
Show overview
Humans of Martech has been publishing since 2020, and across the 6 years since has built a catalogue of 234 episodes, alongside 1 trailer or bonus episode. That works out to roughly 190 hours of audio in total. Releases follow a fortnightly cadence.
Episodes typically run thirty-five to sixty minutes — most land between 40 min and 1h — though episode length varies meaningfully from one episode to the next. It is catalogued as a EN-language Business show.
The show is actively publishing — the most recent episode landed 6 days ago, with 33 episodes already out so far this year. The busiest year was 2025, with 50 episodes published. Published by Phil Gamache.
From the publisher
Future-proofing the humans behind the tech. Follow Phil Gamache and Darrell Alfonso on their mission to help future-proof the humans behind the tech and have successful careers in the constantly expanding universe of martech.
Latest Episodes
View all 234 episodes233: 4 Mindsets that modern marketing leaders need for unpredictable markets, with author Kathleen Schaub
232: How taste can be codified into systems and is no longer a durable skill, and what's next with Sharon Gai
231: Why Lattice careers and communication skills are built for the AI era, with Michele Martin from Ticketmaster
230: Zero-click marketing broke the measurement layer, so what should ops teams do now, with Amanda Natividad
229: The Privacy lawsuits coming for every marketing team, with Cara Caruso and Dustin Taylor
228: The Dispatch tower (The Dungeon of martech architecture, part 4)
227: The Correlation masquerade (The Dungeon of martech architecture, part 3)
226: The Eye of context (The Dungeon of martech architecture, part 2)
225: The Fall of CRM gravity (The Dungeon of martech architecture, part 1)
224: Keith Jones: How OpenAI’s GTM leader structures teams and spots standout candidates
223: Lindsay Rothlisberger: How Zapier uses a shared brain to manage AI context and skills
222: Ashley Langford: How Senior MOps Practitioners Are Navigating the 2026 Job Search
221: Jason Dobbs: You need Minimum Viable Readiness for AI because perfect data doesn't exist
220: Alex Halliday: How to build content engineering systems that get cited and scale without slop
219: Elizabeth Dobbs: Inside Databricks' stack with 3 AI agents, 1 lakehouse, and 6 years of data work
218: Tata Maytesyan: Build a marketing career that survives AI as a deep generalist
217: How to interview a company before you take the job (The Martech job hunt survival guide, part 3)
216: How to stand out as a candidate with AI prep, portfolios and tools (The Martech job hunt survival guide, part 2)
215: How to find hidden job opportunities (The Martech job hunt survival guide, part 1)

214: Austin Hay: Claude Code is creating a new class of elite marketers and the mental models that make it click
What's up everyone, today we have the pleasure of sitting down with Austin Hay, Martech, Revtech, and GTM systems advisor, AND – AI builder, writer, and ex-founder. In This Episode:(00:00) - Austin-audio (01:16) - In This Episode (01:54) - Sponsor: RevenueHero (02:48) - Sponsor: Mammoth Growth (04:09) - How Code-Driven AI Workflows Outperform Chat-Based Prompting (14:55) - How to Start Building With Claude Code When You Have No Time (19:45) - The Programming Concepts Non-Developers Need to Build With Claude Code (23:49) - How to Turn Repeating Prompts Into Automations That Run Themselves (31:11) - Sponsor: MoEngage (32:07) - Sponsor: Knak (33:37) - Why Spending All Your Time in Meetings Is a Career Liability (36:28) - Why the Best First Claude Code Project Is the Task That Already Annoys You (40:22) - Why T-Shaped Marketers With Claude Code Will Cover the Work of Entire Teams (46:27) - Why Marketing Taste Matters More Than Technical Skill in the AI Era (49:43) - How Early-Career Professionals Build Judgment When Entry-Level Work Gets Automated (53:14) - How Austin Hay Runs His Career as a Flywheel Austin Hay has spent 15 years moving between the technical and strategic ends of marketing, starting as the 4th employee at Branch, building and selling a mobile growth consultancy that was acqui-hired by mParticle, and eventually rising to VP of Growth before moving on to Ramp as Head of Martech. He later co-founded Clarify, a CRM startup he took from zero to $100K+ ARR while completing a Wharton MBA. Today he works as a fractional advisor to scaling companies on martech, revtech, and GTM systems, teaches thousands of practitioners through his Martech course at Reforge, and writes the Growth Stack Mafia newsletter on Substack.Austin spent months as a chatbot skeptic before Claude Code changed his view entirely. In this conversation, he maps the gap between using AI through a chat interface and wielding it as code in your actual environment, explains why meeting-heavy schedules are a compounding career liability, and makes the case for a new class of professional he calls the white collar super saiyan.---## How Code-Driven AI Workflows Outperform Chat-Based PromptingMost marketers use AI the same way they used Google in 2005. Open the interface, type something in, read what comes back, copy it somewhere. Austin Hay did this for months. He was not an early Claude Code adopter. He says this upfront, almost as a confession. He thought it was another chatbot.What broke him was specific. He was querying financial data at his startup, Clarify, through Runway, an FP&A platform connected to QuickBooks. Every SQL change required the same round trip: write the query in terminal, copy it to Claude, get feedback, paste it back, run it. He built a folder just to manage the back-and-forth. The model couldn't see his local files. The chat UI had upload limits. He was stuck in what he calls a world of calling and answering. Functional. But slow. And bounded in a way you eventually stop ignoring.Claude Code gave him access. When you type claude in a terminal, the model reads your actual files — the data as it lives in your repository, not a paste you copied, not a summary you wrote. It runs commands against your system, observes what happens, and acts on the result. The round trip ends. You stop relaying information and start working in the same environment. That is a different thing than a smarter chatbot.The shift combined with several unlocks arriving at once: Opus as a model, MCPs that worked reliably, a Max plan that made unlimited credits economical, and an agent architecture built around memory files and commands. All of it hit critical mass for Austin in January. He says the last 6 months felt like 3 years. You can hear in how he talks about it that he means it.The 2 chasms he had written about in his newsletter turned out to be real and distinct. Adopting AI at all is chasm 1. Crossing from chat to code is chasm 2. Most practitioners have cleared the first. Almost none have cleared the second. And the view from the other side, Austin says, is unrecognizable.> "It's this culmination of many things that I think really hit this critical mass in about January of this year."Key takeaway: Install Claude Code, open a terminal, point it at a folder with files you actually work with — SQL queries, drafts, data exports, notes — and run a real task on them. The gap between giving AI access to your environment and describing your environment through a chat window is immediate and felt, and that feeling is what changes the mental model.---## How to Start Building With Claude Code When You Have No TimeThe time problem is real. You have a 9-to-5. Your weekends disappear. Nobody at your company is running AI hackathons. "Learn the command line" is not advice you can act on between your Thursday syncs.Austin doesn't dismiss this. But he points at the part most people miss: they know step 1 (chat interface) and they see step 3 (Clau