GTM Engineer School Podcast

Jared & Matteo

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

  1. HACE 12 H

    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 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. 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, Salesforce, Supabase * Signal hub: Common Room * Outbound orchestration and messaging: Octave, Clay, Instantly, Outreach * Audience sync and ads: Vector * Sales intelligence: Gong * AI assistants and dev: Claude Code, 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 * Airbyte Where to Connect with Jared & Matteo * Matteo Tittarelli, GTM Engineer School Co-founder: LinkedIn, X, Website, Newsletter * Jared Waxman, GTM Engineer School Co-founder: LinkedIn 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

    46 min

  2. 6 MAY

    "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, Claude, ChatGPT, Cursor AI search & content engineering: AirOps, Perplexity, Google AI Overviews, MCP GTM & ops infrastructure: Salesforce, Clay, 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 AirOps Where to Connect with Jared & Matteo Jared Waxman, GTM Engineer School Co-founder: LinkedIn Matteo Tittarelli, GTM Engineer School Co-founder: LinkedIn, X, Website, Newsletter 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

    54 min

  3. 30 ABR

    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, Cursor, Claude, ChatGPT GTM & enrichment: Common Room, Clay, Clearbit, Apollo, Outreach, Artisan, 11x AI search visibility: Scrunch AI, Perplexity, Google AI Overviews Citation surfaces: Reddit, G2, Trustpilot, Wikipedia Web infra & CDN: Cloudflare, Akamai, Vercel 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 Scrunch AI Where to Connect with Jared & Matteo Jared Waxman, GTM Engineer School Co-founder: LinkedIn Matteo Tittarelli, GTM Engineer School Co-founder: LinkedIn, X, Website, Newsletter 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

    51 min

  4. 22 ABR

    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 (GitHub for your GTM context) Data & Prospecting: Clay, Salesforce, HubSpot AI & Agents: Claude, Claude Code, Claude Co-work, OpenAI (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 Clay Where to Connect with Jared & Matteo Jared Waxman, GTM Engineer School Co-founder: LinkedIn Matteo Tittarelli, GTM Engineer School Co-founder: LinkedIn, X, Website, Newsletter 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

    50 min

  5. 15 ABR

    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 (Ads, Audiences in beta), Salesforce, Attention AI & Agents: Claude, Claude Code, OpenClaw (formerly Cloudbot), OpenAI Productivity & Ops: Notion (custom agents, meeting recorder), Dust, Slack Workflow Automation: N8N, Zapier, Airtable Learning: Clay University, Code Academy, 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 Clay Where to Connect with Jared & Matteo Jared Waxman, GTM Engineer School Co-founder: LinkedIn Matteo Tittarelli, GTM Engineer School Co-founder: LinkedIn, X, Website, Newsletter 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

    50 min

  6. 27/11/2025

    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 GTM Sigma 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

    32 min

  7. 28/10/2025

    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 The Revenue Architects 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

    42 min

  8. 03/10/2025

    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 Vitality Marketing 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

    36 min
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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

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  • Creador
    Jared & Matteo
  • Años de actividad
    2025 - 2026
  • Episodios
    15
  • Clasificación
    Apto
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    © Jared Waxman & Matteo Tittarelli
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    GTM Engineer School Podcast

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