GTM Engineer School Podcast – Details, episodes & analysis

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

* HyperGrowth Partners

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

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.

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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.

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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 (aggregating 20+ vendors)

* CRM and data infrastructure: Salesforce, Snowflake

* Workflow automation: Clay, 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

* FullEnrich

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

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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: Primary enrichment platform and ecosystem orchestrator ("aggregate of every enrichment tool")

* N8N: Workflow automation platform for AI agents and trigger-based processes

* Lovable: Vibe coding tool for building custom software and dashboards with natural language

* HubSpot: Current preferred CRM with enterprise support and integration capabilities

* Attio: Future CRM bet as it develops enterprise features

* Claude: Preferred LLM for writing tasks due to superior voice and tonality

* HG Insights: Expensive but powerful technographics integration within Clay (8 credits per run)

* Crust Data: Live LinkedIn enrichment scraper with higher accuracy than static lists

* Exa.ai: Underrated natural language lead sourcing tool for niche prospect finding

* Cursor: Engineer-focused vibe coding platform for technical development

* Fathom: 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

* The Kiln

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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: Primary data manipulation and orchestration platform for campaign building

* Smartlead: Email sequencing platform for outbound distribution (Kellen is certified partner)

* ChatGPT: AI assistance for message creation and data processing with 4.0 mini for cost efficiency

* Scaled Mail: Infrastructure provider for email deliverability (Dean gets the shoutout)

* Lead Magic: Email validation and email finding data source

* Miro: Kellen's secret weapon for visualizing ideas and client collaboration

* RSS Feed: Creative data source for podcast guest scraping and engagement

* HubSpot: 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

* The Deal Lab

* Clay Cafe

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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: The orchestration powerhouse that can make 72 API calls per row for enrichment

* Octave: Most underrated GTM tool for messaging and copywriting

* Claude: Superior for copywriting and email generation over other LLMs

* Instantly/Maildoso: Email infrastructure and sequencing platform combination

* Manus AI: Advanced research platform delivering PhD-level competitive analysis

* Apify & Firecrawl: Web scraping tools for unique data acquisition

* PandaMatch: Lookalike modeling for prospect identification

* HubSpot/Salesforce: CRM platforms (Josh uses both depending on client needs)

* Model Context Protocol (MCP): Advanced Claude integration for enhanced workflows

* Cursor & Lovable: No-code development tools for rapid prototyping

* Delphi: 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

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: Wesley's preferred email infrastructure provider with 0 deliverability issues

* Octave: His top GTM tool for personalized messaging ("Octave done. Full stop")

* Clay: The orchestration platform that serves as enrichment hub

* Instantly/Smartlead: Email sequencing platforms ("doesn't matter, just choose one")

* HeyReach: For LinkedIn automation alongside email sequences

* Claude vs ChatGPT: Claude for complex tasks, ChatGPT for data use

* Apollo: 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

* Cymate

* Akaiza

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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: Bobby's preferred CRM for low barrier to entry and integrations

* Clay: The orchestration engine and integration glue for everything

* Lemlist: Multi-channel sequencer (email, LinkedIn, calling in one platform)

* Octave: Bobby's transformational AI messaging platform ("once you're on, you're on")

* Smartlead & Instantly: Alternative email-only sequencers

* Nation Graph: Public sector signal detection (board meetings, FOIA requests)

* Lead Magic: Primary enrichment tool based on monthly spend

* Pandamatch: Lead scoring and ICP fitting

* Ocean: Additional lead qualification option

* API language and JSON: Now non-negotiable for GTM engineers

* HTTP API calls: Bobby's recommended learning method over native integrations

* N8N, Make.com: Low-code platforms for workflow automation

* Claude: Bobby's preference for deep research tasks

* ChatGPT: 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

* Tam To Target

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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: Jorge's go-to CRM for most clients

* Salesforce: Enterprise CRM option

* Attio: Modern CRM Jorge wants to test

* Clay: Primary data orchestration and enrichment platform

* Lemlist: Preferred sequencer for multi-channel outreach (LinkedIn + email)

* Smartlead & Instantly: Alternative email sequencers

* RB2B: Website visitor tracking and identification

* Notion: Jorge's current CRM and wiki repository

* Apify: Go-to scraping tool with extensive actor library

* Phantom Buster: LinkedIn automation and network growth

* ChatGPT: Browser-based AI conversations

* Claude (Anthropic): API integrations within other tools

* Gemini, DeepSeek: Additional AI model options

* Reoon: Underrated email verifier ($80 for 100k verifications + 500 daily credits)

* NeverBounce: Alternative email verification

* Apollo, Lead Magic, Prospeo: 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

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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."

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Watch the full interview here at our Youtube channel:

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.

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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.

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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

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