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
Tag Archives: AI
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
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
208: Anthony Rotio: Exploring causal context graphs and machine customers, starting in retail media networks
The casino floor never sleeps. Lights hum, cards shuffle, and people come not just to gamble but to feel alive. While other industries went digital overnight, casinos stayed grounded in human moments, and Blair’s mission has been to connect those experiences through smarter tech.More
204: Phyllis Fang: Trust infrastructure and freakish curiosity as career growth levers
Phyllis learned how fragile marketing becomes when systems move faster than trust while working between lifecycle execution and product marketing at Uber. Safety work around emergencies, verification, and COVID forced messages to withstand scrutiny from riders, drivers, regulators, and the public.More
203: Jordan Resnick: How to distinguish fake traffic from real machine customers
Distinguishing fake traffic from real machine customers requires reading behavior. Jordan shows how AI-driven bots now scroll, click, and submit forms while inflating dashboards with activity that never converts. The signal lives in speed, sequencing, and follow-through. Teams that act protect the conversion point, block synthetic demand early, and report only after traffic earns trust.More
202: Aleyda Solís: AI search crawlability and why your site’s technical foundations decide your visibility
AI search is rewriting how people find information, and Aleyda explains the shift with clear, practical detail. She has seen AI crawlers blocked without anyone noticing, JavaScript hiding full sections of sites, and brands interpreting results that were never based on complete data.More
201: Scott Brinker: If he reset his career today, where would he focus?
Scott Brinker would build one deep specialty to judge AI’s confident mistakes, grow cross-functional range to bridge marketing and engineering, and lean into technical skills like SQL and APIs to turn ideas into working systems. More