Episodios

  • 214: Austin Hay: Claude Code is creating a new class of elite marketers and the mental models that make it click

    214: Austin Hay: Claude Code is creating a new class of elite marketers and the mental models that make it click
    Apr 7 2026
    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 FlywheelAustin 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...
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    1 h y 3 m
  • 213: John Whalen: The next marketing advantage is pre-testing ideas on synthetic users

    213: John Whalen: The next marketing advantage is pre-testing ideas on synthetic users
    Mar 31 2026
    What’s up everyone, today we have the pleasure of sitting down with Dr. John Whalen, Cognitive Scientist, Author, and Founder at Brilliant Experience.Summary: 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.In this Episode…(00:00) - Intro (01:13) - In This Episode (04:31) - What Are Synthetic Users and Why Do They Matter? (10:00) - How Synthetic Users Make Stakeholders Hungry for Real Human Research (15:56) - Pre-Testing on Synthetic Users: Shortcut or Smart Step? (18:53) - How to Actually Build a Synthetic User: Tools, Layers, and Agentic Systems (40:51) - Is the Average Persona Dead? Scale, Diversity, and the World Model (43:01) - Asking the Uncomfortable Questions: What AI Agents Reveal That Humans Won't (49:30) - Ending the Quant vs. Qual Debate with Statistically Relevant Qualitative Data (56:37) - Mining the 'Why' Behind Silent Behavioral Data with Synthetic Users (01:02:31) - Designing for Agent Users: The Coming Shift to Human-and-Machine-Centered Design (01:05:28) - The Happiness Question: Dogs, Nature, and Staying AnalogAbout JohnDr. John Whalen is a Cognitive Scientist, Author, and Founder of Brilliant Experience, where he applies cognitive science principles to help organizations design products and experiences that align with how people actually think and make decisions. He’s also an educator, teaching two AI customer research courses on Maven.His work explores the intersection of human psychology and marketing, including the emerging practice of pre-testing ideas on synthetic users to give brands a faster and more informed competitive edge. He is also the author of a book on the science of designing for the human mind, bringing academic rigor to practical business challenges.How Synthetic User Research Works and When to Trust ItSynthetic user research sounds like something creepy out of a dystopian science fiction film, and John is the first to admit the terminology does nobody any favors. When asked about what synthetic users actually are and what they mean for research, he admited: if he had been on the branding team, he would have pushed hard for something like “dynamic personas” instead. The name creates unnecessary friction before the conversation even starts. And that friction matters when you’re trying to get skeptical executives or methiculous researchers to take the whole thing seriously.Under the hood, specialized AI tools simulate how a defined audience segment would respond to a question, concept, or stimulus, without recruiting, scheduling, incentivizing, or waiting on real human participants. John runs a class where he collects genuine human data first, then feeds comparable inputs into these tools to benchmark accuracy head-to-head. The results are pretty wild. AI-generated responses align with real human findings somewhere between 85% and 100% of the time on major topics and consumer needs. That is not a peer-reviewed clinical trial, and John is not pretending otherwise. But 85% alignment is enough signal to stop reflexively dismissing the method and start asking harder, more specific questions about exactly where it fits into a research stack.So what does this mean for you and your company though? Think all the decisions that currently live in a black hole of zero structured input. How many product calls, campaign concepts, and messaging pivots happen with nothing more than a conference room full of people who all read the same talking heads on LinkedIn? John argues that low cost, round-the-clock accessibility, and minimal public exposure make these tools a natural fit for precisely those moments: pressure-checking a hypothesis at 11pm, testing whether a pitch direction even makes sense before it touches a client, or deciding whether a concept deserves the time and money required for proper validation.“If these are only going to keep getting better and better, which they are, then logically, what kinds of decisions right now go completely by gut and no research, and what could we use to help us frame that?”One of the more underappreciated angles John raises is global inclusivity. Large organizations routinely test in the US and Western Europe, then extrapolate those findings to markets in Southeast Asia, Latin America, or Sub-Saharan Africa because local research budgets simply do not exist. Big nono. Synthetic personas trained on broader, more representative data could at minimum provide directional signals for those markets, making research more geographically honest without a proportional spike in spend.The early AI bias problem, where models essentially mirrored the...
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    1 h y 8 m
  • 212: Tobias Konitzer: The Causal AI revolution and the boomerang effect in marketing decision science

    212: Tobias Konitzer: The Causal AI revolution and the boomerang effect in marketing decision science
    Mar 24 2026
    Summary: 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. It also fuels his skepticism of unleashing agentic AI on historical data without a causal layer. If you want to change outcomes instead of forecast them, your systems need to understand levers and log decisions you can actually audit.(00:00) - Intro (01:22) - In This Episode (04:07) - Why Predictive Models Fail Without Causal Inference (09:49) - How to Validate Causal Impact on Customer Lifetime Value (13:04) - Reducing Uncertainty Around Causal Effects by Optimizing Levers, Not Labels (17:01) - Why Dynamic Allocation Works Better Than Fixed Horizon A B Testing (31:54) - The Boomerang Effect and Why Uninformed AI Sabotages Early Results (40:15) - Escaping Local Maxima and The Failure of Randomly Initialized Decisioning (44:04) - Why Agentic AI Trained on Data Warehouse Correlations Reinforces Bias (49:00) - The Power of Composable Decisioning (53:06) - How Machine Decisioning Transcends Marketing (01:01:41) - Why Clear Priority Hierarchies Improve Executive Decision MakingAbout TobiasTobias Konitzer, PhD is VP of AI at GrowthLoop, where he’s chasing closed-loop marketing powered by reinforcement learning, causality, and agentic systems. He’s spent the past decade focused on one core problem: moving beyond prediction to actually influencing outcomes.Previously, Tobi was Chief Innovation Officer at Fenix Commerce, helping major eCommerce brands modernize checkout and delivery with machine learning. He also founded Ocurate, a venture-backed startup that predicted customer lifetime value to optimize ad bidding in real time, raising $5.5M and scaling to $500K+ ARR before its acquisition. Earlier, he co-founded PredictWise, building psychographic and behavioral targeting models that drove over $2M in revenue.Tobi earned his PhD in Computational Social Science from Stanford and worked at Facebook Research on large-scale ML and bias correction. Originally from Germany and based in the Bay Area since 2013, he writes frequently about causal thinking, machine decisioning, and the future of marketing.Why Predictive Models Fail Without Causal InferencePrediction dominates most marketing roadmaps. Teams invest months refining churn models, tightening confidence intervals, and debating which threshold deserves a campaign. Tobi built an entire company on that logic. His team produced highly accurate lifetime value predictions using deep learning and granular event data. The forecasts were sharp. The lift curves were clean. Buyers were impressed.Then lifecycle marketers asked a more uncomfortable question: what action should follow the score?A predictive model encodes the current trajectory of a customer under existing policies. It describes what will likely happen if nothing changes. Marketing changes things constantly. The moment you intervene, you alter the system that generated the prediction. The forecast reflects yesterday’s conditions, not tomorrow’s strategy.> “Prediction tells you the future if you do nothing. Causation tells you how to change it.”Consider the Prediction Trap.On the left, the status quo labels a person as high churn risk. The function is observation. The outcome is a description of what happens if you leave the system untouched. On the right, a lever gets pulled. The function is intervention. The outcome is directional change.That shift in function changes how you work.Prediction thinking centers on segmentation:Who is likely to churn?Who is likely to buy?Who looks like high LTV?Causal thinking centers on levers:Which incentive reduces churn?Which sequence increases repeat purchase?Which offer raises lifetime value incrementally?Tobi often uses an LTV example to expose the trap. Suppose high LTV customers frequently viewed a specific product early in their journey. A team might redesign the onboarding flow to feature that product more aggressively. The correlation looks persuasive. The causal effect remains unknown.Several alternative explanations could drive the pattern:The product may correlate with a specific acquisition channel.The product may have been highlighted during a limited campaign.The product view may signal prior brand familiarity.Only an intervention test can estimate incremental impact. Correlation can guide hypothesis generation, but it cannot validate the lever itself.Tobi also highlights a deeper issue. Acting on predictions introduces compounding uncertainty across multiple layers:The predictive model carries statistical variance.The translation from model features to campaign strategy introduces interpretation bias.The ...
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    1 h y 5 m
  • 211: Jenna Kellner: Overcoming frankenstacks and AI uncertainty with first principles and business judgement

    211: Jenna Kellner: Overcoming frankenstacks and AI uncertainty with first principles and business judgement
    Mar 17 2026
    What’s up everyone, today we have the pleasure of chatting with Jenna Kellner, VP Marketing at Workleap.(00:00) - Intro (01:14) - In This Episode (04:30) - How to Manage Marketing Tech Debt During Rapid Growth (10:10) - How to Prioritize RevOps Tech Debt Without Perfect ROI Models (14:23) - Reasoning Through Broken Systems and Imperfect Data (19:23) - How High Performers Progress Anyway (24:28) - How to Build Confidence With AI Through Small Experiments (33:06) - How to Use Exit Planning and Cost Benefit Analysis for AI Tool Selection (35:57) - First principles matter more than tools (38:59) - Why Staying Close to Execution Improves Marketing Leadership (45:13) - Why Critical Thinking Skills Drive Marketing Career Growth (49:33) - How to Build Business Judgment in Technical Marketing Roles (53:03) - Why Confidence Without Humility is Dangerous (55:47) - How Revenue Leaders Prioritize Daily Energy (59:49) - Growing up (01:01:10) - Book recSummary: 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. If leadership doesn’t get it, the best way to prioritize it is to shape it as an opportunity cost and lost leverage that will drain revenue the longer we wait. In the face of AI uncertainty, she argues that judgment compounds faster than technical knowledge, and that the marketers who become indispensable blend business awareness, proximity to execution, and decisive action grounded in humility.About JennaJenna Kellner is Vice President of Marketing at Workleap and a revenue-focused marketing leader who has spent more than a decade building marketing teams and scaling companies. She brings experience across Enterprise, SMB, D2C, SaaS, two-sided marketplaces, venture studios, and other high-growth environments.Her career spans senior leadership roles at Minerva, On Deck, RBCx, and Ownr, where she led marketing, growth, and revenue functions inside complex, evolving organizations. At RBCx, she served as Chief Growth Officer for Ampli and directed marketing and growth initiatives within a large financial institution setting. She has also co-founded communities such as GrowthToronto and Little Traders, reflecting her commitment to building networks and businesses in parallel.Jenna operates with a strong sense of ownership and accountability, grounded in her belief that every challenge ultimately becomes her responsibility to solve. Recognized as a WXN Top 100 Women in Canada, she focuses on developing high-performing teams that connect strategy to execution and translate marketing into measurable revenue impact.The Frankenstein Reality of Managing Tech Debt: How to Manage Marketing Tech Debt During Rapid GrowthYou know it.. Most marketers are operating inside half-connected systems. No company has a pristine, perfectly synchronized tech stack. Even if they think they do, it doesn’t last. Growth creates pressure, and pressure produces shortcuts. Jenna has seen the same cycle in startups and enterprise environments. In the early days, teams build whatever gets the job done. They start in spreadsheets, layer on point solutions, wire tools together with lightweight integrations, and move fast because revenue matters more than architecture.Those early decisions never disappear. They compound. Years later, larger organizations inherit layers of systems that were added at different stages of maturity. Tools do not scale in sync. One platform gets upgraded. Another stays frozen because a team depends on it. Reporting becomes an exercise in orchestration. Jenna recalls walking into an organization where a sales leader pulled her weekly report from eight separate tools. That routine consumed time, drained energy, and normalized operational friction.“You have to Frankenstein your way through them to get the answers you need.”That sentence captures the daily reality inside many marketing and revenue teams. Quarter-end reporting still happens. Board decks still go out. The numbers get assembled through exports, CSV files, manual joins, and late-night reconciliation. Leadership often tolerates the strain because revenue continues to land. But the cost isn’t super visible:Reporting cycles stretch longer each quarter.Forecast confidence erodes.Team morale dips as manual work expands.Strategic decisions rely on partial or inconsistent data.So how do we get out of this mess? Jenna views this as a leadership obligation. Someone has to decide that cleaning house earns priority alongside pipeline generation. She describes working with a founder who paused other initiatives to repair core systems. The work moved slowly. It required budget discipline and uncomfortable trade-offs. It rebuilt trust in data and freed ...
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    1 h y 2 m
  • 210: Ronald Gaines: 6 Things the next generation of marketing ops leaders must learn

    210: Ronald Gaines: 6 Things the next generation of marketing ops leaders must learn
    Mar 10 2026
    What’s up folks, today we have the pleasure of sitting down with Ronald Gaines, Digital Transformation & Marketing Ops Leader at Sunbelt Rentals, Inc.(00:00) - Intro (01:12) - In This Episode (06:18) - 1. Learning to Operate Without Formal Authority (13:59) - 2. Stop Waiting for the Org to Define Your Marketing Ops Role (22:53) - 3. The Hidden Cost of Self Taught Ops and Minimum Viable Discipline (31:46) - 4. Thinking in Products Instead of Tasks (39:15) - 5. Data Discipline Outlasts Any Platform (48:38) - 6. How to Design a Marketing Ops Intake Process That Protects Team Capacity (52:18) - Personal Energy Allocation Framework For Marketing Ops LeadersSummary: Ronald shares a framework for marketing operations leaders to move from reactive support into proactive systems authority by building influence through measurable credibility, structured intake processes, and disciplined governance. It argues that operational work should be managed like a product with clear boundaries, documented standards, and strong data discipline, which protects team capacity, prevents burnout, and makes impact visible to the business. By defining their own role and communicating value in commercial terms, operators convert technical execution into durable strategic leverage.About RonaldRonald Gaines is a Digital Transformation and Marketing Operations leader who builds scalable revenue engines across complex enterprise environments. He combines strategic direction with hands-on expertise in marketing automation, data architecture, analytics, and customer experience optimization.As Senior Manager of MarTech and Data Analytics at Sunbelt Rentals, he leads the enterprise martech roadmap, governs lead management and data integrity, and aligns marketing technology with measurable revenue outcomes. His experience across Cisco, Dell, and global consulting engagements reflects a consistent focus on operational rigor, system design, and performance-driven growth.Outside of work, Ronald is a dedicated fan of comic books and graphic novels, with a particular appreciation for mech stories and towering kaiju battles. He is also launching a nonprofit focused on building youth leaders and strengthening communities, speaks at career days to introduce young people to digital marketing, and is committed to serving families and helping the next generation build a path toward a thriving, stable quality of life.1. Learning to Operate Without Formal AuthorityMarketing ops leaders operate at the center of execution. Campaigns depend on them for tracking, lifecycle depends on them for clean product data, and growth teams depend on them for accurate reporting. Work flows through their systems every day. Authority often sits somewhere else.We describe this tension as an authority paradox. You touch everything. You own very little. Influence becomes the mechanism that moves work forward.Ronald believes influence grows from operational credibility. Ops leaders who become indispensable demonstrate rigor and produce dependable outcomes with quantifiable business impact. They can show how their work reduces launch time, decreases system incidents, improves data accuracy, or drives measurable revenue lift. When the numbers are visible, stakeholders treat the function differently.“If you cannot quantify the work that you’re doing for the business and the impact that it is making, it becomes very hard to have the influence and authority you need to push back and protect your bandwidth.”That perspective shifts the conversation from personality to proof. Relational influence still matters. Cross functional trust smooths collaboration. Operational influence carries more weight because it compounds. When a team consistently delivers outcomes that are measured and shared, credibility grows with each cycle.Ronald points to structure as the starting point. A centralized intake process creates visibility and discipline. A mature intake process includes:A required business outcome for every request.An estimated level of effort based on real sizing.A defined metric tied to revenue, cost savings, risk reduction, or speed.A transparent prioritization rubric that stakeholders can review.When every request moves through this filter, conversations become sharper. Trade offs move from hallway debates to documented decisions. You protect capacity because the impact is visible. You prioritize high value work because the math supports it.He also encourages ops leaders to create formal deliverables that showcase impact. Publish a quarterly ops impact report. Share a dashboard that tracks launch velocity. Track incident reduction over time. Circulate a capability roadmap tied to revenue targets. These artifacts signal accountability. Accountability grants the authority to set priorities and allocate resources.Influence grows when stakeholders associate your involvement with consistent business gains. Teams start asking for your perspective earlier in the planning process...
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    59 m
  • 209: Maria Solodilova: Why Adtech is really a marketplace with its own economics

    209: Maria Solodilova: Why Adtech is really a marketplace with its own economics
    Mar 3 2026
    What’s up everyone, today we have the pleasure of sitting down with Maria Solodilova, Head of Business Development at Yango Ads.(00:00) - Intro (01:17) - In This Episode (04:23) - Mobile Ad Mediation Business Development Explained (09:58) - AI Credibility In Ad Tech Sales (18:42) - Why Adtech is Really a Marketplace With Its Own Economics (30:30) - Programmatic Ad Auctions And Inventory Dynamics (35:22) - Building Trust in Programmatic Advertising Transparency (43:39) - The Future of Contextual Advertising (46:47) - Buy-in Tip (48:03) - Books Recommendations (51:07) - Happiness SystemSummary: Maria takes us on a guided tour across the adtech landscape from a bird’s-eye view, describing a real-time marketplace where mobile ad mediation converts app usage into revenue through auctions that price every impression. She explains how supply-side work at Yango Ads centers on SDK integration, auction behavior, and performance tradeoffs that directly shape earnings once systems operate in production. The conversation frames adtech as a market governed by supply, demand, and incentives, which explains why performance shifts often outrun planning models and attribution frameworks. She grounds AI and transparency in observable mechanics, showing how reconciled data, clear ownership, and contextual execution support trust and durable monetization.About MariaMaria Solodilova leads global business development at Yango Ads, where she oversees revenue growth and strategic partnerships for an AI-driven mobile ad monetization platform. She manages distributed teams across the United States, China, Southeast Asia, and Latin America, with consistent delivery of seven-figure quarterly revenue and sustained performance above enterprise sales targets.Her career spans more than a decade across North America, Europe, and Latin America, with senior roles in AdTech, SaaS, and LegalTech. Before joining Yango Ads, Maria led international business development at Yandex, where she launched AI-based B2B products into APAC, LATAM, and MENA markets, shortened sales cycles through stronger qualification, and increased average contract value.Earlier roles at BrandMonitor and KidZania placed her in direct collaboration with Fortune 500 brands and executive leadership teams on complex, multi-market commercial partnerships. Her work consistently centers on enterprise sales execution, partner ecosystems, and monetization strategy in competitive mobile and platform-driven markets.Mobile Ad Mediation Business Development ExplainedMobile ad mediation explains how free apps generate revenue without charging users directly. The system converts attention into income through auctions that run inside apps every time an impression becomes available. Maria frames the work in plain terms when she talks to people outside adtech. Users open familiar apps, skip payment screens, and still participate in a transaction. Attention becomes the currency, and ads become the exchange mechanism.“When you are not paying for the product, chances are you might be one. You are paying with your attention.”Mediation platforms sit at the center of that exchange. Multiple ad networks bid for each impression in real time, and the highest bid wins access to a specific user. Maria’s role focuses on the supply side at Yango Ads, where her team works with mobile app developers and game studios. They integrate the SDK, tune performance, and make sure the auction behaves in ways that maximize revenue without degrading the app experience.The work demands technical fluency because developers expect concrete answers. A normal week includes discussions about factors that materially affect earnings, such as:SDK weight and its impact on app performance.Latency and how slow auctions affect fill rates.Competition density across ad networks.User experience tradeoffs that influence retention and ad tolerance.These conversations move quickly from high-level strategy to implementation details. Credibility depends on understanding how the auction behaves in production, not how it sounds in a pitch.The revenue dynamics often surprise people. Large payouts do not always come from enterprise publishers with recognizable logos. Maria has seen individual developers build a single game, monetize through ads, and generate seven-figure income. These outcomes come from timing, execution, and exposure to competitive bidding, rather than procurement cycles or brand recognition. That possibility keeps many operators engaged in the space, even as the vocabulary around ads grows tired and recycled.Business development in mediation operates as a bridge between market mechanics and human outcomes. The role connects developers who want predictable income with systems that price attention at scale. Clear explanations, technical competence, and realistic expectations shape long-term partnerships more than lofty promises ever could.Key takeaway: Mobile ad mediation monetizes attention through real-time...
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    54 m
  • 208: Anthony Rotio: Exploring causal context graphs and machine customers, starting in retail media networks

    208: Anthony Rotio: Exploring causal context graphs and machine customers, starting in retail media networks
    Feb 24 2026
    What’s up folks, today we have the pleasure of sitting down with Anthony Rotio, Chief Data Strategy Officer at GrowthLoop.(00:00) - Intro (01:10) - In this episode (04:05) - Journeying From Robotics to Modern Marketing Systems (11:05) - Most Marketing Systems Don’t Learn Because They Lack Feedback Loops (16:10) - The Martech Engineering Talent Gap (19:51) - AI Will Amplify Whoever Has the Cleanest Causal Feedback Loop (29:17) - Agent Context Graphs for Drift Detection in Marketing Systems (31:51) - Humans Will Set Hypotheses, AI Will Accelerates Iteration (35:50) - The Evolution of Retail Media Networks (45:07) - How Commerce Networks Redefine Targeting With Governed Data (48:26) - How Agent to Agent Commerce Operates Inside Marketing Funnels (53:04) - Google Universal Commerce Protocol Explained (54:43) - Personal Happiness System (56:30) - Favorite BooksSummary: Anthony traces a path from robotics and computer science to his current role where he approaches marketing as an engineering system. He explains how execution-first marketing stacks weaken feedback loops and fragment data, which slows learning and iteration. He introduces the agent context graph as a causality model that lets AI simulate and predict customer behavior with greater confidence. The conversation also covers retail media networks, first-party data monetization through governed access, and a shift toward zero-to-zero marketing driven by agent-to-agent transactions. He closes by stressing that strong data foundations determine who can compete as marketing becomes more automated and agent-driven.About AnthonyAnthony Rotio is the Chief Data Strategy Officer at GrowthLoop, where he leads partnerships and builds generative AI product features for marketers, including multi-agent systems, AI-driven audience building, and benchmarking and evaluation work. He previously served as GrowthLoop’s Chief Customer Officer, where he built and led teams across data engineering, data science, and solutions architecture while supporting product development and strategic sales efforts.Before GrowthLoop, Anthony spent nearly six years at AB InBev, where he led a $100M owned retail business unit with full P&L responsibility and drove major growth through operational and digital transformation work. He also led U.S. marketing for Budweiser, Bud Light, Michelob Ultra, Stella Artois, and other brands across music, food, and related consumer programs. He earned a B.A. in computer science from Harvard, played linebacker on the Harvard football team, founded the consumer product Pizza Shelf, and holds a Google Professional Cloud Architect certification.Journeying From Robotics to Modern Marketing SystemsAnthony’s career started far away from marketing. He trained as a computer scientist and spent his early years working with robotics and reinforcement learning. His first exposure to a learning agent left a lasting impression because the system behaved less like traditional software and more like something adaptive. That experience shaped how he would later think about work, systems, and feedback. He learned early that progress comes from loops that learn, not static instructions.That mindset followed him into an unexpected chapter at AB InBev. Anthony entered a world defined by scale, brands, and operational complexity. He treated his technical background like a carpenter treats tools, useful only when applied to real problems. Running marketing across major beer brands taught him how value is created inside large organizations. It also exposed a recurring issue. Marketing teams had ambition and data, but execution moved slowly because ideas had to travel through layers of translation before anything reached customers.That friction became impossible to ignore. Audience definitions moved through tickets. Campaigns waited on queries. Data teams became bottlenecks through no fault of their own. Anthony felt the pull back toward technology, where systems could shorten the distance between intent and action. That pull led him to GrowthLoop, where he joined early and worked directly with customers. The appeal was immediate. The product connected straight to cloud data and removed several layers of mediation that marketing teams had accepted as normal.As language models improved, Anthony recognized a familiar pattern. Audience building behaved like a translation problem. Marketers described people and intent in natural language, while systems demanded structured logic. Early experiments showed that natural language models could close that gap. Anthony framed the idea clearly.“Audience building is a translation problem. You start with a business idea and you end with a query on top of data.”Momentum followed quickly. Customers like Indeed and Google responded because speed changed behavior. Teams experimented more often and refined audiences based on results instead of assumptions. Conversations with Sam Altman and collaboration with OpenAI reinforced ...
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    59 m
  • 207: Building a career that doesn't hollow you out (50 Operators share the systems that keep them happy, part 3)

    207: Building a career that doesn't hollow you out (50 Operators share the systems that keep them happy, part 3)
    Feb 17 2026
    "Hey – So what do you do?” Why is it that we always default to work when we get this question. its like many of us have let our jobs become the center of how we see ourselves. This slowly happens to many of us, as work occupies more mental and emotional space.I asked 50 people in martech and operations how they stay happy under sustained pressure. This 3 part series – titled “50 Operators share the systems that keep them happy” explores each of these layers through the lived experience of operators who feel the same pressure you probably feel right now.Today we close out the series with part 3: meaning. We’ll hear from 19 people and we’ll cover:(00:00) - Teaser (01:08) - Intro / In This Episode (04:27) - Rich Waldron: Auditing Whether Work Is Actually Moving (06:49) - Samia Syed: Tracking Personal Growth (08:33) - Jonathan Kazarian: Tracking Growth Across Life Health and Work (10:11) - Kim Hacker: Choosing Roles With Daily Visible Impact (12:21) - Mac Reddin: Checking Work Against 3 Personal Conditions (14:11) - Chris Golec: Choosing Early Stage Building Work (15:19) - Hope Barrett: Feeding curiosity across multiple domains (17:45) - Simon Lejeune: Treating work like a game (19:52) - Ana Mourão: A mental buffer between noticing and doing (21:46) - Tiankai Feng: Anticipation planning (25:30) - István Mészáros: Choosing Who You Are When Work Ends (29:52) - Danielle Balestra: Feeding Interests Unrelated to Work (31:42) - Jeff Lee: Continuing to Build Personal Projects After the Workday Ends (33:23) - John Saunders: Keeping a builder practice outside of work (34:41) - Ashley Faus: Group Creative Rituals Outside of work (37:40) - Anna Aubucho: Maintaining a second self through solo creative practice (39:56) - Ruari Baker: Preserving Identity Through Regular Travel (42:15) - Guta Tolmasquim: Building a personal product roadmap (45:37) - Pam Boiros: Feeding identities that have nothing to do with work (47:52) - OutroAll that and a bunch more stuff after a quick word from 2 of our awesome partners.A lot of the operators I chatted with don’t talk about happiness like it suddenly arrives. They describe it as something you feel when things actually start to move. Our first guest gets there right away by tying happiness directly to progress, the kind that tells you you’re not stuck.Rich Waldron: Auditing Whether Work Is Actually MovingFirst up is Rich Waldron, Co-founder and CEO at Tray.ai. He’s also a dad, and a mediocre golfer.Progress sits at the center of Rich’s definition of career happiness. He treats it as a felt sense rather than a dashboard metric. When work advances in a direction that makes sense to him, his energy steadies. When that movement slows or stalls, frustration surfaces quickly and spreads into everything else. That feeling becomes a cue to examine direction rather than effort.“Happiness is mostly driven by progress.”That framing resonates because it names something many operators struggle to articulate. Long hours can feel sustainable when the work moves forward. Light workloads can feel draining when days repeat without traction. Progress gives work narrative weight. It answers a quiet internal question about whether today connects to something that matters tomorrow.Rich also points to patterns that erode meaning over time.Roles with little challenge dull attention, even when the pay is generous.Constant activity without visible change breeds irritation that lingers after work ends.Both conditions interrupt momentum. The mind keeps searching for movement that never arrives. Rest stops working because unresolved motion occupies every quiet moment.Progress also shapes identity beyond work. When things move in the right direction, attention releases its grip on unfinished problems. Rich links that release to showing up better at home. He describes being more present as a parent because mental energy is no longer trapped in work that feels stuck. Forward motion restores proportion. Work keeps its place as one part of a full life rather than the dominant one.Balance emerges as a byproduct of this orientation. You choose problems that move. You notice when progress fades. You adjust before frustration hardens into burnout. That rhythm preserves meaning over long career arcs and keeps work aligned with the person you want to remain.Key takeaway: Track progress as a signal of meaning. When your work moves in a direction you respect, it stays contained, your identity stays intact, and the rest of your life receives the attention it deserves.Samia Syed: Tracking Personal GrowthThat’s Samia Syed, Director of Growth Marketing at Dropbox. She’s also a mother, outdoor fanatic, and an avid hiker.Progress became the scorecard Samia relies on to keep her career from consuming her sense of self. Early professional years trained her to chase perfection, because perfection looked measurable, respectable, and safe. That mindset quietly tightened the frame around what counted ...
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