, the team focuses on agents and workflows * (47:18) Four traits for great GTM engineers — agency, curiosity, taste, chip on the shoulder * (49:33) Stop doom scrolling, start listening — the Gong digest workflow as the one habit to start Where to Find Mario * LinkedIn (https://www.linkedin.com/in/mmoscatiello) * Airbyte (https://airbyte.com/) Where to Connect with Jared & Matteo * Matteo Tittarelli, GTM Engineer School Co-founder: LinkedIn (https://www.linkedin.com/in/matteo-titta/), X (https://x.com/matteo_titta), Website (https://genesysgrowth.com/), Newsletter (https://newsletter.genesysgrowth.com/) * Jared Waxman, GTM Engineer School Co-founder: LinkedIn (https://www.linkedin.com/in/jwaxman/) Thanks for reading GTM Engineer School! Subscribe for free to receive new posts and support my work. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gtmengineerschool.substack.com (https://gtmengineerschool.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

5. 

   6 May 2026

   "Earning the Right to Automate" | Eoin Clancy

   About our guest — Eoin Clancy Eoin Clancy is the VP of Growth at AirOps, the AI search and content engineering platform powering content workflows at companies like Webflow, Ramp, and Notion. He came up through the same GTM engineering path our audience is on — six years at Telnyx going from growth engineer to head of growth, then founded Build AI First in late 2023. At AirOps, Eoin scaled the company from $2M to $15M ARR in 11 months and runs the AirOps content engineering cohort that's become one of the most concrete training programs in AI search. Core takeaways Polarization Is Accelerating, Not Closing: The spectrum between top operators and the median has never been wider. Laggards are still figuring out Zapier; the top 10% are spending days in Claude Code and shipping at a different rate. AI doesn't replace people — it replaces the ones who don't use it well. Eoin's call to action: if you're actively learning today, you're already in the top 10%, and the only job is to stay there. Information Gain Is The Whole Game In AI Search: Search engines and the LLMs powering them reward unique context that isn't in the training data. Where that context lives — in your customer calls, support tickets, internal Slack threads, expert heads — is now reachable through MCPs and agents. The competitive moat is the proprietary substrate, not the publishing pipeline. Cosine similarity is your enemy: undifferentiated content reads as gray, replicated, and the algorithm penalizes it. AI/Human Collaboration Is A Modulator, Not A Switch: The "either let the AI do everything or do it all yourself" frame is broken. The right question per workflow: which steps reduce human involvement (legal review codified into an agent over time), which steps increase it (subject-matter-expert capture from your engineers and salespeople), and what's the hybrid that gets to 10 out of 10 quality. The pure ends get to 6 or 7. Earn The Right To Automate: High-velocity experimentation comes before infrastructure. AirOps ran 5–10 manual webinars before codifying anything; Ramp ran years of trigger experiments before building internal sales tooling. Premature automation locks in process before learnings exist. Take 10 shots, learn from the misses, codify only what's repeatedly worked. Top quotes > "I'd much rather take 10 shots on goal, five of them hit. Two of them are failings that you learn more about your audience or the market on, and then you develop from there." > "If you actually don't know what works and what doesn't, you're, you haven't earned the right to go automate it yet." > "Internally, I'm known as the guy who drinks our own champagne. So drinking your own champagne is my preferred method." > "If you're actively learning today, you're probably in the top 10%. And that's just where you want to make sure that you stay." Referenced tools and resources AI assistants & dev: Claude Code (https://www.anthropic.com/), Claude, ChatGPT, Cursor AI search & content engineering: AirOps (https://www.airops.com/), Perplexity, Google AI Overviews, MCP GTM & ops infrastructure: Salesforce, Clay (https://www.clay.com/), Slack, Gong, Intercom Image generation: Nano Banana Analytics: GSC, GA4 Workflow automation: Zapier Timestamps (04:23) Welcome to S2E4 — the SEO to AEO shift, Eoin's path from growth engineer to VP Growth at AirOps (07:06) What's actually changed in GTM, content, and context engineering in the last year (09:53) The polarization of skills — top operators racing ahead, the median falling behind (12:00) AI won't replace people, it should empower them — Ross Simmons quote, anti-doom framing (13:49) Hype vs reality in AI-powered content — where information gain actually lives (17:25) Don't let your agent fan out a thousand pages — it does more harm than good (17:55) Cosine similarity and the gray middle — why undifferentiated content loses in AI search (20:18) Build vs buy plus bundling and unbundling cycles — the TV subscription analogy (22:30) Bundling is the next move — tool consolidation with MCP and Claude Code orchestration on top (24:33) The maintenance burden of vibe-coded apps — late-night pings and product-owner drift (25:30) Pick your battles — the nano banana lesson on waiting out the better tool (27:14) What Eoin's team optimizes for — high-velocity experimentation over single-bet projects (30:31) Switching to AI search — why no acronym (AEO, GEO, LLMO) has won yet (32:56) Top operator misconceptions — "AI content is bad" and "we have nothing unique to say" (35:00) The hybrid is the answer — modulating AI and human involvement per workflow step (41:07) What you can do this week — sales calls plus GSC plus Slack into Claude or ChatGPT (46:49) Where content engineering sits at AirOps vs Webflow vs Ramp — and why it sits under growth (48:42) True north metric for AI search — mention and citation rate, branded search as correlation (50:34) Drinking your own champagne — AirOps runs its full GTM motion through AirOps (52:13) Hiring the next GTM engineer — sit between sales and marketing, build infra plus boost team efficiency (56:55) Closing — the gap is widening, keep the human in the loop, where to find Eoin Where to Find Eoin LinkedIn (https://www.linkedin.com/in/eoinclancy/) AirOps (https://www.airops.com/) Where to Connect with Jared & Matteo Jared Waxman, GTM Engineer School Co-founder: LinkedIn (https://www.linkedin.com/in/jwaxman/) Matteo Tittarelli, GTM Engineer School Co-founder: LinkedIn (https://www.linkedin.com/in/matteo-titta/), X (https://x.com/matteo_titta), Website (https://genesysgrowth.com/), Newsletter (https://newsletter.genesysgrowth.com/) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gtmengineerschool.substack.com (https://gtmengineerschool.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

6. 

   30 Apr 2026

   S2E3: "Building for Machines, Not Humans" | Kevin White

   About our guest — Kevin White Kevin White is the head of marketing at Scrunch AI, where he's building visibility infrastructure for the post-LLM web — analytics and optimisation that show brands how they're being represented inside ChatGPT, Perplexity, Claude, and Google AI Overviews. Before Scrunch, Kevin spent a decade marketing for the companies that defined the modern operator stack — Common Room, Retool, and Segment — and has advised teams at Ashby and Deepnote. He's one of the most pragmatic operators in the AEO space, with a working hypothesis that "there are no experts in this market yet" and a habit of running controlled experiments instead of taking anyone's word for it. Core takeaways Bots Are Your New VIP Visitor: Retrieval bots from ChatGPT, Perplexity, Claude, and Gemini are now hitting sites at meaningful volume — on Scrunch's own site, bot traffic exceeds human visits. Each retrieval call has a real human with commercial intent behind it. The work for marketers and engineers is shifting toward making sites cheap to crawl in tokens (Markdown and JSON over heavy JavaScript), so retrieval bots get more useful information and your brand surfaces in more answers. Wave 1 GTM-E Was Enrichment. Wave 2 Is Bespoke Trigger Hunting: The first wave of go-to-market engineering consolidated firmographic enrichment for SDRs. That layer is mature. The alpha now lives one step upstream — in identifying the specific sequence of triggers that puts a buyer in commercial-intent mode (a product action, a competitive event, a regulatory shift) and building the workflow to catch it at scale. Tools like Cloud Code have collapsed the build cost from a 6-figure software contract to a $20/month subscription. Hire The Vibe Coder Over The Content Writer: Vibe-coded interactive tools — site graders, prompt generators, real-time domain audits — are now more compelling outbound offers than gated PDFs. The same build gets reused as outbound asset, programmatic SEO play, sales enablement surface, and self-serve qualification interface. Kevin would hire a vibe coder over a content writer 100 times out of 100. Elena Verna at Lovable just hired one full-time. The move is no longer fringe. Reddit Citations Are TOFU. Lower-Funnel Intent Lives In The Long Tail: Most "win at AEO" advice fixates on Reddit and Wikipedia. Kevin's data says those surfaces cover top-of-funnel referential prompts — "what is X" — not the comparison and evaluation prompts that drive commercial intent. The prompts you actually want to win for are answered by long-tail niche publications. Optimise where your buyer's lower-funnel question lives, not where the volume looks biggest on a leaderboard. Top quotes > "We have more bot traffic now than we have human visits to our site." > "If I had the choice between hiring a vibe coder to build really cool tools and someone on the content side of things who's going to write a bunch of like white papers, I'm definitely going to hire the vibe coding person 100 times out of 100." > "There's not really an expert in the space. I would say instead of listening to me or others, go out there, create controlled experiments, see if those experiments yield the right kind of results that you're looking for." > "As marketers, you would typically dismiss in the past — bot traffic, this is not going to give me any information on the user experience. But now, it's like, I want that bot traffic, because there's a person with intent behind that bot." Referenced tools and resources AI assistants & dev: Cloud Code (https://www.anthropic.com/), Cursor (https://cursor.sh/), Claude (https://www.anthropic.com/), ChatGPT GTM & enrichment: Common Room (https://www.commonroom.io/), Clay (https://www.clay.com/), Clearbit, Apollo, Outreach, Artisan, 11x AI search visibility: Scrunch AI (https://www.scrunchai.com/), Perplexity, Google AI Overviews Citation surfaces: Reddit, G2, Trustpilot, Wikipedia Web infra & CDN: Cloudflare, Akamai, Vercel (https://vercel.com/) Analytics: GA4, PostHog, Similar Web Timestamps (00:04) Welcome to S2E3 — Kevin's path from Segment to Retool to Common Room to Scrunch AI (03:00) What's actually changed in GTM engineering — Wave 1 enrichment vs. Wave 2 trigger reverse-engineering (04:55) Reverse-engineering signals back from buyer commercial intent (09:25) The biggest failure mode — list-building beats message-coaching, every time (12:00) SDRs as multipliers, not dispensable headcount (14:00) Stack the plays as patterns emerge — and don't stop hiring once it works (16:25) Where GTM engineering is winning — and the untapped industries with greenfield opportunity (18:15) Cloud Code as today's tool of choice — and why Kevin won't be loyal next quarter (20:40) Worked example: paid spend × declining organic = Scrunch ICP (23:05) Who maintains the sprawl of vibe-coded tools? Enter the AI architect (24:43) The vibe coder vs. content writer hiring decision — 100 out of 100 times (27:30) One vibe-coded tool, four surface areas — outbound, SEO, enablement, self-serve (30:30) AEO, GEO, AI search — and why Kevin stays acronym-agnostic (33:14) There are no AEO experts yet — run controlled experiments instead (36:00) Reddit and Wikipedia cover top-of-funnel. Long-tail niche pubs cover lower funnel. (39:00) Two Scrunch personas — marketing (CMO/SEO) and engineering (CTO/CIO) (42:30) Bots are now your VIP visitor — Scrunch's own traffic data (44:00) Three categories of bots: traditional search, training, and retrieval (44:48) The token economy of crawlability — Markdown and JSON over JavaScript (48:46) The mental shift from "filter bot traffic" to "want bot traffic" (53:12) Hire deep IC experts and let them stack AI on top — the new shape of marketing teams Where to Find Kevin LinkedIn (https://www.linkedin.com/in/kevbosaurus/) Scrunch AI (https://www.scrunchai.com/) Where to Connect with Jared & Matteo Jared Waxman, GTM Engineer School Co-founder: LinkedIn (https://www.linkedin.com/in/jwaxman/) Matteo Tittarelli, GTM Engineer School Co-founder: LinkedIn (https://www.linkedin.com/in/matteo-titta/), X (https://x.com/matteo_titta), Website (https://genesysgrowth.com/), Newsletter (https://newsletter.genesysgrowth.com/) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gtmengineerschool.substack.com (https://gtmengineerschool.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

7. 

   22 Apr 2026

   S2E2: "Context Is the Moat" | Zach Vidibor

   About our guest — Zach Vidibor Zach Vidibor is the co-founder and CEO of Octave, the first context engine built specifically for GTM teams. Think of Octave as a GitHub for your go-to-market context — a structured home for your ICP, your value props, your competitive opinions, and every piece of institutional knowledge that should shape how AI and humans represent your company in market. Before founding Octave, Zach spent a decade as an operator inside companies that defined how modern GTM is done — LinkedIn, DocuSign, and Dropbox — seeing from the inside how strategy leaks and compresses by the time it reaches the frontline. Octave is his answer to that problem: a shared repo so every agent, workflow, and person on the team represents the company consistently. Core takeaways Context is the moat, AI is the commodity: Every team now has Claude, GPT, and Gemini. Using AI is table stakes. The alpha has to sit one layer up — in your first-party context: your codified ICP, your competitive opinions, the customers you know you don't serve and why. The lens you put between your company and the market is the defensible thing; the model behind it is not. Wave 1 was filling fields. Wave 2 is codifying opinions: The first year of GTM engineering was dominated by enrichment — "we can now look up 30 DevOps engineers and drop them into an email." Problem solved. The next wave is interpreting what 30 DevOps engineers mean for how you act on that account — and the best teams aren't optimizing yesterday's assembly line. They're 3D-printing new motions from scratch with context as the starting input. If you skip ICP, you will regret it: LLMs are sycophantic optimists. Without your opinions baked in as constraints, they iterate plausibly against any problem indefinitely — "GPT splits atoms until infinity." ICP isn't a static deliverable; it's a testable hypothesis the system grades itself against. Teams that skip this foundation end up producing AI-slop at scale. Retire the set-and-forget mindset: The market's resting heart rate has gone from 60 to 120 beats a minute. VPs who still expect high conviction before every decision will lose to the ones who shift on a dime. Ten new competitors arrive per quarter. Category-defining tools appear on one-month cycles. The mindset for 2026 isn't "decide once"; it's "decide fast, shift faster, keep your context repo clean enough that changing direction is cheap." Top quotes > "Using AI, that's not a differentiator, right? You have to put alpha on top." > "What about, instead of an assembly line, we 3D print this thing?" > "The resting heart rate of the market has gone from 60 to 120 beats a minute." > "Don't just expect the models to know what you know." Referenced tools and resources Context & Messaging: Octave (https://www.octave.ai/) (GitHub for your GTM context) Data & Prospecting: Clay (https://www.clay.com/), Salesforce, HubSpot AI & Agents: Claude (https://www.anthropic.com/), Claude Code, Claude Co-work, OpenAI (https://openai.com/) (GPT), Google Gemini Past operator context: LinkedIn, DocuSign, Dropbox (Zach's prior career) Timestamps (03:10) Welcome to season 2, and why the season shifts from hype to operating system (04:00) Meet Zach Vidibor — co-founder and CEO of Octave, the context engine for GTM teams (06:20) Why context is the lens between your company and your markets (08:00) How GTM engineering has evolved in the last year — Wave 1 to Wave 2 (09:50) Using AI isn't a differentiator — you have to put alpha on top (10:20) Why LLMs are sycophantic optimists and what that means for GTM work (13:10) Where GTM engineering is real vs. where it's oversold (18:30) The "3D print this thing" reframe — beyond assembly-line optimization (19:30) Claude Code hype and what it means for solo operators vs. the enterprise (22:10) The coordination problem Claude Code creates in scaled sales orgs (25:00) Cut vs. grow — why you need to pick the goal before deploying AI (26:00) "Slice the world into 50 subverticals" — what different goals actually look like (28:00) Defining context engineering, and why ownership is still TBD (30:00) Institutional knowledge: the "we don't sell to higher ed" problem (34:30) The infrastructure monitoring case study — developer outbound at scale (36:00) Brownfield vs. greenfield routing and persona-specific context (39:40) Why the goal is positive interactions, not just demos (41:00) Matteo on the PMM perspective — teams that skip ICP (42:30) "If you skip, you will regret" (43:30) How Octave structures its GTM team — forward-deployed engineers reporting to sales (47:00) Hiring bar for great GTM engineers — taste plus systems thinking (50:40) "Go-to-market is super quantum" (52:30) What VPs should stop doing immediately (53:40) The resting heart rate of the market — 60 to 120 beats a minute Where to Find Zach LinkedIn (https://www.linkedin.com/in/yashtekriwal/) Clay (https://www.clay.com/) Where to Connect with Jared & Matteo Jared Waxman, GTM Engineer School Co-founder: LinkedIn (https://www.linkedin.com/in/jwaxman/) Matteo Tittarelli, GTM Engineer School Co-founder: LinkedIn (https://www.linkedin.com/in/matteo-titta/), X (https://x.com/matteo_titta), Website (https://genesysgrowth.com/), Newsletter (https://newsletter.genesysgrowth.com/) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gtmengineerschool.substack.com (https://gtmengineerschool.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

8. 

   15 Apr 2026

   S2E1: Top Talent Invests In Transferrable Skills | Yash Tekriwal

   About our guest — Yash Tekriwal Yash Tekriwal is Head of Education at Clay, where he builds the programs, content, and partnerships that help GTM operators learn one of the most powerful — but complex — tools in the modern stack. Before running education, he was Clay's first founding GTM engineer, building out the core sales process, demo tables, and custom POC model that supported the company's back-to-back years of 6–10x revenue growth. A former high school computer science teacher, 2x founder (Lectureless, Radify Labs), and self-described expert generalist, Yash brings an unusual mix of classroom chops and operator instincts to Core takeaways The Layered Grunt Work Problem: AI doesn't delete grunt work — it moves it up a layer of abstraction. What used to be "write your weekly update" becomes "read everyone else's updates," and that shift unlocks more interconnected team conversations downstream. The leverage is in what becomes newly possible, not just in hours saved. Computational Thinking Beats a CS Degree: Having written Python doesn't prove you can think computationally. The real test is transferability — can you port a skill from Claude to ChatGPT, or from Python to JavaScript, with light pointers? If not, you've been given fish, not taught to fish. In a toolscape that reshapes every 90 days, transferable skills are the highest-signal indicator of top talent. Three Personas Hit Three Different Walls: GTM-background operators get lost in computational thinking and burn credits before they learn. Ops-background people with no-code experience need to learn workflow thinking — where Clay fits vs. where it doesn't. Engineers are often the hardest persona because syntax knowledge doesn't guarantee systems thinking; the abstraction layer is the new challenge. Automate by Category + Time to Value: Every automation either replaces manual work with identical output OR unlocks capability that wasn't possible before. Prioritise by the 5–10 minute test: can you get 80% of the way there? If yes, ship it. Optimise the last mile of any automation only after you've covered the broad gains. Pareto Principle is always in effect. Top quotes > "What I think AI is doing, which is still a step change forward, is it is moving the grunt work up one layer of abstraction." > "Just because you have a computer science degree does not mean that you know how to think computationally." > "We will probably enter the most entrepreneurial generation of the past couple of decades because you don't need all the things or the obstacles that were in your way of starting a business before." > "Take the 20% that you can automate to give you 80% of your returns on the things that you need. Do that for as many things as you can before you start to try and optimize the last mile of your automations." Referenced tools and resources Sales & Revenue: Clay (https://www.clay.com/) (Ads, Audiences in beta), Salesforce, Attention (https://www.attention.com/) AI & Agents: Claude (https://www.anthropic.com/), Claude Code, OpenClaw (formerly Cloudbot), OpenAI (https://openai.com/) Productivity & Ops: Notion (https://www.notion.so/) (custom agents, meeting recorder), Dust (https://dust.tt/), Slack Workflow Automation: N8N (https://n8n.io/), Zapier, Airtable Learning: Clay University, Code Academy (https://www.codecademy.com/), Algorithms to Live By (book) Timestamps (02:34) Welcome back to the GTM Engineer Podcast season two, and why Yash is the right person to open it (05:20) How GTM engineering has evolved in the last year, and the Andreessen parallel to software engineering titles (07:19) The evolution from software engineer to frontend to forward-deployed — and what that means for GTM engineering next (07:38) What Yash would miss most if GTM engineering disappeared tomorrow (08:48) AI doesn't delete grunt work — it moves it up a layer of abstraction (09:16) Where the hype is overselling and what still has to happen manually (11:29) The two-category framework for deciding what to automate first (14:37) The 5–10 minute time-to-value test for any new automation (16:00) Why we're entering the most entrepreneurial generation in decades (16:20) Why larger companies struggle — and the fear of job loss that blocks adoption (18:36) The transferability test: Python → JavaScript, Claude → ChatGPT (20:53) Playing with OpenClaw for EA workflows — meeting briefs from calendar (21:19) Claude Code vs Claude Co-work vs OpenClaw — what's actually different (24:59) Security and permissions when giving agents tool access (31:13) What's new at Clay: Ads GA and Audiences beta (32:34) Three learner personas and why each hits a different wall (37:17) Tool picks beyond Clay: Notion custom agents and Attention (42:48) The highest-leverage skills for GTM engineers this year Where to Find Yash LinkedIn (https://www.linkedin.com/in/yashtekriwal/) Clay (https://www.clay.com/) Where to Connect with Jared & Matteo Jared Waxman, GTM Engineer School Co-founder: LinkedIn (https://www.linkedin.com/in/jwaxman/) Matteo Tittarelli, GTM Engineer School Co-founder: LinkedIn (https://www.linkedin.com/in/matteo-titta/), X (https://x.com/matteo_titta), Website (https://genesysgrowth.com/), Newsletter (https://newsletter.genesysgrowth.com/) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gtmengineerschool.substack.com (https://gtmengineerschool.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

9. 

   27 Nov 2025

   E10: "Systems to Get, Keep, or Make Customers Worth More" | Laurens Nys

   About our guest — Laurens Nys Laurens Nys is the founder of GTM Sigma, a studio that builds AI-led GTM systems. He is a prominent voice in the GTM engineering community, known for his practical job descriptions for the role and his advocacy for dynamic, signal-based TAM lists. Laurens is an expert in N8N a workflow automation tool, and uses it to create powerful and efficient GTM motions. He is passionate about building systems that help companies acquire, retain, and grow their customer base. Laurens —  also a lead instructor at GTM Engineer School cohort 2 — is a a strong proponent of using automation to drive efficiency and growth. Core takeaways GTM Engineering from First Principles: A GTM engineer is someone who builds systems to get, keep, or make customers worth more. It’s about applying an engineering mindset to the entire go-to-market process. The Convergence of Factors: The rise of GTM engineering is the result of several factors, including the end of the “growth at all costs” era, the increasing importance of efficiency, and the advent of AI. The System is Key: The specific CRM or tools used are less important than the underlying system. As long as the tools have a decent API, a GTM engineer can build an effective system around them. The Intelligence Layer: The core of a modern GTM stack is the intelligence layer, which for Laurens is N8N coupled with AI. This is where the data is processed, and the decisions are made. Finding the Constraint: To effectively design a GTM system, it’s crucial to identify the bottleneck in the customer journey. This allows the GTM engineer to focus their efforts on the area that will have the greatest impact. Top quotes On GTM engineering: “A GTM engineer is just someone that builds systems to get, keep, or make customers worth more.” On the importance of systems: “I don’t really care about CRM as long as I can interact with it, meaning it has an API that’s not complete s**t. I’m fine.” On the modern GTM stack: “You have a database where obviously all the data lives. And then two, you have kind of the intelligence layer or kind of the brain of the operation. And for me, that’s N8N coupled to either workflows or some sort of an AI system.” On identifying the bottleneck: “The top of the bottle usually is the bottleneck. So outbound is a very common one.” Referenced tools and resources CRM: Atio, HubSpot LLM: OpenAI (ChatGPT), Claude Enrichment/Scraping: Bright Data, Rapid API Workflow Automation: N8N Timestamps (02:08) Laurens’ definition of GTM engineering (03:07) The factors that led to the rise of GTM engineering: efficiency and AI (04:33) Lightning Round: Favorite CRM (Atio, HubSpot) (05:13) Lightning Round: Top LLM (OpenAI/ChatGPT, Claude) (05:46) Lightning Round: Top enrichment tool (Bright Data, Rapid API) (06:54) Laurens’ top overall GTM engineering tool (N8N) (07:09) The most underrated GTM engineering tool (Bright Data) (08:22) The building blocks of Laurens’ GTM stack: database, intelligence layer (N8N), and interaction layer (10:14) Identifying the bottleneck in the customer journey (12:53) A deep dive into a GTM play for a company with a planning API (16:19) A walkthrough of the N8N workflow for the planning API use case (20:27) How to get good at N8N: project-based learning (21:53) Emerging skills for GTM engineers: GTM knowledge and technical fundamentals (23:16) The importance of mental models and learning how to think (26:18) Advice for aspiring GTM engineers: figure out your skill gaps and fill them (28:42) Why Laurens switched from Clay to N8N (29:48) How to build maintainable GTM systems in a rapidly changing tool landscape (31:33) The future of GTM engineering: will we be rebuilding systems every year? How to connect with Laurens LinkedIn (https://www.linkedin.com/in/laurensnys/) GTM Sigma (https://gtmsigma.com/) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gtmengineerschool.substack.com (https://gtmengineerschool.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

10. 

    28 Oct 2025

    E8: "The Bridge Between Data, Tools, and Strategies" | Nico Druelle

    About our guest — Nico Druelle Nico Druelle is the founder of The Revenue Architects, a consultancy that helps B2B SaaS companies build and scale their revenue engines. He is a leading voice in the GTM engineering community, recognized for his early advocacy of the role and his expertise in building signal-driven GTM motions for companies like Attention, Descript, and Preply. Before launching The Revenue Architects, he led GTM ops at Melio, scaling pipeline with advanced workflow tools. Core takeaways The Evolution of Rev Ops: GTM engineering is a consolidation of skills from traditional Rev Ops, Marketing Ops, and data engineering. The modern GTM engineer is an architect, a data expert, and an executor, all in one. The Power of Consolidation: A single GTM engineer can replace a team of specialists, leading to increased speed, efficiency, and ROI. This consolidation reduces friction and allows for faster iteration and validation of growth experiments. The Modern GTM Stack: The core components of a modern GTM stack include a data layer (data warehouse), an orchestration layer (Clay, Cargo), an engagement layer (Unify), and a CRM (Salesforce). The Scarcest Resource: The ability to set up and iterate on a holistic GTM system is the most valuable and scarce resource, not the data or the tools themselves. Top quotes On GTM engineering: “Go-to-market engineering is a discipline of orchestrating first party data and third party data into a system of action, system of engagement to execute a given vision, a given go-to-market strategy.” On the GTM engineer’s role: “He’s that glue that comes in, just runs experiments, know, test things out, get some results filled back from the market and keep on iterating. And ultimately the uniqueness of that position is that he generates pipeline.” On the evolution from Rev Ops: “If rev ops was the before and go to market is the now or after, I think there is a bit of a consolidation of function of skills.” On the value of a GTM engineer: “The scarce resource is basically the ability to set up that system altogether as a holistic solution and iterate on it to build a defensible system to design growth. That is the real value in this.” Referenced tools and resources CRM: Salesforce LLM: OpenAI (ChatGPT), Claude Enrichment & Orchestration: Clay, Cargo Engagement: Unify Workflow Automation: N8N Timestamps (02:13) Nico’s definition of GTM engineering (03:58) The before and after of GTM engineering (06:24) The GTM engineer as an architect, plumber, and electrician (08:19) Why the GTM engineer role is a consolidation of multiple roles (09:45) The benefits of consolidation: speed and less friction (12:10) Lightning Round: Favorite CRM (Salesforce) (14:18) Lightning Round: Top LLM (OpenAI/ChatGPT) (16:19) Lightning Round: Top enrichment tools (Cargo and Clay) (17:23) Nico’s top GTM engineering tools (Unify) (19:16) The core building blocks of Nico’s GTM stack (23:12) The role of a tool like Default for PLG companies (24:25) Tradeoffs in designing GTM stacks: modularity vs. speed (27:13) A deep dive into a PQL nurturing flow built for Descript (31:26) The importance of evaluations (evals) in AI model performance (37:53) Essential skills for aspiring GTM engineers: data literacy, tool fluency, and business acumen (40:10) How to acquire GTM engineering skills (42:14) The importance of feature engineeringHow to connect with Nico Where to find Nico LinkedIn (https://www.linkedin.com/in/nicolasdruelle/) The Revenue Architects (https://www.the-revenue-architects.com/) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gtmengineerschool.substack.com (https://gtmengineerschool.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

11. 

    3 Oct 2025

    E7: "Messaging, No Matter What Else You Do" | Jason Pulliam

    About our guest — Jason Pulliam Jason Pulliam is a fractional CMO and founder of Vitality Marketing Firm, specializing in helping early to mid-stage B2B companies ($1-20M revenue) develop differentiated messaging and execute high-ROI outbound campaigns. As one of Octave’s most vocal power users, Jason has built a reputation for diving deep into deliverability, leveraging AI for prospect research, and proving that campaign quality always beats volume. His approach combines old-school direct response copywriting principles with modern GTM engineering tools to deliver predictable revenue outcomes—often within 90 days. Core takeaways GTM Engineering defined: “Building the infrastructure that turns signals into revenue. It’s sales ops and marketing automation, but the tooling and the data are where the decision-making converges to move the money.” The stack transformation: Before GTM engineering, we were slaves to our tools. APIs didn’t exist, Zapier was the only power user option, and your stack dictated your motion. Now the stack conforms to the motion—builders design for control, not convenience. Clay as infrastructure: “Clay is the Zapier of our time. I’m not even using their native enrichment tools—I’m using their API to bring in my own tools. At some point we’ll look back and say Clay was the Zapier of yesteryear.” Messaging is non-negotiable: “No matter what else you do, if you don’t have your messaging right, it doesn’t matter how cool and automated and signal-led your whole GTM is.” The $40K case study: A lower mid-market M&A client with only 6,000 targetable prospects. Jason’s team sent signal-stacked emails mentioning overnight packages, got 50 replies, sent 50 physical packages, and converted 5 deals worth several hundred thousand in fees—10x ROI. Deliverability over warm-up: Email Bison and Email Guard are underrated tools that give granular control over deliverability. They’re designed for power users who already know how to send cold email, not first-timers. The 60-day learning curve: For every client, Jason spends the first 60 days capturing every reply variation and objection. After building 50-60 different reply templates, the subject matter expert can step back—the system knows all the answers. AI as knowledge base: Jason uses Typing Mind to load entire copywriting books, client playbooks, and reply templates. This creates a constantly-improving knowledge base that educates virtual assistants and SDRs on how to respond to any scenario. Build your database: Track every email subject, body, and reply rate. After 50+ campaigns, dump it into AI and ask: “What are the patterns? Why did these work?” This is how you develop true campaign intelligence. Study the greats: Read direct response copywriters like John Caples, Eugene Schwartz, Gary Halbert, and David Ogilvy. Load their books into AI and ask them to help you think through problems. Their logic still holds because it’s based on fundamental human psychology. Top quotes On messaging: “Octave helps you figure out who your target market is and how to talk to them. Even at a fundamental level, it helps you think out what problems you solve for different categories of people.” On Clay’s role: “Clay is kind of the Zapier of our time. It’s just a connector into everything. Even if I think about Clay, I’m not even using the enrichment tools that they have native—I’m using their API and going and bringing in my own tools.” On authenticity in AI: “In an age where everything is now free and unlimited and looks real but it’s fake—an age of ‘frake’—being authentic now stands out. Your brand now matters more because if everybody’s selling the same stuff, what makes you different? It’s your brand and your history.” On learning from clients: “Every single time I start with a new client, I’m like, I don’t know what the answer is, but I’m going to be able to solve it. After 60 days, I normally can eliminate the subject matter expert from the loop because we already know all the answers.” On campaign quality: “I care more about how it works than how fast it scales. I can’t afford to just keep trying different things at high volume. Every single time something doesn’t work, I go all the way back.” On direct response wisdom: “80% of the answers are within 20 feet of where the work’s being done. If you can figure out how your ICP thinks, it answers a lot of other downstream problems.” On the weekend reading assignment: “If you have eight hours, read ‘Made to Stick.’ That book will make you understand why some of your campaigns and messaging work and others don’t.” Referenced tools and resources Typing Mind: Multi-LLM interface that lets Jason run Claude, ChatGPT, and other models side-by-side for the best output per task Octave: Messaging platform for structuring ICP, playbooks, and value props—Jason’s top GTM engineering tool Clay: Data orchestration and enrichment platform (”the Zapier of our time”) Email Bison: Underrated sequencer with granular deliverability control Email Guard: Partner tool to Email Bison for deep email deliverability management Instantly / Smartlead: Alternative email sequencers (Jason prefers Email Bison for control) AirScale: Obscure enrichment tool for accessing founder data BetterEnrich: Custom data enrichment source Ocean.io: Lookalike enrichment provider Copywriting Books “Made to Stick” by Chip Heath and Dan Heath: Jason’s #1 weekend reading recommendation “Breakthrough Advertising” by Eugene Schwartz John Caples (”They Laughed When I Sat Down at the Piano”) Gary Halbert (direct response legend) David Ogilvy (advertising fundamentals) Other Commercial scanner: Jason uses this to gut books and load them into AI (cuts the spine, scans pages) OpenRouter: Subscription service for accessing multiple LLM APIs through one account Timestamps (00:00) Introduction to Jason Pulliam and Vitality Marketing Firm (01:56) Jason’s definition: GTM engineering as “RevOps and growth hacking having a baby” (04:43) The shift from tools controlling motion to motion controlling tools (06:38) Lightning Round: CRM preferences—why Jason avoids HubSpot and Salesforce (07:24) LLMs: Typing Mind as the “cockpit” for all models (07:52) Top enrichment tool: “Clay all the way baby, I’m married” (08:43) Most underrated tool: Email Bison and Email Guard for deliverability (10:05) Current GTM stack: Typing Mind, Email Bison/Guard, Octave, Clay (13:40) Why Octave is Jason’s #1 GTM tool—messaging before automation (15:11) How Jason uses Octave playbooks to build reply knowledge bases (18:14) The M&A campaign case study: 6,000 prospects, 50 replies, $40K spend,hundreds of thousands in revenue (21:27) Building reply intelligence: 60 days to capture every objection (24:12) Emerging GTM skill: Patience—workflows take time to tune (25:09) Communication clarity: Explaining technical concepts to average users (27:28) Learning advice: Real-life use cases beat endless LinkedIn scrolling (30:42) Where to find Jason How to connect with Jason LinkedIn (https://www.linkedin.com/in/jasonpulliam/) Vitality Marketing (https://vitalitymarketingfirm.com/) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gtmengineerschool.substack.com (https://gtmengineerschool.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

12. 

    17 Sept 2025

    E6: "Clients don't pay for crazy, they pay for effective": Building custom software at Clay scale | Patrick Spychalski

    About our guest — Patrick Spychalski Patrick Spychalski is co-founder of The Kiln, a skunk works collective of GTM experts, data scientists, and former Clay employees that turns messy RevOps data into revenue engines for SaaS teams. He's a bonafide Clay OG who spent two years at Clay running their early partnership program and now drops marathon-length Clay table teardowns on LinkedIn. Beyond his agency work, Patrick founded and runs Unique Market, a web store showcasing men's vintage designer and avant-garde fashion. This combination of technical GTM expertise and creative taste gives him a unique perspective on building both functional and aesthetically compelling solutions. Patrick's approach centers on assembling "the Avengers" of best-in-class tools for each specific use case, while maintaining a ruthless focus on business value over technical complexity. His recent viral Lovable integration that builds custom software programmatically at scale exemplifies his philosophy of pushing GTM engineering boundaries while solving real business problems. Core takeaways * The "Avengers assembly" approach — finding best-in-class tools for specific use cases rather than one-size-fits-all solutions * Credit engineering mastery — how strategic API key usage can reduce Clay project costs from 60K to 7-10K * The Lovable breakthrough — building custom software programmatically at scale through Clay integrations * CRM data cleaning as foundation — why enrichment and data quality must come before any advanced workflows * N8N vs Clay decision framework — when to use workflow automation versus enrichment-focused tools * MCP servers as emerging skill — why Model Context Protocol development is becoming essential * Value-first philosophy — clients pay for effectiveness, not complexity or flashy demonstrations * The technical skills spectrum — from beginner-friendly Lovable to engineer-focused Cursor for vibe coding Top quotes Best Definition of GTM Engineering: "In my mind, a go-to-market engineer is somebody who has both highly technical ability and the tools required to run go-to-market systems, specifically automated go-to-market systems, as well as the general strategy and intuition of somebody who would be in a go-to-market leadership position." On His Background: "I actually wasn't in sales prior to Clay existing. And so I actually got into sales at the same time as discovering Clay... I can't really imagine having to go and individually prospect and research and reach out to people. I've actually never had to do that." The Avengers Approach: "I think the best way to approach it initially is just figuring out what the best tool for any given use case would be. And it very quickly allows you to assemble the Avengers for a specific client." On Clay's Power: "Clay feels like cheating almost... It's obviously, in my opinion, the best one. I don't think there's a close second... it's just an aggregate of every enrichment tool. Fundamentally, that's what Clay is." Credit Engineering: "You can literally, if your client's planning on using Clay regardless of whether they hire you or not, you can make the money they're paying you back just in recommending a specific set of API keys." The Value Philosophy: "Clients don't pay for crazy. They pay for effective... You're not getting hired to build crazy workflows that people think are cool. You're building workflows that actually add value." On Learning: "Think about these tools as a value vehicle and not a... just a Lego set that you build for fun." Referenced tools and resources * Clay (https://www.clay.com/): Primary enrichment platform and ecosystem orchestrator ("aggregate of every enrichment tool") * N8N (https://n8n.io/): Workflow automation platform for AI agents and trigger-based processes * Lovable (https://lovable.dev/): Vibe coding tool for building custom software and dashboards with natural language * HubSpot (https://www.hubspot.com/): Current preferred CRM with enterprise support and integration capabilities * Attio (https://attio.com/): Future CRM bet as it develops enterprise features * Claude (https://claude.ai/): Preferred LLM for writing tasks due to superior voice and tonality * HG Insights (https://hginsights.com/): Expensive but powerful technographics integration within Clay (8 credits per run) * Crust Data (https://crustdata.com/): Live LinkedIn enrichment scraper with higher accuracy than static lists * Exa.ai (https://exa.ai/): Underrated natural language lead sourcing tool for niche prospect finding * Cursor (https://www.cursor.com/): Engineer-focused vibe coding platform for technical development * Fathom (https://fathom.video): Current call transcript tool (via Zapier integration despite limitations) * MCP: Emerging requirement for advanced GTM engineering integrations Timestamps * (00:01) Introduction to Patrick Spychalski and The Kiln background * (01:32) Defining GTM engineering: Technical ability plus strategic intuition * (02:20) Evolution question: Never knowing sales before GTM engineering tools * (03:27) Lightning Round: Tool preferences and rapid-fire recommendations * (06:44) System design approach: Assembling the Avengers of best-in-class tools * (12:58) Favorite workflow: The viral Lovable custom software generation table * (16:16) Credit engineering: How API keys saved clients 50K+ on Clay projects * (19:55) Emerging skills: N8N, MCP servers, and vibe coding tool spectrum * (24:22) N8N vs Clay use cases: When to use each platform * (28:22) Learning resources: From GTM Engineer School to free YouTube content * (29:45) Practical advice: Focus on value creation over technical complexity * (31:23) Where to connect with Patrick on LinkedIn and The Kiln How to connect with Patrick * LinkedIn (https://www.linkedin.com/in/patrickspychalski/) * The Kiln (https://thekiln.com/) Subscribe to never miss the next episodes, playbooks, frameworks, or deep dive. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gtmengineerschool.substack.com (https://gtmengineerschool.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

13. 

    10 Sept 2025

    E5: "Message-market-fit": How to systematically test 13 campaigns to find asymmetric GTM results | Kellen Casebeer

    About our guest — Kellen Casebeer Kellen Casebeer is founder of The Deal Lab, a Smartlead Certified Partner and Clay Certified Expert agency helping B2B companies achieve message-market-fit through systematic outbound testing. He runs the weekly Clay Cafe open office hours and shares GTM engineering teardowns with his growing LinkedIn following. Before launching The Deal Lab, Kellen's diverse background included wiring ultra-luxury home automation systems, crushing quota as an enterprise SDR, and serving as chief of staff at a pre-product health tech startup. This zero-to-one experience across technical implementation, sales execution, and startup operations gives him unique perspective on rapid GTM experimentation. Kellen is obsessed with speed to value and has built a systematic approach to dividing markets, identifying buyer segments, and rapidly testing messaging to find what he calls "message-market-fit" — the sweet spot where messaging resonates so strongly it generates asymmetric results. Core takeaways * The message-market-fit framework — why finding the right communication approach matters as much as product-market fit * Market segmentation methodology — breaking down markets by segment, persona, and angle for systematic testing * The RSS podcast scraping play — how creative signal detection generated 5% meeting rates in enterprise healthcare IT * Experimentation over perfection — why running 13 simultaneous campaigns beats trying to craft one perfect message * The single-issue voter principle — understanding that prospects make decisions based on one primary factor * Asymmetric results mindset — seeking 30 meetings per month instead of incrementally improving 8 to 9 * The phone as underrated GTM tool — why voice remains the most direct path to decision makers * Scientific method for GTM — applying cancer research methodology to campaign testing and iteration Top quotes Best Definition of GTM Engineering: "GTM engineering to me is a concept... basically the idea of taking the outcome or the challenge of what you're trying to achieve with your go-to-market. And I think just engineering what it looks like to build that out." On Market Evolution: "B2B SaaS itself as an industry is extremely new. And then like these growth motions against it are pretty new... the idea that what we're participating in is the mature state of what's to come is ridiculous." The Clay Ecosystem: "When Clay went like, hey, we have a tool set that can sort of congeal a bunch of these places in one place... it creates this little triangle where it's like the tool provider, the companies that want the benefit of the tool, and then tool experts." Message-Market-Fit Defined: "Someone not thinking about anything. What can we say to get someone to come talk to us? Product market fits, like what do you sell? What's the price point? Will they buy it if they know what it is?" The Asymmetric Mindset: "Clients don't want incremental, they're looking for asymmetrical... if you're at eight and you're like, I think if we did this, we could get nine, the path from eight to nine will never get you to 30." On Experimentation: "The things that work and the things that you wanted to work are not synonymous... experimenting and deifying being in motion, running imperfect tests and allowing the results to dictate what happens next is a much faster, more effective way." Single-Issue Voters: "People are single issue voters behaviorally... We are not inclined to figure out every single factor, measure every single factor and make our best decision. What we do is we care about of all the factors, one thing the most. Referenced tools and resources * Clay (https://www.clay.com/): Primary data manipulation and orchestration platform for campaign building * Smartlead (https://www.smartlead.ai/): Email sequencing platform for outbound distribution (Kellen is certified partner) * ChatGPT (https://chat.openai.com/): AI assistance for message creation and data processing with 4.0 mini for cost efficiency * Scaled Mail (https://www.scaledmail.com/): Infrastructure provider for email deliverability (Dean gets the shoutout) * Lead Magic (https://leadmagic.io/): Email validation and email finding data source * Miro (http://miro.com): Kellen's secret weapon for visualizing ideas and client collaboration * RSS Feed (https://rss.com/blog/how-do-rss-feeds-work/): Creative data source for podcast guest scraping and engagement * HubSpot (https://www.hubspot.com/): Preferred CRM for ease of use and integration capabilities * The Phone: Most underrated GTM tool for direct prospect engagement Timestamps * (00:01) Introduction to Kellen Casebeer and The Deal Lab background * (01:45) GTM engineering: Problem-solving approach to go-to-market challenges * (04:30) Gradual momentum vs. before/after transformation moments * (08:56) Clay's role in creating the GTM engineering ecosystem and job category * (13:21) Lightning Round: Tool preferences and rapid-fire recommendations * (15:54) System design approach: Market, segment, persona, and angle framework * (21:10) Sample size methodology: Qualitative over quantitative testing approach * (26:31) Favorite play: RSS podcast scraping for enterprise healthcare IT penetration * (32:45) Essential skills for different GTM engineering: sales, technical, strategy * (38:31) Core tool stack: ChatGPT, Clay, ScaleMail, Lead Magic, Smartlead * (41:54) Final advice: Experiment more and follow scientific methodology * (43:41) Where to connect with Kellen and join Clay Cafe community How to connect with Kellen * LinkedIn (https://www.linkedin.com/in/joshwhitfieldai/) * The Deal Lab (https://thedeallab.com/) * Clay Cafe (https://www.linkedin.com/company/gtmcafe/) Subscribe to never miss the next episodes, playbooks, frameworks, or deep dive. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gtmengineerschool.substack.com (https://gtmengineerschool.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

14. 

    3 Sept 2025

    E4: "It's creativity, not prompts": Why GTM engineers need business sense over technical skills | Josh Whitfield

    About our guest — Josh Whitfield Josh Whitfield is founder of Content Marketing Media (CMM), the only agency globally certified across Clay, Instantly, HeyReach, and Octave—the four cornerstone platforms powering modern outbound strategy. He's also building Signaliz.com while sharing innovative GTM workflows and AI agents across LinkedIn and X. Before diving into go-to-market engineering, Josh spent 15 years in insurance leading agile teams focused on intelligent automation solutions including process mining, API integrations, and robotic process automation. His technical background in AI and automation—before it was mainstream—gives him unique perspective on what truly matters as these technologies democratize. Josh's philosophy centers on creativity over technical prowess, arguing that as AI handles the complex technical work, success comes from institutional knowledge, business understanding, and the ability to orchestrate innovative solutions that others haven't thought of. Core takeaways * The creativity revolution — Why prompt engineering is dead and creative business thinking is the new differentiator * Institutional knowledge over coding — How understanding business fundamentals matters more than technical skills * The alpha signal methodology — Moving beyond basic demographic targeting to find unique buying indicators * AI-powered research workflows — Using Manus AI and other tools to deliver PhD-level competitive intelligence * The democratization paradox — As tools get easier, differentiation comes from creative application not technical mastery * Strategic retention model — How agencies evolve from email senders to trusted AI advisors for sustained growth * The 25% learning rule — Why dedicating a quarter of your time to exploring new tools is non-negotiable * Robotic handwritten notes case study — The wild workflow that automatically sends real handwritten notes to high-value prospects Top quotes New Reality of Skills: "If you'd asked me that six months ago, I'd have said prompt engineering. Today, I would tell you, I think it's creativity because you know, I don't write any of my own prompts anymore. I just ask the models to write the prompts for me." On Institutional Knowledge: "The future true impactful GTM engineer has enough institutional knowledge of the business, knows how to go find out and fill in the gaps of what they don't know." The Alpha Signal Philosophy: "Clay calls it the alpha signal and really find that thing that really defines, like, this person is telling me they need to get or be involved in the solution that's being offered and that they have the means or will to do so." On Creative Differentiation: "It takes creativity to not be like everybody else and pull news and funding and job changes. It takes creativity to say, look, I'm gonna go out and I'm going to figure out every time someone inserts a geocode radius outside of a conference location in San Jose." The Learning Imperative: "I spent 25% of my existence doing that... When you take the 10 rich companies in the world and they're all focused on the same thing, that's a clue that you probably should be paying attention to it too." On Accessible Learning: "You could, this could be the first time you've ever heard the word GTM engineering. And if you spend enough time, even just open AI with web search, you can, it can teach you how to build clay tables." Referenced tools and resources * Clay (https://www.clay.com/): The orchestration powerhouse that can make 72 API calls per row for enrichment * Octave (https://www.octavehq.com/): Most underrated GTM tool for messaging and copywriting * Claude (https://claude.ai/): Superior for copywriting and email generation over other LLMs * Instantly (https://instantly.ai/)/Maildoso (https://getmaildoso.com/): Email infrastructure and sequencing platform combination * Manus AI (https://manus.im/): Advanced research platform delivering PhD-level competitive analysis * Apify (http://apify.com) & Firecrawl (https://www.firecrawl.dev/): Web scraping tools for unique data acquisition * PandaMatch (https://www.pandamatch.io/]): Lookalike modeling for prospect identification * HubSpot (https://www.hubspot.com/)/Salesforce (https://www.salesforce.com/uk/): CRM platforms (Josh uses both depending on client needs) * Model Context Protocol (MCP) (https://modelcontextprotocol.io/docs/getting-started/intro): Advanced Claude integration for enhanced workflows * Cursor (https://www.cursor.com/) & Lovable (https://lovable.dev/): No-code development tools for rapid prototyping * Delphi (https://www.delphi.ai/): AI training platform Josh used to build his 560,000-word personal AI assistant Timestamps * (01:24) Josh's background: 15 years in insurance building AI before it was cool * (02:07) Definition deep dive: Why GTM engineering is broader than people think * (04:31) The evolution question: From structured enterprise AI to democratic vibe coding * (07:03) Lightning Round: CRM agnostic, Claude for copywriting, Clay for orchestration * (08:55) Most underrated tool: Octave's game-changing impact on messaging * (09:57) System design: Octave brain, Clay orchestration, Instantly distribution * (12:18) The alpha signal methodology: Finding unique intent signals * (14:07) Technology trade-offs: Managing vendor reliability and rapid AI evolution * (16:14) Client adaptation: Balancing multiple stacks and varying organizational maturity * (18:34) First play strategy: Using demo-quality builds to prove value before onboarding * (21:21) Impact metrics: Retention over conversion as agencies become advisors * (24:37) New skills: From prompt engineering to creativity and institutional knowledge * (27:15) Defining creativity: Balancing business understanding with new tool application * (29:30) The 25% rule: Why Josh dedicates 25% of his time exploring new tech * (32:38) Practical advice: Using free ChatGPT as your learning starting point This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gtmengineerschool.substack.com (https://gtmengineerschool.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

15. 

    27 Aug 2025

    E3: Infrastructure, copywriting, data, ops: Building GTM systems that actually work | Wesley Hoang

    About our guest — Wesley Hoang Wesley Hoang is co-founder of Cymate, a B2B lead gen agency that leverages tailored automations and AI to scale pipeline. He's also founder of Akaiza, an ops hub for go-to-market teams to streamline outbound campaign operations from deliverability to analytics. Before building GTM engineering workflows full-time, Wesley held engineering roles at Twitter, Apple, and Experian. His technical background gives him a unique systems perspective on building scalable go-to-market operations that blend technical precision with marketing psychology. Wesley's philosophy centers on starting with fundamentals — getting basic workflows right before adding complexity. His agency has transformed from manual processes to sophisticated AI-powered systems while maintaining focus on what actually drives results. Core takeaways Wesley’s four-pillar GTM tech stack framework that every operation needs: infrastructure, copywriting, data, and operations Why "basic" beats "fancy" — Wesley's favorite workflow is the most straightforward one that actually works The market sophistication trade-off — how to decide between high-volume outreach vs. precision targeting based on your market maturity Psychology over tools — why copywriting and buyer psychology are the most underrated parts of GTM engineering The "just do it" learning method — how to overcome analysis paralysis and start building workflows immediately Communication as a core GTM engineering skill — why working with founders and product teams is essential for success Top quotes Definition of GTM Engineering: "Go to market engineering... I think that is definitely like a new term that has been popping up this past few years. If I were to redefine it, I would sort of like separate that into two sections. So go to market and then engineering." On the Evolution: "It did get easier, but it also did get harder... marketing nowadays is not just like creative anymore. It's almost like half technical, half creative." The Four Pillars of GTM: "If I was to sort of like break it down and make it super, super simple, there are going to be four key areas that you need to focus on. The infrastructure, the copywriting, the data and operations." On Starting Simple: "My favorite bill when it comes to go to market is the most basic bill... Don't look into anything more complicated. Don't worry about like intent data triggers or whatever it might be." The Action Imperative: "Just do it. Just start doing s**t... A lot of people, they do have passion for GTM, including myself. The one thing that's holding them back is just taking action in general." On Learning: "I genuinely believe if you just put your head down and literally just put like three days of your calendar to learn like one of the top tools... you know what, you know how it works right away." Referenced tools and resources * ZapMail (https://zapmail.ai/): Wesley's preferred email infrastructure provider with 0 deliverability issues * Octave (https://www.octavehq.com/): His top GTM tool for personalized messaging ("Octave done. Full stop") * Clay (https://www.clay.com/): The orchestration platform that serves as enrichment hub * Instantly (https://instantly.ai/)/Smartlead (https://www.smartlead.ai/): Email sequencing platforms ("doesn't matter, just choose one") * HeyReach (https://www.heyreach.io/): For LinkedIn automation alongside email sequences * Claude (https://www.anthropic.com/claude) vs ChatGPT (https://chat.openai.com/): Claude for complex tasks, ChatGPT for data use * Apollo (https://apollo.io/): Solid data source despite criticism ("billion dollar company for a reason") Timestamps * (00:01) Introduction to Wesley Huang and SciMate agency background * (01:10) GTM engineering as separate go-to-market plus engineering components * (02:57) The evolution question: How things got easier and harder simultaneously * (04:53) Lightning Round: Tool preferences and rapid-fire recommendations * (05:24) Claude vs ChatGPT: Why prompting matters more than the model choice * (05:37) Clay as enrichment orchestrator, not database: "Clay is a third party tool" * (08:09) The four-pillar GTM tech stack framework deep dive * (12:32) Trade-offs in system design: Market sophistication determines strategy * (15:30) The three essential tools: ZapMail, Octave, Clay, plus distribution layer * (18:19) Wesley's favorite workflow: Why basic beats sophisticated every time * (21:06) When clients need agency help: Infrastructure, data, or copywriting gaps * (25:11) Essential GTM engineering skills: Communication as underrated necessity * (27:01) Practical learning advice: "Just do it" and commit three days to one tool * (31:06) Avoiding LinkedIn rabbit holes: Focus on fundamentals over flashy workflows * (33:37) Where to connect with Wesley and Akiza's beta program timeline How to connect with Wesley * LinkedIn (https://www.linkedin.com/in/weshoang/) * Cymate (https://cymate.io/) * Akaiza (https://akaiza.com/main) Subscribe to never miss the next episodes, playbooks, frameworks, or deep dive. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gtmengineerschool.substack.com (https://gtmengineerschool.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

16. 

    20 Aug 2025

    E2: "It's an orchestra": How to conduct GTM systems that drive revenue | Bobby Offterdinger

    About our guest — Bobby Offterdinger Bobby Offterdinger is CEO of TAM to Target, a full-service outbound agency offering fractional SDR and go-to-market services across multiple B2B verticals. A former teacher turned GTM systems architect, Bobby has evolved his agency from email-only campaigns to building complete go-to-market operating systems for clients. He currently operates in the K-12 space (education and learning environment) while advising top-tier GTM startups including Octave. Bobby has used GTM Engineering to transform his approach from high-volume outbound to precision-targeted revenue engines. Bobby's unique background as an education specialist gives him a conductor's perspective on orchestrating complex GTM systems — highlighting that teaching kids to read and teaching prospects to respond require similar orchestration skills. Core takeaways * The orchestra conductor analogy — why GTM engineering is about orchestration, not just tools * How Bobby's grandfather used home-buying lists in the 1960s — the original signal-based outbound * The evolution from "anyone with a laptop" lead gen to sophisticated go-to-market operating systems * Why 2025 is a buyer-led market and how that changes everything about outbound strategy * Bobby's complete system design: From signal detection to engagement scoring and re-targeting * The K-12 leadership development play using board meeting transcripts as intent signal * How Nation Graph scrapes public school board minutes to create hyper-targeted campaigns * Why limiting your TAM improves performance more than expanding it * The paradigm shift challenge: Bridging sales and marketing with GTM engineering * Essential technical skills: Why API and JSON knowledge is now non-negotiable * The "force yourself to hit the wall" learning method for mastering integrations Top quotes Best Definition of GTM Engineering: "Go-to-market engineering for me is the orchestration of systems, processes, and most importantly, and often forgot, strategy and creative that drives pipeline and revenue." The Conductor Analogy: "I think about go-to-market engineering... like an orchestra. It's an orchestration... And so you think about a tool like Clay, like it's the go-to-market orchestration tool, right?" The Modern Reality: "It's a buyer-led market. Because if I send you an email and you're moderately interested, what are you going to do? You're going to come to my website and then you're going to do your research." On Technical Skills: "You need to know API language. You need to understand JSON and low code like N8N or Make.com... That's becoming more of a non-negotiable." Learning Philosophy: "Don't use the native integration... Force yourself to get out of the habit of using those native ones until you know JSON really well." The Human Element: "The most underrated part of this whole piece is the human being behind all the tools." Referenced tools and resources * HubSpot (https://www.hubspot.com/): Bobby's preferred CRM for low barrier to entry and integrations * Clay (https://www.clay.com/): The orchestration engine and integration glue for everything * Lemlist (https://www.lemlist.com/): Multi-channel sequencer (email, LinkedIn, calling in one platform) * Octave (https://www.octavehq.com/): Bobby's transformational AI messaging platform ("once you're on, you're on") * Smartlead (https://www.smartlead.ai/) & Instantly (https://instantly.ai/): Alternative email-only sequencers * Nation Graph (https://www.nationgraph.com/): Public sector signal detection (board meetings, FOIA requests) * Lead Magic (https://leadmagic.io/): Primary enrichment tool based on monthly spend * Pandamatch (https://www.pandamatch.io/): Lead scoring and ICP fitting * Ocean (https://ocean.io/): Additional lead qualification option * API language and JSON (https://www.json.org/json-en.html): Now non-negotiable for GTM engineers * HTTP API calls (https://www.clay.com/university/guide/http-api-integration-overview): Bobby's recommended learning method over native integrations * N8N (https://n8n.io/), Make.com (https://www.make.com/en): Low-code platforms for workflow automation * Claude (https://claude.ai/): Bobby's preference for deep research tasks * ChatGPT (https://chat.openai.com/): Alternative for building agents and assistants Timestamps * (00:01) Introduction to Bobby Offterdinger and TAM to Target agency * (01:12) Bobby's definition: GTM engineering as orchestra conductor orchestration * (02:25) The evolution question: Life before vs. after GTM engineering * (02:46) Grandfather's 1960s insurance play using home-buying signals * (04:53) Lightning Round: CRM preferences - HubSpot for low barrier to entry * (05:18) LLMs: Gravitating toward Claude for deep research capabilities * (05:37) Top enrichment tools: Clay and Lead Magic based on monthly spend * (05:57) Top GTM tool: "Octave done. Full stop." * (06:13) Most underrated tool: The actual go-to-market engineer as human orchestrator * (07:32) System design principles: Moving beyond "signal, email, profit" thinking * (09:36) The complete GTM operating system: Outbound drives inbound recapture * (12:31) Signal scoring and engagement threshold automation in HubSpot * (15:48) The three-tool minimum: Clay, Lemlist, and HubSpot or Octave dilemma * (19:51) Life before Octave vs. the transformational bet Bobby made * (21:12) Performance gains: Less emails, more replies, no more spin tax needed * (23:11) K-12 case study: Nation Graph partnership and board meeting mining * (26:33) The signal goldmine: "Thomas Middle School needs leadership development" * (28:42) Emerging skills: API language and JSON as non-negotiable requirements * (31:03) Learning advice: Force yourself to use HTTP calls instead of native integrations * (33:19) Practical tip: Use ICP agents to limit TAM and improve targeting * (34:29) Where to connect with Bobby and TAM to Target How to connect with Bobby * LinkedIn (https://www.linkedin.com/in/bobbyoffterdinger/) * Tam To Target (https://tamtotarget.com/) Subscribe to never miss the next episodes, playbooks, frameworks, or deep dive. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gtmengineerschool.substack.com (https://gtmengineerschool.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

17. 

    13 Aug 2025

    E1: "Get the (GTM Engineering) Reps" | Jorge Macias

    About our guest — Jorge B. Macías Jorge Macias is an industrial engineer turned GTM engineering wizard who scaled the first Puerto Rican YC-backed startup from zero to $3M ARR. He's now a go-to-market engineering consultant and advisor helping B2B SaaS companies turn messy data into synchronized revenue engines. Jorge operates and advises multiple startups on sales, PLG, and GTM engineering while sharing his workflows with thousands of GTM operators on LinkedIn. We recorded this just one month after he became a dad — proving that if he can revolutionize go-to-market operations with a newborn, there's no excuse for the rest of us. Core takeaways * The perfect definition of GTM engineering: "RevOps and growth hacking having a baby" * How to replicate your best seller's "secret sauce" and scale it across your entire team * The Series A GTM stack: Essential tools for companies approaching $1M ARR * His favorite technographic play using job descriptions for targeted campaigns * The two emerging skills every GTM engineer needs: patience and clarity * Why real-life use cases beat endless LinkedIn scrolling and course consumption * The simple LinkedIn data hack that revives stale leads every 3-6 months * How AI transforms manual 45-minute meeting prep into automated prospect research * Why GTM engineering is "more art than science" and what that means for you Top quotes Best definition of GTM Engineering: "If RevOps, marketing operations, sales operations, data engineering and growth hacking had a baby... It's turning messy data, tools that are scattered around, half-baked playbooks into one automated synchronized work." The transformation: "Instead of having one awesome seller and a lot of mediocre salespeople, you're gonna have a lot of average salespeople, which is good for business models because it's gonna be more predictable." On tool selection: "Look for the tools that are right for the stage that you are in your company and the stage that you are in your go-to-market journey." Learning philosophy: "The real value in go-to-market engineering comes from practice and from building workflows that are going to be living out there in the world... New skills are like sports—you need to get the reps." Referenced tools and resources * HubSpot (https://www.hubspot.com/): Jorge's go-to CRM for most clients * Salesforce (https://www.salesforce.com/uk/): Enterprise CRM option * Attio (https://attio.com/): Modern CRM Jorge wants to test * Clay (https://www.clay.com/): Primary data orchestration and enrichment platform * Lemlist (https://www.lemlist.com/): Preferred sequencer for multi-channel outreach (LinkedIn + email) * Smartlead (https://www.smartlead.ai/) & Instantly (https://instantly.ai/): Alternative email sequencers * RB2B (https://instantly.ai/): Website visitor tracking and identification * Notion (https://www.notion.so/): Jorge's current CRM and wiki repository * Apify (https://apify.com/): Go-to scraping tool with extensive actor library * Phantom Buster (https://phantombuster.com/): LinkedIn automation and network growth * ChatGPT (https://chat.openai.com/): Browser-based AI conversations * Claude (https://claude.ai/) (Anthropic): API integrations within other tools * Gemini (https://gemini.google.com/), DeepSeek (https://www.deepseek.com/): Additional AI model options * Reoon (https://www.reoon.com/): Underrated email verifier ($80 for 100k verifications + 500 daily credits) * NeverBounce (https://www.neverbounce.com/): Alternative email verification * Apollo (https://apollo.io/), Lead Magic (https://leadmagic.io/), Prospeo (https://prospeo.io/): Lead generation databases Timestamps * (00:00) Introduction to Jorge Macias and GTM engineering fundamentals * (01:56) Jorge's definition: "RevOps and growth hacking having a baby" * (02:57) The biggest transformation: Replicating your best seller's secret sauce * (04:43) How AI scales what already works without reworking everything * (06:38) Lightning Round: CRM preferences - HubSpot vs Salesforce vs Attio * (07:24) LLMs: Claude vs ChatGPT for different use cases * (07:52) Top enrichment tool: "Clay all the way baby, I'm married" * (08:43) Most underrated tool: Reoon email verifier at $0.0008 per verification * (10:05) Jorge's current GTM stack: Notion CRM, Clay orchestration, RB2B tracking * (13:40) Why Notion as CRM: 8 years of familiarity and wiki integration * (15:11) Series A GTM stack recommendations for $1M ARR companies * (18:14) Jorge's favorite GTM plays: Technographic data + job description mining * (21:27) Automated meeting prep: From 45 minutes to AI-generated prospect research * (24:12) Emerging GTM engineering skills: Patience with complex workflows * (25:09) Communication clarity: Explaining technical concepts to average users * (27:28) Learning advice: Real-life use cases beat endless content consumption * (28:42) Sports analogy: Practice builds mental connections, not just watching * (29:19) Practical tip: Download LinkedIn data for automated prospect revival * (30:42) Where to find Jorge and his new GTM engineering consultancy Subscribe to never miss the next episodes, playbooks, frameworks, or deep dive. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gtmengineerschool.substack.com (https://gtmengineerschool.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

18. 

    9 Jun 2025

    Scaling GTM with AI

    I sat down with Jared Brickman to find out what he’s been learning from having helped half of the 550 Insight Partners portcos implement AI to drive more growth and efficiency Here are 10 key lessons learned. 1. Buying Tools Isn’t a Strategy Too many leaders assume that buying ChatGPT or Copilot and handing it to their teams will yield breakthrough performance. It won’t. Brickman emphasized that real results require systemic workflows tied to core KPIs—not just generalized tool access. "We’re well past the prompt-sharing stage. You need coordinated systems to move the needle." 2. From Prompts to Playbooks to Deep Interventions Insight’s journey evolved from prompt training in 2023, to replicable "recipes" in 2024, and now to hands-on deployments. The difference-maker? Structured use cases that tackle specific business problems. Case studies became playbooks. Playbooks became MVPs. That’s the flywheel that creates repeatable success. 3. How Insight Engages with PortCos Insight works in two ways: * Structured design: Mapping processes, finding bottlenecks, and building from there. * Solution-led prototyping: Bringing working templates and helping companies deploy fast. One LA-based portfolio company built a working AI-powered campaign system in a single-day hackathon—Ops upstairs, marketing downstairs, launching side-by-side. 4. A Simple GTM AI Prioritization Framework Brickman’s 4-part approach: * Automate inbound lead response. * Engage high-intent outbound signals. * Scale campaign capacity. * Support the deal team with co-pilots and admin tools. Post-sale? Apply the same lens, with a bonus focus on Tier 1 customer support. 5. Real Examples of Real Results * 6sense: Moved from 50% to 100% SLA compliance by deploying conversational bots when reps didn’t respond in time—generating 20% of pipeline. * Cybersecurity firm: 10x’ed campaign output with a self-serve campaign builder. * E-commerce platform: 100x’ed first-page keyword rankings by auto-generating long-tail SEO content from support docs. 6. Don’t Default to New Tools If you’re locked into an existing stack, don’t jump ship. Brickman recommends mapping pain points and evaluating if your current tools (like Zapier or Make) can handle it—especially with the new LLM-based extensions. 7. Build vs. Buy Varies Widely Larger companies often build in-house using Snowflake or AWS. But some early-stage companies skip hiring altogether and start with AI agents. Brickman sees success in both paths. 8. Structure and Coordination Matter More Than Titles An AI committee or center of excellence is essential at scale. One cautionary tale: a PortCo built their own Clay-like tool—without realizing the CMO had already bought Clay. 9. The Power of the Portfolio Insight fosters cross-company collaboration through its Onsite Expert Hour series, cohort training, and shared libraries of prompts and Zaps. It’s a living lab of what’s working. 10. The Rise of the AI Org Chart Brickman sees a future where employees direct—not operate—AI agents. Multi-agent architectures are already mimicking org charts. Humans are stepping up into strategic roles while agents handle coordination and execution. "Think of it as managing an intern—one that can scale." Final Thoughts The takeaway? Don’t chase the shiniest tools. Start with business problems, build systems around them, and scale what works. As Brickman puts it, "It’s not hype anymore. It’s traction." Want more sessions like this? Learn more at GTM Engineer School (https://gtmengineer.com/). Watch the full interview here at our Youtube channel: This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gtmengineerschool.substack.com (https://gtmengineerschool.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

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