Getting Sh*t Done: Tom Amies-Cull on Fixing Broken Agency Operating Models

The Negotiated Future: Ethan Settel on Newton Research, Agentic Media Buying, and the Reinvention of Media Operations

What happens when AI stops assisting media teams and starts acting on their behalf? In this episode of Signal & Noise, we sit down with Ethan Settel, Head of Sales & Accounts at Newton Research, to explore one of the most important developments in advertising today: the emergence of agentic media buying. While much of the industry remains focused on AI tools that summarize dashboards, generate briefs, or automate reporting, Newton Research is pursuing something far more ambitious. The company is building specialized AI agents that can connect data, execute advanced analytics, build forecasting models, recommend optimizations, and increasingly interact directly with media platforms to support planning, buying, and activation.  At the center of our conversation is a provocative thesis: the future of advertising may be negotiated by machines. Rather than relying on human teams to manually interpret reports, adjust budgets, and coordinate across dozens of disconnected systems, agentic platforms like Newton are creating teams of AI specialists that can work together to analyze campaign performance, simulate scenarios, and execute media decisions with unprecedented speed and precision. These agents can communicate with DSPs, publishers, clean rooms, and planning tools, transforming what has historically been a fragmented and labor-intensive process into an increasingly automated operating model.  We also discuss Newton’s groundbreaking work with NBCUniversal, FreeWheel, Yahoo, and Locality, where buy-side and sell-side AI agents collaborated to support premium video buying across linear television and streaming. The initiative offers a compelling glimpse into a future where software agents negotiate inventory directly with one another, potentially reshaping the role of DSPs, SSPs, and other intermediaries throughout the advertising ecosystem.  Along the way, Ethan explains why measurement and analytics are the foundation of any effective agentic system. Without trusted data, consistent models, and full transparency into how decisions are made, automation simply amplifies errors. Newton addresses this by combining its proprietary marketing science knowledge base with each client’s unique methodologies, creating a repeatable and highly customized intelligence layer that becomes more valuable over time.  We also explore some of the biggest questions facing the industry: * How AI agents differ from generic tools like OpenAI ChatGPT, Anthropic Claude, and Google Gemini * Why data normalization has historically consumed most of a data scientist’s time * How agentic systems can democratize advanced analytics for planners and buyers * The role of protocols such as MCP and AdCP in enabling agent-to-agent communication * Whether DSPs and SSPs become strategic platforms or simply “dumb pipes” * Where liability and accountability sit when AI begins making media decisions * Why human oversight remains essential, even as automation accelerates Ethan also shares his perspective on the organizational impact of agentic AI. Rather than replacing media professionals outright, he argues that the technology frees analysts, planners, and buyers from repetitive manual work, allowing them to focus on strategy, experimentation, and innovation. The result is not fewer insights, but potentially unlimited analytics applied to every campaign and every decision.  This conversation offers a rare and highly practical look at what applied AI actually looks like inside advertising. It moves beyond hype to examine how real systems are being deployed today to transform measurement, planning, and activation. If Ethan is right, the future of media will not be defined by faster reporting or prettier dashboards. It will be defined by intelligent agents negotiating with one another across the buy-side and sell-side, continuously optimizing outcomes in a market that becomes more automated, transparent, and data-driven than ever before. The negotiated future has already begun.

The Godfather of MarTech on the Great Convergence: Scott Brinker on Al, First-Party Data, and the Future of MadTech

The MarTech landscape exploded from a few hundred tools to more than 15,000. But according to Scott Brinker, the real story isn’t software sprawl — it’s the collapse of the silos between marketing, advertising, data, AI, and enterprise operations.... In this episode, we sit down with the “Godfather of MarTech” to unpack one of the biggest shifts happening in modern business: the convergence of MarTech and AdTech into a new AI-driven operating model for marketing. We explore why the old world of disconnected systems, fragmented customer journeys, and rigid SaaS categories is breaking apart — and what replaces it. From first-party data and composable architecture to agentic AI and context engineering, Scott lays out a vision for a future where marketing systems become less about interfaces and workflows… and more about intelligence, orchestration, and decision-making. We also dig into: * Why the SaaS business model is under pressure * The rise of the “hyper-tail” of custom AI-built software * Why analytics may be the first MarTech category fully disrupted by AI * The hidden organizational problem behind poor data strategy * Why “context” may become the most important concept in enterprise software * How AI is changing the relationship between platforms, APIs, services, and custom development * Why brands need to rethink the divide between owned media and paid media * The future of composable MarTech stacks and semantic data layers * Why the next generation of marketers will need radically different skills Scott also shares his perspective on: * The evolution from suites → ecosystems → composable AI systems * Why traditional UI-driven software may be headed toward a major transformation * The role of first-party data in connecting customer experience across channels * Why most organizations still aren’t prepared for the operational realities of AI * And why the companies that bridge MarTech and AdTech most effectively may have a major competitive advantage in the years ahead This conversation goes deep into the infrastructure layer of modern marketing — but ultimately it’s about something bigger: how organizations make decisions in an AI-native world. If you care about: AI + marketing Composable architecture Customer data strategy Enterprise software Agentic workflows The future of SaaS Or the convergence of MarTech, AdTech, and DataTech… …this is a must-listen episode.

The Accuracy Crisis in Advertising: Scott McKinley on Bad Data and the Hidden Tax on CTV

What if nearly half of every dollar spent on Connected TV is being wasted before an ad is ever served to the right person? In this episode of Signal & Noise, Brett House and Rio Longacre sit down with Scott McKinley, one of the advertising industry’s most outspoken voices on data quality and identity. As founder and CEO of Truthset, Scott has spent years exposing a problem that sits at the heart of modern advertising: the vast majority of audience data flowing through digital and CTV ecosystems is far less accurate than marketers assume. Drawing on decades of experience spanning Nielsen, Exelate, and his own entrepreneurial ventures, Scott explains why advertising markets ultimately run on one thing: trust. When buyers and sellers lack confidence in audience quality, the result is friction, inefficiency, and billions of dollars in wasted media spend. The conversation takes a deep dive into Connected TV, where Scott argues that a hidden “accuracy tax” is undermining the promise of precision advertising. Much of today’s CTV targeting relies on probabilistic links between IP addresses and consumer identities—connections that, according to Truthset’s research, are often wrong the majority of the time. The result is a system where advertisers believe they are buying highly targeted audiences, while in reality they are frequently reaching the wrong households altogether.   Scott also shares a provocative thesis about the future of media: publishers, broadcasters, and streaming platforms must embrace authentication if they hope to compete with the walled gardens. Companies like Google, Meta Platforms, Amazon, and Netflix command premium advertising economics not simply because they have scale, but because they know exactly who their users are. Without authenticated audiences, much of the open internet risks becoming an increasingly commoditized marketplace of low-quality impressions and collapsing CPMs. Along the way, Scott and the hosts explore: * Why trust is the foundational currency of advertising markets * How bad identity linkages create massive inefficiencies in CTV * The historical role Nielsen played in establishing confidence in television advertising * Why many marketing measurement systems reward cheap reach over true effectiveness * The economic case for authenticated audiences across the open web * How publishers can dramatically increase yield by prioritizing data quality over scale * Why CPMs for truly verified audiences are likely to rise significantly in the years ahead * The need for independent standards and governance to restore confidence in digital advertising This episode is a powerful reminder that sophisticated algorithms, AI, and attribution systems are only as good as the data beneath them. If the underlying audience signals are wrong, every optimization built on top of them becomes suspect. For marketers, publishers, and technology providers alike, Scott makes the case that the future of advertising belongs to those who can prove that their data is accurate—and earn the trust that makes markets work.

Signal Break: The Real Take on the LiveRamp Acquisition by Publicis Groupe

The independent infrastructure era may be ending. In this inaugural Signal Break, we unpack one of the most consequential AdTech deals in years: Publicis Groupe [https://www.publicisgroupe.com?utm_source=chatgpt.com] acquiring LiveRamp [https://www.liveramp.com?utm_source=chatgpt.com]. Joined by Bob Walczak and Krish Raja, we break down what this deal really means for identity, clean rooms, publishers, UID2, systems integrators, and the future of the open internet. * Is this the end of “neutral” identity infrastructure? * Are HoldCos becoming walled gardens? * Does agentic AI accelerate consolidation—or make it obsolete? We get into all of it. This is the first official Signal Break: rapid-response episodes covering the biggest shifts happening across AdTech, AI, media, and infrastructure in real time. Enjoy!

The End of Marketing’s Age of Opinion: Greg Stuart on Measurement, Attribution, and Making Marketing More Predictable

What if marketing’s biggest problem isn’t a lack of data… but a lack of discipline? In this episode of Signal & Noise, we sit down with Greg Stuart, CEO of the Marketing + Media Alliance (MMA), for a deep, unfiltered conversation on why marketing still struggles to earn trust—and what it will take to fix it. Greg has spent the last several years rebuilding MMA into a global force focused on one core mission: turning marketing from a field driven by opinions, proxies, and vendor narratives into a real profession grounded in science, evidence, and predictable outcomes.  This conversation is a hard reset. Because despite more dashboards, more tools, and more AI than ever, most marketing organizations still can’t answer the one question that matters: Is this actually making better decisions—and driving real business impact? The “Age of Opinion” is Ending Greg argues that marketing has operated for decades without a codified body of knowledge—unlike finance, medicine, or engineering. And until that changes, trust from the C-suite (and especially the CFO) will remain fragile. Measurement ≠ Better Decisions * Only ~30% of marketers trust their KPIs enough to use them for strategy * Only ~29% can trace decisions back to data * Just ~22% describe their analytics capabilities as robust  The problem isn’t dashboards. It’s decision-making maturity. The Attribution Illusion From last-click to MMM, Greg breaks down why most measurement frameworks still fall short—and why marketing continues to struggle to prove value in financial terms that CFOs actually believe. AI Won’t Save Broken Foundations AI doesn’t create truth—it reflects patterns. If your assumptions, metrics, and operating model are weak, AI will simply scale those weaknesses faster. Why Marketing Lacks Trust (and How to Fix It) * No standardized “science” of marketing * Overreliance on vendors and proxies * Weak linkage to financial outcomes * A discipline still driven too often by narrative over evidence * Marketing’s credibility problem is structural, not cosmetic * Measurement maturity is organizational—not just technological * CFO trust is the ultimate test of marketing effectiveness * AI is a force multiplier—but only if the fundamentals are sound * The future belongs to teams that move from reporting → experimentation → evidence → prediction This isn’t another conversation about dashboards, tools, or tactics. It’s about whether marketing can evolve into something more rigorous, more trusted, and more predictable—or whether it continues to operate as a function driven by opinion, intuition, and fragmented incentives. If you’re a CMO, operator, or builder trying to navigate measurement, attribution, and AI… this episode will challenge how you think about all of it. Watch the full episode and join the conversation. #SignalAndNoise #Marketing #AI #Attribution #Measurement #CMO #AdTech #MarTech 🔑 What We Cover💡 Key Takeaways🎯 Why This Episode Matters