Alex breaks down what content engineering actually means: building the systems infrastructure to maintain quality, freshness, and brand accuracy across everything a company has ever put online. He makes the counterintuitive case that great content engineering puts more humans into the content process.More
Category Archives: AI
219: Elizabeth Dobbs: Inside Databricks’ stack with 3 AI agents, 1 lakehouse, and 6 years of data work
Liz spent 6 years at Databricks building the data infrastructure before deploying any AI on top of it. She’s shipped 3 production agents (Marge, Tagatha, and Atlas) and she’ll tell you exactly what broke first and why the team kept going anyway. You’ll hear how a marketing lakehouse becomes the foundation that makes every agent actually work, why the agent label debate is a distraction.More
218: Tata Maytesyan: Build a marketing career that survives AI as a deep generalist
Tata breaks down why the best AI automation targets are the boring, repeatable tasks nobody talks about on LinkedIn, and why the specialist-to-generalist shift in marketing is already happening whether your org chart reflects it or not. She also gets direct about the 10,000-hour threshold for building genuine competence across domains, and the self-preservation fear she hears from leaders every week. More
217: How to interview a company before you take the job (The Martech job hunt survival guide, part 3)
Darrell shares a firsthand account of taking a job under financial pressure, ignoring red flags he recognized in the moment, and landing in a toxic environment within months. What follows is a structured set of interview questions across 6 categories, from leadership self-awareness to what happened to the last person in the role, designed to help you separate the job offer from the job reality.More
216: How to stand out as a candidate with AI prep, portfolios and tools (The Martech job hunt survival guide, part 2)
What actually moves the needle when you’re searching for a role: building the portfolio that almost no marketing ops professional bothers to save, navigating the AI experience question, knowing when to take a contract role instead of holding out, and skipping the AI job-search tools that make you look like everyone else.More
215: How to find hidden job opportunities (The Martech job hunt survival guide, part 1)
This episode is a guide for martech and marketing ops professionals navigating one of the toughest job markets in years. Phil and Darrell cover the tactical mechanics of finding roles most candidates never see. From the Ashby Google search hack to VC job boards, staffing firm pipelines, and stealth startup cold outreach, the counterintuitive moves are the most useful ones here.More
214: Austin Hay: Claude Code is creating a new class of elite marketers and the mental models that make it click
You’ll be hard pressed to find someone that understands martech and is more advanced in their Claude Code journey than Austin Hay. He maps the 2 chasms separating most marketers from big AI leverage, makes the case for a new class of professional he calls the white collar super saiyan, and walks through the automations he’s actually built.More
213: John Whalen: The next marketing advantage is pre-testing ideas on synthetic users
John has spent his career studying how people actually think, and his conclusion is uncomfortable for anyone who believes their marketing decisions are more rational than they are. In this episode, John explores how synthetic users built from cognitive science principles can fill the massive research gap that most teams quietly ignore, and why removing the human interviewer from the room might be the fastest way to finally hear the truth.More
212: Tobias Konitzer: The Causal AI revolution and the boomerang effect in marketing decision science
Tobi challenged marketing’s fixation on prediction. He has built highly accurate LTV models, but accuracy alone does not move revenue. Marketing is intervention. Correlation shows patterns; causality tells you what happens when you pull a lever. That shift reshapes experimentation, explains why dynamic allocation can outperform static A B tests, and highlights how self learning systems can backfire or get stuck in local maxima.More
211: Jenna Kellner: Overcoming frankenstacks and AI uncertainty with first principles and business judgement
Jenna is a VP of marketing that can talk about the weeds of messy systems, uncertain decisions, and personal growth. You can’t hide from it, every company accumulates tech debt as teams rush to hit revenue targets. She frames tech debt as a leadership responsibility and urges executives to reinvest in core systems when patchwork begins to outweigh building. More