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.

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## How Code-Driven AI Workflows Outperform Chat-Based Prompting

Most 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.

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## How to Start Building With Claude Code When You Have No Time

The 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 (Claude Code in terminal) and they conclude the gap is too wide. Step 2 exists. And step 2 is where everything clicks.

Anthropic's rollout is layered deliberately. Chat first: ask a question, read the answer, copy the output. Cowork space second: Claude works inside a folder on your computer, local or cloud-based, and you're giving it real files to act on. Coding interface third: terminal, commands, agents. The cowork space is a distinct step with its own payoff. It's where the model stops being a question-answering machine and becomes an environment you work inside.

> "Once people understand that Claude lives in a folder on your computer and you can throw stuff in that folder and have it work for you — that's the next step."

When you upload documents inside a Claude project and ask it to work on them, you learn something you can't get from chat: Claude lives in a folder. It acts on what's in front of it. That sounds obvious. It does not feel obvious until you've done it. And once you feel it, the jump from cowork to terminal starts feeling like a small step forward rather than a cliff.

Where this leads, eventually, is automation that runs without you. A cron job fires at 6am. A script processes your data. A workflow runs in the cloud while you're on a call or asleep. Austin maps the progression clearly: folder on your machine, then a local cron, then a cloud-deployed process that runs continuously. The people building now are building the muscle memory to get there faster. You don't have to start in the deep end. But you have to start somewhere.

Key takeaway: Start in Claude's cowork space, not the terminal. Upload a folder of documents you already work with regularly — meeting notes, a newsletter draft, recurring reports, templates — and ask Claude to perform a real task on them. That interaction builds the foundational mental model before you write a single line of code.

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## The Programming Concepts Non-Developers Need to Build With Claude Code

Austin has been saying "learn the command line" for a decade. That advice predates AI by years. The reason it matters now is completely different from the reason it mattered then.

The 3 foundations: command line (how computers work), object orientation (how APIs work), one programming language (how the web works). You don't need to master any of them. You need to understand them. Because without that base layer, you can use the tools that exist today, but you can't evaluate what Claude does when it uses them on your behalf.

> "When you have those 3 things, you can teach yourself anything."

That's the real value. When you...