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
Tag Archives: AI agents
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
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
191: Aboli Gangreddiwar: Self healing data agents, hivemind memory curators and living documentation
Aboli and Phil explore AI agent use cases like self-healing data quality agents, AI hivemind memory curator, documentation agents and AI browsers and a bunch more!More
189: Aditi Uppal: How to capture, activate and measure voice of customer across go to market efforts
Aditi shows how five honest conversations can reshape how you read data, because customer language carries context that numbers miss. She points to overlooked signals like product usage trails, community chatter, sales recordings, and event conversations, then explains how to turn them into action through a simple pipeline of capture, tag, route, track, and activate. More
186: Olga Andrienko: Ex-VP at Semrush left her 35-person brand team to build AI for marketing ops
Olga thought she was ahead of the AI curve, but a weekend course on autonomous systems showed her she was thinking too small. She pitched a shared internal AI stack at Semrush, built systems off APIs, skipped procurement by using already-approved tools, and tracked hours saved instead of promising vague ROI.More
183: Kevin White: Building a super IC role to escape management burnout and fixing the broken promise of AI SDRs
Kevin rebuilt his career around the work that fuels him. After years leading teams at Segment, Retool and Common Room, he walked away from politics and board decks to create a “super IC” role focused on experiments, product evangelism, and hands‑on growth. He applies that same mindset to go‑to‑market: strip out the bloat, ditch templated outreach, and use real buyer behavior to build small, personal campaigns.More
182: Simon Lejeune: Wealthsimple’s VP of Growth on 2 keys to be a top 5% marketer
Simon Lejeune learned early that chasing small wins keeps growth teams stuck, a lesson that landed hard when Hopper’s CEO dismissed his price‑point test as a “local maximum” and pushed him toward ideas bold enough to reshape the business. That experience drives how he leads at Wealthsimple, where he tells teams to stop polishing the same hill and start climbing new mountains.More
177: Chris O’Neill: GrowthLoop CEO on how AI agent swarms and reinforcement learning boost velocity
Chris explains how leading marketing teams are deploying swarms of AI agents to automate campaign workflows with speed and precision. By assigning agents to tasks like segmentation, testing, and feedback collection, marketers build fast-moving loops that adapt in real time. More
176: Rajeev Nair: Causal AI and a unified measurement framework
Rajeev believes measurement only works when it’s unified or multi-modal, a stack that blends multi-touch attribution, incrementality, media mix modeling and causal AI, each used for the decision it fits. At Lifesight, that means using causal machine learning to surface hidden experiments in messy historical data and designing geo tests that reveal what actually drives lift. Attribution alone can’t tell you what changed outcomes.More