Explore every episode of the podcast Signal & Noise

AI agents can retrieve, combine, analyze, infer from, and act on enormous amounts of data—often at a speed no human compliance team can match. But access to data does not automatically confer the right to use it. So who sets the rules, and who remains accountable when an AI system crosses the line?

In this episode of Signal & Noise, Brett House and Rio Longacre sit down with Richy Glassberg, Co-Founder and CEO of SafeGuard Privacy, for a candid and wide-ranging conversation about privacy, consent, AI governance, and the digital advertising industry’s long history of creating problems it later asks technology to solve.

Richy brings a rare perspective to the discussion. He helped build CNN.com’s commercial business, co-founded the IAB, worked across publishing, agencies, ad tech, and media, and now leads a company focused on making privacy compliance and vendor diligence standardized, operational, and auditable.

The conversation begins with a provocative argument: AI may not require an entirely new category of privacy law because AI is ultimately software—and existing rules governing data use, discrimination, consent, and accountability still apply. The real challenge is enforcing those rules as AI dramatically increases the speed, scale, and complexity of data use.

Richy explains why companies are now responsible for privacy compliance throughout their vendor chains, including the DSPs, publishers, data brokers, identity providers, models, APIs, and other partners involved in a transaction. When one black box passes data to another black box—and AI begins making decisions across the entire chain—policies and promises are no longer enough. Organizations need standardized diligence, enforceable controls, ongoing monitoring, and proof.

The group also examines why today’s consent system is fundamentally broken. Cookie banners have created consent fatigue without giving consumers meaningful understanding or control. Privacy policies are rarely read, permissions do not travel cleanly across platforms, and people can opt out in one place only to reappear in the same identity graph somewhere else.

Other topics include:

• Why an AI agent should never have more authority than the person or organization it represents
• The tension between giving AI more context and protecting individual privacy
• Why human oversight remains essential in agentic systems
• How marketers should assess and monitor every company handling their data
• Why privacy diligence must become machine-readable for real-time agent decisions
• The failure of one-to-one targeting and the industry’s obsession with questionable audience data
• How poor frequency management is damaging the connected TV experience
• Why better privacy practices could become a mark of data quality and competitive differentiation
• The threat AI-generated content poses to trusted information and the open internet
• Whether consumer-controlled data and permission agents could produce a healthier advertising ecosystem

It’s a funny, blunt, and occasionally uncomfortable conversation about what responsible data use should look like when machines can move faster than the institutions meant to govern them.

Learn more about SafeGuard Privacy: https://safeguardprivacy.com/

#ArtificialIntelligence #DataPrivacy #AIPrivacy #AIGovernance #Consent #DigitalAdvertising #AdTech #AgenticAI #PrivacyTech #MarketingTechnology #DataGovernance #ProgrammaticAdvertising #SignalAndNoisePodcast

Everyone is talking about AI. Far fewer people have spent decades actually building AI and data systems inside some of the world’s largest organizations.

In this episode of Signal & Noise, Brett House and Rio Longacre sit down with Zoher Karu, Head of AI at Taelor, to separate AI hype from what it actually takes to create measurable business value.

Zohar brings an unusually broad perspective. His career has taken him through McKinsey, Sears, Citi, eBay, Blue Shield of California, and now Taelor—an AI-powered men’s clothing rental service attempting to combine machine intelligence with human styling expertise.

Across those very different businesses, Zohar argues that the same lesson keeps resurfacing: the technology is rarely the hardest part.

The conversation starts with one of enterprise AI’s least glamorous truths: bad data doesn’t disappear because you put an LLM on top of it. As Zoher puts it, AI can simply give you “bad answers faster.” Data governance, business processes, organizational knowledge, and change management remain foundational.

From there, the discussion gets practical. Zoher explains how Taelor is attempting to teach machines something surprisingly difficult: taste. Matching clothes to a person requires understanding not just size and style, but weather, occasion, context, individual preferences, previous feedback—and even whether two individually appropriate pieces of clothing actually work together. That becomes a window into a much bigger conversation about the future of personalization.

Generative AI dramatically expands the amount of customer context businesses can process, how quickly they can respond to new signals, and the number of individualized experiences they can create. Instead of choosing among three versions of an email, brands could theoretically generate an almost infinite number of variations for individual customers.

The discussion also tackles the uncomfortable economics of enterprise AI. Companies are spending enormous amounts on models, infrastructure and tokens—but are they actually redesigning the business processes required to capture the ROI?

Zoher argues that automating pieces of an existing workflow may deliver incremental efficiency, while the much larger opportunity comes from asking whether that workflow should exist at all.

Finally, the conversation explores what may become one of the most important issues in enterprise AI: context. Agents can access data, but data alone doesn't contain all the rules, judgment and institutional knowledge humans use to make decisions. Capturing that tacit business knowledge—and making it available to AI systems—could become a critical source of competitive advantage and intellectual property.In this episode:

* Why dirty data can derail even sophisticated AI

* Why AI transformation is really organizational transformation

* The gap between AI spending and measurable ROI

* Why simply automating existing processes isn't enough

* How AI is changing personalization and recommendation systems

* How Taelor combines human stylists with machine intelligence

* Why context and business knowledge matter as much as models

* Whether AI is actually eliminating jobs or simply changing them

* Why change management may be the biggest barrier to enterprise AI

* The continuing importance of human judgment in increasingly autonomous systems

The companies that win the AI race may not be the ones with the most sophisticated models. They may simply be the ones that figure out how to build AI that people actually use.

#ArtificialIntelligence #AI #EnterpriseAI #GenerativeAI #AgenticAI #Personalization #CustomerExperience #DataStrategy #DataGovernance #MachineLearning #DigitalTransformation #AITransformation #ChangeManagement #MarTech #RecommendationEngines #FutureOfWork #SignalAndNoise #Podcast

Artificial intelligence may be the most transformative technology of our generation—but according to Fern Potter, AI alone is not a strategy.In this episode of Signal & Noise, hosts Rio Longacre and Brett House sit down with Fern Potter, Co-Founder of Intelligent Assistance, to explore why the greatest opportunity in AI isn’t replacing people—it’s amplifying human judgment.After more than two decades leading strategy, product, partnerships, and commercial growth across agencies, media, and ad tech—including serving as Chief Strategy & Growth Officer at Multilocal—Fern made the leap to entrepreneurship. Alongside her co-founders, she launched Intelligent Assistance around a simple but powerful philosophy: AI creates the most value when it enhances human expertise, context, creativity, and accountability rather than attempting to eliminate them.The conversation begins with Fern’s journey from agency leadership to founding an AI company at a moment when enterprises are rushing to deploy generative AI. She explains why so many organizations start with technology instead of business problems—and why that approach almost always leads to disappointing results.From there, the discussion explores what “intelligent assistance” actually means in practice. Fern explains how organizations should determine which work should be automated, which decisions should remain firmly human, and how AI can become a force multiplier instead of another disconnected productivity tool.Rio and Brett also dive into one of the episode’s central themes: the difference between intelligence and autonomy. Just because AI can make a decision doesn’t mean it should. Fern discusses the critical role of context, accountability, governance, and human oversight as organizations increasingly rely on AI-assisted workflows.Drawing on her experience helping reshape programmatic advertising through curation and supply-side innovation, Fern shares lessons that extend far beyond media. The trio explores how unchecked automation created inefficiencies and opacity in advertising—and why enterprise AI risks repeating many of the same mistakes if organizations optimize solely for automation instead of outcomes.The conversation also tackles larger questions about the future of work. What happens to agencies, consultancies, and professional services when small AI-enabled teams can accomplish what once required dozens of people? Which human capabilities become more valuable as technical execution becomes increasingly automated? And how should leaders redesign organizations around “thinking power” rather than simply reducing headcount?Whether you’re leading AI initiatives, building products, transforming marketing organizations, or simply trying to understand what responsible AI adoption looks like, this episode offers a thoughtful, practical framework for moving beyond the hype toward meaningful business impact.In this episode, you’ll learn:* Why AI is not a business strategy* The difference between automation, autonomy, and intelligent assistance* Why most enterprise AI initiatives fail to deliver commercial value* How to combine AI with human judgment for better outcomes* Lessons enterprise AI can learn from programmatic advertising* Why organizational redesign matters more than technology deployment* Which uniquely human skills become more valuable in the AI era* How leaders should think about governance, accountability, and trustIf you enjoy conversations about AI strategy, marketing transformation, organizational design, and the future of work, be sure to subscribe to Signal & Noise for weekly conversations with the leaders shaping the future of business and technology.#SignalAndNoise #ArtificialIntelligence #AI #GenerativeAI #FutureOfWork #HumanCenteredAI #Leadership #BusinessTransformation #Marketing #MarTech #AdTech #DigitalTransformation #EnterpriseAI #Innovation #Technology #IntelligentAssistance

For more than two decades, digital marketing revolved around a familiar goal: get people to your website.

Rank higher. Earn the click. Drive the traffic. Convert the visitor.

But what happens when the customer never makes it to your homepage?

As consumers increasingly turn to ChatGPT, Google AI Overviews, Perplexity, Gemini, Claude, and other AI-powered experiences to research products, vendors, and brands, the rules of discovery are being rewritten. Increasingly, AI is deciding which companies get mentioned, how they’re described, which sources are trusted—and which brands get recommended at all. 

In this episode of Signal & Noise, hosts Rio Longacre and Brett House sit down with Leah Nurik, CEO and Co-Founder of Brandi AI, to explore the rapidly emerging world of AI Visibility and Generative Engine Optimization (GEO). 

Leah argues that this isn’t simply another evolution of SEO. It represents a fundamental shift from a web organized around keywords, rankings, links, and clicks toward one organized around meaning, context, authority, credibility, and narrative.

The conversation breaks down the increasingly confusing landscape of SEO, Answer Engine Optimization (AEO), and GEO—and why traditional SEO isn’t disappearing. Instead, Leah sees SEO increasingly becoming one component of a broader AI visibility strategy.

But being mentioned by AI isn’t enough.

One of Leah’s most important points is that marketers need to understand the sentiment and narrative surrounding their brands inside AI-generated answers. Is the brand being represented accurately? Positively? What competitors appear alongside it? What sources are shaping the story? 

The discussion also explores one of the biggest unintended consequences of AI-powered discovery: the future of publishers and the open web. If answer engines increasingly satisfy users without sending them to the original source, what happens to referral traffic, publisher economics, and the value exchange that has supported the web for decades?

And paradoxically, AI may make some traditional marketing disciplines more important, not less. Leah makes the case that earned media and PR are poised for a resurgence because authoritative third-party sources can influence how AI systems understand and describe brands. In her view, trying to “game” AI algorithms misses the bigger opportunity: building genuine authority, credibility, and a coherent brand narrative. 

The conversation ultimately raises a bigger question for marketers: Are we moving from an era of earning the click to an era of earning the recommendation?

If so, the homepage may no longer be the front door to your brand. AI might be.

In this episode:

• Why AI-powered discovery represents a fundamental change to search
• SEO vs. AEO vs. GEO—and why the distinctions matter
• How AI systems understand and represent brands
• Why mentions, citations, sentiment, and narrative are becoming critical marketing metrics
• Why traditional SEO still matters in an AI-first world
• The surprising resurgence of PR and earned media
• Why brands shouldn’t try to “game” AI
• How AI search could reshape publisher economics and the open web
• Why marketing teams may need to reorganize around AI visibility
• What CMOs should be doing now to prepare for the next era of brand discovery

Leah’s takeaway is clear: the brands that win won’t simply be those that rank highest. They’ll be the brands that AI can find, understand, trust, cite, and ultimately recommend.

#SignalAndNoise #AI #ArtificialIntelligence #GenerativeAI #GEO #GenerativeEngineOptimization #AEO #AnswerEngineOptimization #SEO #AIVisibility #AISearch #SearchMarketing #DigitalMarketing #Marketing #MarketingStrategy #BrandStrategy #BrandDiscovery #BrandMarketing #ContentMarketing #ContentStrategy #PublicRelations #EarnedMedia #ThoughtLeadership #FutureOfMarketing #FutureOfSearch #LLM #ChatGPT #GoogleAI #Perplexity #MarTech #CMO #BrandVisibility

What becomes valuable when artificial intelligence makes information—and misinformation—nearly limitless? According to Nextdoor CEO and Co-Founder Nirav Tolia, the answer is trust.

In this episode of Signal & Noise, Nirav joins Brett House and Rio Longacre for a wide-ranging conversation about the internet’s evolution from the age of information to the age of intelligence—and why that transformation must be accompanied by a renewed age of human connection.

Nirav reflects on lessons from his career as an entrepreneur and early Yahoo employee, his return to lead Nextdoor, and the challenge of building a digital platform around real people, verified identities, and actual neighborhoods. He also speaks candidly about the unintended consequences of optimizing social platforms for short-term engagement, including how outrage and complaints can drive clicks while ultimately eroding loyalty and trust.

The conversation explores Nextdoor’s effort to move beyond the traditional attention economy. That includes protecting neighborhood conversations from outside AI models, using technology to elevate constructive local recommendations, connecting residents with small businesses, and bringing professional local journalism into the same environment as neighborhood discussion.

Nirav also shares a personal experience in which an AI system admitted to fabricating information to make its response more compelling—a moment that challenged even his deeply optimistic view of the technology. His conclusion is not that we should reject AI, but that we must pair its extraordinary intelligence with human judgment, transparency, and authentic relationships.

Brett, Rio, and Nirav discuss:

• Why trust becomes more important as AI-generated content proliferates
• The tension between building trust and removing friction
• Why Nextdoor does not license private neighborhood conversations to large language models
• How platforms can resist outrage-driven engagement loops
• The difference between advertising that interrupts and advertising that provides genuine utility
• Why verified human identity matters in a world increasingly populated by bots and AI agents
• Nextdoor’s approach to recommendations through “Faves” rather than negative star ratings
• How local journalism supports civic engagement, informed debate, and healthy communities
• The risk of AI disintermediating publishers and other original sources
• Why online conversations should become gateways to offline relationships
• How AI agents might serve residents and neighborhood businesses without pretending to be human
• Why the next generation of the internet should be measured by human value—not simply time spent

At the center of the episode is the idea of communitas: the solidarity, warmth, and shared sense of belonging that emerges when people genuinely come together. AI can summarize knowledge, accelerate work, and help us make decisions—but it cannot replace community itself.

As Nirav argues, the future should not be framed as artificial intelligence versus human beings. The opportunity is to use AI to strengthen human judgment, facilitate real-world connection, and help people love where they live.

Listen now and join the conversation about what it will take to rebuild the internet around trust, utility, and human connection.

#SignalAndNoise #NiravTolia #Nextdoor #ArtificialIntelligence #AI #TrustEconomy #Communitas #HumanConnection #FutureOfTheInternet #SocialMedia #OnlineCommunities #CommunityBuilding #LocalCommunities #LocalJournalism #DigitalTrust #TechLeadership #Entrepreneurship #ResponsibleAI #AIEthics #VerifiedIdentity #LocalBusiness #CivicEngagement #FutureOfMedia #Technology #Podcast

Dollar General might be one of the most underestimated media businesses in America.

With more than 21,000 stores, over two billion transactions annually, and 75% of Americans living within five miles of a Dollar General, DG combines enormous physical reach with high-frequency customer relationships, rich first-party data, and access to audiences that advertisers often struggle to reach elsewhere.

In this episode of Signal & Noise, Krish Raja sits down with Austin Leonard, Vice President and General Manager of DG Media Network, to explore how Dollar General is turning those assets into a sophisticated advertising business—and how AI is helping accelerate the transformation.

Austin brings experience across radio, eBay, Walmart, Sam’s Club, Rakuten and Epsilon. Today, he’s applying that combination of media, retail, identity and technology experience to a company whose advertising potential is much bigger than many people realize.

Austin explains how identity acts as a spine connecting transaction data, MyDG membership, digital coupons and other customer signals. That foundation gives DG the ability to better understand customers, build relevant audiences and connect advertising back to real commerce outcomes.

DG is also making those audiences easier for advertisers to access through partnerships and integrations including The Trade Desk and DV360.

One of the most interesting innovations is DG’s expanding in-store radio network. What started as a 6,000-store pilot is growing to roughly 12,000 locations. Working with QSIC, DG is using AI alongside inventory and sales signals to help determine the right store, day and time for advertising—and optimize campaigns based on performance.

The audience opportunity is equally compelling. Austin says that in work with The Trade Desk, adding DG audiences to campaigns has generated roughly 50% unique reach beyond audiences reached through other third-party data providers and retailers.

The conversation also explores Austin’s refreshingly practical view of AI. With more than two billion transactions annually, AI can help DG analyze signals faster, automate media planning and operational tasks, improve targeting, accelerate measurement and ultimately use predictive analytics to better anticipate customer needs.

Austin calls AI something of a “superpower” for a lean organization—especially when it eliminates manual work and allows teams to focus on higher-value problems.

Underlying everything is a simple philosophy: great retail media needs to create value for the retailer, the advertiser and the customer. It can't just be another revenue line.

And Austin closes with a great leadership principle: maintain a high “say-do ratio.” Don't just talk about what you're going to build. Deliver it.

In this episode:

• Dollar General’s massive, underestimated media opportunity

• First-party identity and closed-loop measurement

• The Trade Desk, DV360 and easier activation

• AI-powered in-store media

• Reaching rural and underserved audiences

• AI for targeting, insights and automation

• The future of retail media

• Austin’s “say-do ratio”

#RetailMedia #DollarGeneral #DGMediaNetwork #AdTech #MarTech #AI #FirstPartyData #CommerceMedia #ProgrammaticAdvertising #RetailInnovation #CustomerExperience #DigitalAdvertising #Omnichannel #MarketingTechnology #SignalAndNoise

Programmatic advertising automated the transaction. AI may automate the negotiation.

In this episode of Signal & Noise, Brett House and Rio Longacre sit down with Andrew Mole, CEO and Co-Founder of PubX, for a provocative conversation about agentic trading, publisher monetization, and what happens when AI agents begin representing both sides of the advertising market.

Andrew has spent much of his career watching programmatic evolve from a breakthrough in efficiency into an extraordinarily complex ecosystem of DSPs, SSPs, exchanges, data providers, verification platforms, and intermediaries. His argument is simple: much of this infrastructure exists because humans needed interfaces and platforms to manage complexity. AI agents don’t necessarily have the same limitation. 

That raises a bigger question: If we designed digital advertising from scratch today, would we build the programmatic ecosystem the same way?

Andrew’s answer is essentially no.

PubX is already experimenting with agent-bought and -sold media, with Andrew revealing the company is currently transacting several thousand dollars per day through emerging workflows. The volumes are still small, but agentic trading is beginning to move beyond demos and PowerPoints into actual transactions. 

The conversation explores two possible futures. In one, AI agents operate on top of today’s DSPs, SSPs, ad servers, and other infrastructure. In the more radical version, buyer and seller agents communicate directly—potentially eliminating significant portions of the traditional programmatic supply chain.

The result could be a shift from automated auctions toward autonomous negotiation, where agents negotiate not only price but audiences, 1PD, measurement, inventory quality, outcomes, and commercial terms.

For publishers, the implications could be enormous. Instead of simply accepting market prices, intelligent sell-side agents could continuously represent the value of a publisher’s inventory, audiences, data, & commercial interests at a scale no human sales organization could match. 

Andrew explains how PubX is approaching this opportunity across more than 3,000 publisher sites and why publisher 1PD becomes dramatically more valuable when machines can discover and activate it at scale. The conversation gets deep into the plumbing: OpenRTB, Prebid, ad servers, buyer agents, seller agents, and whether the industry actually needs today’s DSP and SSP architecture in an agentic future.

That leads to one of the episode’s biggest questions: Who wins and who loses?

Andrew argues pubs and advertisers have powerful economic incentives to embrace agentic trading, while intermediaries could face serious pressure. DSPs, SSPs, and agencies won’t necessarily disappear—but their roles may need to change .

The discussion also explores the future of agencies, verification and brand safety, publisher sales, and whether AI could actually strengthen the premium open web by allowing publishers to capture more of the value they create.

And this may not be a distant future. Andrew expects meaningful adoption of agentic trading to begin in 2027, with a potentially significant share of media transactions shifting in this direction. 

In this episode, we explore: agentic trading vs. traditional programmatic; buyer & seller AI agents; publisher 1PD; the future of DSPs and SSPs; OpenRTB and Prebid; agency disruption; autonomous negotiation; publisher monetization; brand safety & verification; and the future of the open web.

The big idea: Programmatic automated execution. Agentic trading could automate judgment. And once buyers and sellers are represented by intelligent agents capable of negotiating directly, the architecture—and economics—of digital advertising could look very different.
#SignalAndNoise #AgenticTrading #AgenticAI #AIAgents #AdTech #ProgrammaticAdvertising #DigitalAdvertising #PublisherMonetization #Publishers #OpenWeb #MediaBuying #FirstPartyData #PubX #FutureOfMedia #FutureOfAdvertising

Digital commerce spent the last two decades perfecting the storefront. AI may be about to make the storefront far less important.

In this episode of Signal & Noise, Brett House and Rio Longacre sit down with Bryan House, CEO of Elastic Path, to explore how AI is reshaping ecommerce, B2B commerce, product discovery—and potentially the act of buying itself.Bryan argues that commerce had actually become somewhat predictable. Platforms had spent years removing friction, optimizing conversion and standardizing the online shopping experience. Then AI arrived and reopened some of the biggest questions in the industry. One of the biggest is where product discovery happens. Increasingly, high-intent consumers aren't beginning with a traditional search engine or ecommerce site. They're asking ChatGPT and other AI assistants what they should buy. That changes the game for brands: instead of optimizing a product page for humans and keywords, companies increasingly need rich, structured product data that AI systems can understand, reason over and recommend.Bryan explains why the composable commerce movement unexpectedly created an ideal foundation for this new world. APIs, microservices and decoupled architectures weren't originally designed for AI agents—but they make it dramatically easier for those agents to interact with product catalogs, pricing, inventory and commerce services.The conversation also goes deep into the enormous—and often overlooked—world of B2B commerce. Complex catalogs, negotiated pricing, ERP systems, EDI, thousands of product variations and highly customized business rules make B2B a very different challenge from consumer ecommerce. Bryan believes it may also provide some of the most practical early applications for agentic commerce, such as AI agents automatically managing routine replenishment and reorders.But Bryan pushes back on some of the industry's biggest hype. Fully autonomous AI shopping remains harder than it sounds. OpenAI and Perplexity's early checkout experiments demonstrate just how complicated the transaction layer can be. The problem may not be consumer trust as much as simply creating a good buying experience.

We also explore the emerging battle between commerce protocols, why Google may have an important structural advantage, the enormous economics surrounding payments, whether stablecoins can realistically challenge credit cards, and what happens to retail media if AI becomes a primary product-discovery channel.Finally, Bryan makes a provocative prediction about enterprise technology itself: implementing a commerce platform could eventually stop being a massive implementation project and become something closer to an onboarding task. AI could dramatically compress the time and cost required for frontend development, integrations, migrations and other implementation work—with major implications for the traditional systems-integration model.The future of commerce may not literally be "without checkouts." But the path between I need something and I bought it is about to look very different.This episode explores themes from our original discussion guide around composable architecture, intelligent commerce systems, AI-driven discovery and the changing role of traditional storefronts. Topics include: composable commerce, AI-powered product discovery, agentic commerce, B2B ecommerce, APIs and microservices, product data, LLM optimization, UCP and commerce protocols, payments, stablecoins, retail media, the future of ecommerce websites, and how AI could transform commerce implementation.#SignalAndNoise #DigitalCommerce #Ecommerce #AI #ArtificialIntelligence #AgenticAI #AgenticCommerce #ComposableCommerce #B2BCommerce #CommerceTechnology #MarTech #RetailMedia #DigitalTransformation #CustomerExperience #ProductDiscovery #EnterpriseAI #FutureOfCommerce

What happens when companies move so fast to adopt AI that they forget everything they already learned about building technology safely?

In this special edition of Signal & Noise Live at AI Con, Signal & Noise Executive Voice contributor Lucas Longacre, Head of Product at Inlightened, sits down with Ken Johnston, co-founder of the AI Governance Operations Foundation (AI GovOps), for a candid conversation about what it really takes to deploy and scale AI inside an organization.

Ken argues that the rush to embrace AI has created a massive case of enterprise FOMO. Companies feel enormous pressure to demonstrate that they're "doing AI," but in the process, many are abandoning fundamentals that took decades of software engineering to establish: observability, testing, CI/CD, security, rollback capabilities, cost controls, and disciplined product development.

Lucas and Ken dig into the rise of what Ken calls "demo theater" — where an impressive AI prototype can be created in hours and appear 90% finished, even though it may represent less than 10% of the work required to turn it into a secure, scalable production system.

They also explore:

• Why enterprises need to bring DevOps, DevSecOps and FinOps discipline into AI
• How AI can dramatically increase the "blast radius" of software failures
• Why observability may be one of the most important — and overlooked — components of enterprise AI
• The difference between an impressive AI demo and a production-ready product
• Why companies should build AI projects around learning loops, not just deployment
• How natural-language interfaces could finally replace dashboards with direct answers to business questions
• Why AI-generated code is creating entirely new challenges for software development and code review
• The opportunities — and dangers — created by vibe coding and the democratization of software development
• How Lucas is using AI inside product development while controlling security, token usage and access to enterprise data
• Why human experience, judgment and taste remain so important when working with increasingly capable AI systems

The conversation eventually moves beyond enterprise governance into something even bigger: What happens to expertise when AI allows people to skip years of learning?

Lucas and Ken discuss whether experienced professionals may actually have an early advantage with AI because they have decades of accumulated knowledge to recognize hallucinations, challenge outputs and know when something simply doesn't make sense — and what that could mean for the next generation entering the workforce.

Ken also previews his forthcoming book, The Lean AI Handbook, and explains why experimentation remains essential when the technology itself is evolving faster than almost anyone can keep up with.

It's a fascinating, funny and highly practical conversation about moving fast with AI — without forgetting everything we learned before AI arrived.

#SignalAndNoise #AICon #AI #ArtificialIntelligence #AIGovernance #AIGovOps #EnterpriseAI #GenerativeAI #AgenticAI #DevOps #DevSecOps #FinOps #LeanAI #VibeCoding #SoftwareDevelopment #AIEngineering #ProductManagement #AIOps #FutureOfWork #TechLeadership #AITransformation

For the past two years, the AI conversation has centered on one question:

Which model is best?

But what if we’ve been focused on the wrong competitive advantage?

In this episode of Signal & Noise, hosts Rio Longacre and Brett House sit down with Eddie Drake, Industry Principal, Marketing, Advertising & Experience at Snowflake, to explore why the future of enterprise AI won’t be determined by the smartest foundation models—but by the quality of an organization’s proprietary data, governance, and enterprise context.

As AI becomes embedded across every business function, companies are beginning to confront much bigger questions than prompt engineering or model selection.

Who owns the intelligence created by AI?

How do organizations protect decades of institutional knowledge from inadvertently training competitors’ systems?

What happens when the context that makes your business unique becomes your most valuable intellectual property?

Drawing on his recent research into AI governance, Eddie explains why enterprises need to rethink how they manage proprietary data, evaluate AI vendors, and architect their technology stacks for an agentic future.

The conversation explores the emergence of the Marketing Context Layer, why governance should be viewed as a competitive advantage rather than a compliance exercise, and how organizations can move faster with AI while maintaining trust, transparency, and control.

Rio and Brett also dive into the rapid evolution of the modern Data Cloud, the changing role of Customer Data Platforms, modular enterprise architectures, AI agents, token economics, and why many of today’s assumptions about enterprise software may soon be rewritten.

Whether you’re a CMO, CIO, Chief Data Officer, technology leader, or anyone trying to understand where enterprise AI is heading next, this episode offers a thoughtful framework for navigating one of the biggest technology shifts in decades.

Topics include:

• Why proprietary enterprise data—not foundation models—is becoming the real AI advantage
• The hidden intellectual property risks of generative AI
• Why AI governance is about strategy, not just compliance
• The rise of the Marketing Context Layer
• How brands should protect competitive intelligence in the AI era
• Why Data Clouds are becoming the operating system for enterprise AI
• The future of Customer Data Platforms and composable architectures
• AI agents, enterprise context, and the next generation of marketing technology
• Token economics, open-source models, and the future of enterprise AI infrastructure
• Why trust may become the single biggest differentiator in enterprise AI

If you enjoyed this conversation, subscribe to Signal & Noise for in-depth discussions with the leaders shaping the future of AI, marketing, advertising, and enterprise technology.

#SignalAndNoise #ArtificialIntelligence #EnterpriseAI #Snowflake #DataCloud #AIGovernance #MarketingAI #GenerativeAI #DataStrategy #EnterpriseData #MarTech #AdTech #AIAgents #DataGovernance #CustomerData #CDP #DataEngineering #DigitalTransformation #MarketingTechnology #BusinessAI

For years, app marketing revolved around one metric: installs.

But what if installs are actually the least important measure of success?

In this episode of Signal & Noise, Brett House and Rio Longacre sit down with Mick Rigby, Founder & CEO of Yodel Mobile, to explore how nearly two decades in mobile have reshaped the way brands should think about app growth.  

Mick founded Yodel Mobile in 2007—months before Apple launched the App Store—and has spent the last nineteen years helping brands including Gymshark, NBCUniversal, B&Q, and Kuda acquire, retain, and monetize app users. Having witnessed every major shift in the mobile ecosystem—from the birth of smartphones to privacy regulation and AI—he offers a unique perspective on where the industry is headed.

The conversation explores why downloads are only the beginning of the customer journey and why sustainable growth comes from activation, engagement, retention, and lifetime value—not simply acquiring more users.

Brett, Rio, and Mick discuss why marketing and product teams must work together, how poor onboarding drives churn, and why companies should spend less time optimizing acquisition and more time creating products customers genuinely want to use.

The episode also examines the impact of Apple’s App Tracking Transparency (ATT) changes, the growing importance of first-party data, attribution challenges, and why experimentation and customer insight have become more valuable than ever.

AI is another major theme. Mick explains how AI assistants are changing app discovery, why traditional App Store Optimization (ASO) is evolving, and how app marketers should prepare for a future where AI recommends apps based on user intent, credibility, and product quality—not just keywords.

The discussion also covers the Apple-Google duopoly, rising acquisition costs, predictive analytics, customer lifetime value, and why marketers need to spend less time asking what happened and more time understanding why it happened.

Topics include:

• Why installs are an overrated KPI
• Moving from acquisition to lifetime value
• Product and marketing alignment
• Customer onboarding and retention
• Privacy, ATT, and first-party data
• AI-driven discovery and the future of ASO
• Mobile measurement and attribution
• The Apple-Google duopoly
• Predictive analytics and churn reduction
• The future of smartphones and app growth

Whether you’re a mobile marketer, product leader, founder, or digital executive, this episode offers practical insights into how the app economy is evolving—and why the next generation of winning apps will be built around customer value, not just customer acquisition.

Subscribe to Signal & Noise for conversations with the innovators shaping the future of AI, marketing, media, and technology.

#SignalAndNoise #AppGrowth #MobileMarketing #AppMarketing #RetentionMarketing #ArtificialIntelligence #AIDiscovery #ProductManagement #GrowthMarketing #CustomerLifetimeValue #FirstPartyData #MarTech #DigitalMarketing

Marketing has spent decades building walls.

Brand versus performance. Advertising versus MarTech. Agencies versus in-house teams. IT versus marketing. Customer experience versus media.

What if those walls are finally coming down?

In this episode of Signal & Noise, Brett House and Rio Longacre sit down with Ana Mourão, a globally recognized marketing technology leader, author, and thought leader, to explore why the future of marketing belongs to leaders who can bridge strategy, technology, data, and AI.

Drawing on years of experience leading marketing transformations inside some of the world’s largest global brands, Ana explains why marketing is evolving from campaign execution into system design. Instead of simply launching promotions, tomorrow’s marketers will architect interconnected systems that continuously learn, adapt, and improve through experimentation, AI, and customer data. 

The conversation dives deep into one of the biggest shifts happening across the industry: the convergence of MarTech and AdTech. Historically, these functions have operated independently, often using different data, different technologies, and different success metrics. But as 1PD becomes more valuable and AI begins driving decisions, those boundaries are disappearing. 

Ana shares why marketers—not IT—must become the architects of modern marketing systems, while also learning to collaborate effectively with finance, legal, privacy, engineering, and tech teams. She argues tomorrow’s marketing leaders won’t simply create campaigns—they’ll translate across disciplines and become the connective tissue that enables organizations to move faster and make better decisions. 

The discussion also explores:

• Why first-party and zero-party data have become strategic business assets
• How customer data can dramatically improve paid media performance and budget efficiency
• Why experimentation—not perfection—is becoming marketing’s competitive advantage

• Why marketers need stronger financial and technical literacy to earn influence in the C-suite
• How AI is changing the skills marketers need to remain indispensable
• Why today’s MarTech stacks have become overly complex—and what happens next
• The evolution of Customer Data Platforms (CDPs), data clouds, and composable architectures
• Why context may become the most valuable ingredient in AI-driven marketing decisioning
• How governance, privacy, and trust must evolve alongside AI adoption 

Ana also introduces the framework from her book, Strategic Marketing Skills That Make You Indispensable in the AI Era, offering practical guidance for marketers who want to thrive in a world where AI handles more execution and humans increasingly create strategy, context, experimentation, and organizational alignment. 

This conversation is particularly valuable for marketing leaders, advertising professionals, media executives, consultants, data strategists, and anyone trying to understand where AI is taking modern marketing organizations.

Because the future won’t belong to marketers who know the most tools.

It will belong to marketers who know how to connect people, systems, data, and AI into one intelligent operating model.

Ana Mourão is a global marketing technology leader, author, speaker, and creator focused on helping marketers develop the strategic, technical, and organizational skills needed to succeed in the AI era. Her work centers on marketing systems, experimentation, first-party data strategy, AI, and the intersection of technology and business transformation. 

Signal & Noise is a podcast exploring the ideas, technologies, and leaders shaping the future of AI, marketing, advertising, media, and business.

#AI #ArtificialIntelligence #Marketing #MarTech #AdTech #FirstPartyData #CustomerData #MarketingTechnology #Advertising #DigitalMarketing #MarketingLeadership #DataStrategy #CustomerExperience #Experimentation #AgenticAI #CDP #DataCloud #MarketingTransformation #SignalAndNoisePodcast

About Ana MourãoAbout Signal & Noise

Advertising has never had more data—or less certainty.

Marketers today have access to billions of signals, AI-powered dashboards, clean rooms, attribution models, marketing mix models (MMM), and platform reporting. Yet despite this explosion of data, one fundamental question has become harder to answer:

What actually worked?

In this episode of Signal & Noise, Brett House and Rio Longacre sit down with Joanna Drews, Co-Founder and CEO of HyphaMetrics, for a thought-provoking conversation about the future of advertising measurement and why the industry may have been chasing the wrong kind of precision all along. The discussion explores the growing gap between deterministic attribution and the messy reality of modern consumer behavior, where audiences move fluidly across linear TV, connected TV (CTV), streaming, mobile, gaming, social media, and user-generated content. 

Joanna explains why simply collecting more data doesn’t necessarily produce better answers—and why representative panels, AI, and person-level exposure measurement may offer a stronger foundation than ever-growing datasets alone. She challenges long-held assumptions about attribution, incrementality, and causality while arguing that the industry’s biggest problem isn’t a lack of information—it’s a lack of trustworthy, independent measurement. 

The conversation also explores the industry’s shift toward hybrid measurement models as traditional TV ratings evolve into Big Data + Panel approaches, the rise of alternative measurement providers, and why the future may not belong to a single “currency” at all. Instead, marketers may increasingly rely on multiple trusted sources that work together to create a clearer picture of advertising effectiveness. 

Along the way, Brett, Rio, and Joanna tackle some of the industry’s biggest questions:

• Why traditional attribution models are breaking down• The resurgence of Marketing Mix Modeling (MMM) and what it still misses• How AI is changing media planning, optimization, and measurement• Why connected TV has created both unprecedented opportunity and unprecedented fragmentation• The future of cross-platform measurement across TV, streaming, gaming, and digital media• Why trust—not scale—may become the industry’s most valuable measurement asset• Whether marketers should stop chasing certainty and start embracing probabilistic decision-making

Joanna also shares why HyphaMetrics is not as another measurement currency, but as a foundational data layer designed to help publishers, agencies, platforms, and brands make smarter decisions in an increasingly automated advertising ecosystem. 

Whether you’re a CMO, media executive, data scientist, advertiser, agency leader, or simply fascinated by where AI is taking marketing, this episode offers an insightful look at one of the industry’s most important and least understood challenges.

Because in the age of autonomous marketing, better AI doesn’t start with better algorithms. It starts with better measurement.

About Joanna Drews

Joanna Drews is the Co-Founder and CEO of HyphaMetrics, a next-generation media measurement company focused on person-level, cross-platform audience measurement across linear television, streaming, connected TV, gaming, mobile devices, and digital media. She has spent her career helping shape the future of advertising measurement and is at the center of many of today’s most important conversations around media effectiveness and analytics. 

About Signal & Noise

Signal & Noise is a podcast exploring the ideas, technologies, and leaders transforming marketing, advertising, AI, media, and business. Hosted by Brett House and Rio Longacre, each episode features candid conversations with innovators shaping what’s next.

#Advertising #Marketing #MediaMeasurement #MarketingMeasurement #Attribution #MMM #MarketingMixModeling #CTV #Streaming #AI #ArtificialIntelligence #AdTech #MarTech #Media #Data #Analytics #HyphaMetrics #SignalAndNoise #Leadership #DigitalMarketing

Mobile advertising has quietly become the largest laboratory for innovation in digital marketing. From privacy changes and AI-powered creative to playable ads and performance optimization, many of the industry’s biggest breakthroughs have happened on mobile first.

Few people have had a closer view of that transformation than Alex Merutka.

In this episode of Signal & Noise, hosts Rio Longacre and Brett House sit down with Alex Merutka, Founder and CEO of CRAFTSMAN+, to explore the evolution of mobile advertising, the future of creative intelligence, and why the next competitive advantage in performance marketing won’t come from better targeting—but from better creative. 

Before founding CRAFTSMAN+, Alex spent more than half a decade helping build AppLovin from one of AdTech’s early startups into one of the industry’s most influential companies. As one of its earliest employees, he helped scale its performance advertising business, led programmatic initiatives during a period of explosive growth, and witnessed firsthand what it takes to build a category-defining tech company. 

Then came a decision that surprised many across the industry.

Leaving just prior to AppLovin’s successful IPO, Alex walked away from what has been widely reported as a nearly $10 million bonus to launch CRAFTSMAN+, betting that the future of advertising wouldn’t be defined by targeting algorithms alone—but by creative excellence powered by AI and technology. 

In this conversation, Alex shares lessons from hypergrowth, entrepreneurship, and building companies through periods of massive industry change.

Together they discuss:

• What it was really like helping build AppLovin from the inside
• The biggest misconceptions about AppLovin
• Why Alex chose to leave at the height of his career to become a founder
• The entrepreneurial risks behind launching CRAFTSMAN+
• Why creative—not targeting—is becoming the biggest performance lever in advertising
• How Apple’s App Tracking Transparency (ATT) permanently changed mobile advertising
• Why mobile continues to be the innovation engine for digital marketing
• How AI is transforming creative production and campaign development
• The rise of playable ads and interactive creative experiences
• What the world’s largest advertisers still get wrong about creative performance
• Founder-led marketing and why audiences increasingly trust people over brands
• Building in public through LinkedIn and thought leadership
• The future of app monetization and performance advertising

• Where Alex believes mobile advertising and creative technology are headed

As audience targeting becomes standardized and privacy regulations reshape the ecosystem, creative is emerging as the primary differentiator. Success will belong to organizations capable of producing, testing, learning, and iterating creative at unprecedented speed—and AI is accelerating that shift.

Alex also shares what he learned from one of AdTech’s most remarkable growth stories, the challenges of leaving a successful company to build something, and why founder conviction often matters more than timing.

Whether you’re a marketer, founder, product leader, creative strategist, or simply fascinated by how AI is reshaping digital advertising, this episode offers an inside look at where the industry is headed.

Alex Merutka is Founder and CEO of CRAFTSMAN+, a creative technology company helping brands build high-performing mobile advertising through AI-powered creative workflows, playable ads, and performance-driven creative production. Prior to founding CRAFTSMAN+, Alex spent half a decade at AppLovin, where he helped build one of the most successful businesses in advertising technology during the company’s rapid growth. 

Subscribe on YouTube, Spotify, and Apple Podcasts, or visit www.signalandnoise.ai for original articles, executive insights, and conversations at the intersection of technology, AI, and the future of business.

What happens when AI stops assisting marketers—and starts becoming the marketing team?

In this episode of Signal & Noise, Brett House and Rio Longacre sit down with Julius Körfgen, Co-Founder & CEO of Uplane, to explore one of the biggest questions facing the marketing industry: if AI can plan campaigns, generate creative, optimize budgets, launch ads, analyze performance, and continuously improve results, what role is left for human marketers?

Julius isn’t simply predicting the future—he’s building it.

After growing up in Germany, launching his first online business as a teenager, and leading growth at one of Europe’s fastest-growing climate technology companies, Julius relocated to Silicon Valley, joined Y Combinator, and founded Uplane. Within months, the company surpassed $1M ARR, raised a $4.5 million seed round, and began helping enterprise brands rethink how modern marketing should operate.

Rather than creating another AI point solution, Uplane is building an AI-native marketing platform that combines strategy, creative production, media buying, optimization, testing, and performance analysis into a continuous learning system. Instead of speeding up individual tasks, Uplane seeks to automate entire marketing workflows—compressing campaign cycles from months to hours while dramatically reducing wasted advertising spend.

During the conversation, Julius explains why he believes today’s marketing stack is fundamentally broken, why most AI tools fail because they automate isolated tasks instead of end-to-end workflows, and why both agencies and in-house marketing teams will need to reinvent themselves over the coming years.

• Why nearly half of digital advertising spend is still wasted
• Building an AI-native company from the ground up
• Why autonomous AI agents may replace much of today’s marketing operations
• The future of media buying, creative optimization, and campaign management
• How AI can improve brand governance and regulatory compliance
• Why point solutions aren’t enough—and what comes next
• How marketing organizations and agencies will evolve in an AI-first world
• Why storytelling, brand strategy, and human creativity become even more valuable as operations become autonomous

Whether you’re a CMO, agency executive, founder, marketer, or AI builder, this episode offers an inside look at how autonomous AI is beginning to reshape the entire marketing ecosystem.

Julius Körfgen is the Co-Founder & CEO of Uplane, an AI-native marketing technology company building autonomous systems for campaign planning, creative generation, media buying, optimization, and performance management. Before founding Uplane, Julius led growth initiatives at one of Europe’s fastest-growing climate technology companies before moving to the United States to participate in Y Combinator, where he launched Uplane and rapidly scaled the business.

Signal & Noise is a podcast exploring the technologies, companies, and ideas transforming marketing, advertising, AI, media, and the future of business. Hosted by Brett House and Rio Longacre, each episode features conversations with founders, executives, investors, and industry leaders building what’s next—and separating real innovation from the hype.

#SignalAndNoise #ArtificialIntelligence #AI #Marketing #MarketingAI #MarTech #AdTech #GenerativeAI #AgenticAI #MarketingAutomation #PerformanceMarketing #DigitalMarketing #MediaBuying #GrowthMarketing #Startup #YC #YCombinator #Founders #CMO #FutureOfMarketing #EnterpriseAI #BusinessTransformation #Automation #Innovation #TechPodcast

Artificial intelligence is changing how work gets done. But what if the real disruption isn’t AI itself?

What if the most valuable asset in the future economy isn’t code, content, or even data—but your judgment?

In this episode of Signal & Noise, Executive Voice Krish Raja sits down with Dan Pratl, CEO of Quadron, for one of the most philosophical and forward-looking conversations we’ve had on the show. Drawing on his unique background in securities regulation, open-source software, cryptocurrency, and AI infrastructure, Dan argues that we’re entering a new economic era where expertise itself becomes an asset that individuals can own, develop, verify, and monetize. 

The conversation begins by examining three major technology movements—financial regulation, open source, and crypto—and why each ultimately drifted away from its original mission. Dan explains how incentives, not technology, determine whether systems succeed over time, and why AI gives us a rare opportunity to redesign those incentive structures from the ground up. 

At the center of Quadron’s vision is a provocative idea:

Your expertise shouldn’t disappear every time you change jobs.

Instead, your accumulated judgment, decision-making patterns, and unique way of solving problems could become a persistent asset that grows more valuable throughout your career.

Krish and Dan explore what this means—not only for professionals, but for organizations, education, and the broader economy.

Together they discuss:

• Why expertise—not information—is becoming the world’s scarcest resource
• How AI is shifting value away from artifacts and toward human judgment
• Why today’s intellectual property systems may no longer fit the AI era
• The difference between generating content and capturing expertise
• Why friction—not automation—is often essential for developing real skill
• How AI should interview us rather than simply execute prompts
• Why “vibe coding” and prompt engineering still depend on human philosophy and judgment
• The future of personal AI systems that continuously learn how you think
• Why organizations struggle to retain institutional knowledge when employees leave
• The concept of expertise as a transferable, verifiable economic asset
• How future professionals may monetize judgment instead of hours worked
• Why verification may become more important than surveillance inside enterprises
• Token economics, programmable incentives, and what crypto got right—and wrong
• The future of consulting, knowledge work, and personal intellectual capital
• Why storytelling may become one of the most valuable business skills in the AI era
• Whether AI will replace knowledge workers—or simply amplify the best ones
• The future of frontier AI models, edge computing, and enterprise AI infrastructure
• Why curiosity, cross-disciplinary thinking, and diverse experiences may become the ultimate competitive advantage

One of the most compelling ideas throughout the discussion is that AI should not remove humans from the loop—it should help us better understand ourselves.

Rather than reducing work to faster outputs, Dan believes AI can help people document their thinking, preserve their expertise, tell their own story more effectively, and ultimately build careers around what makes them uniquely valuable.

It’s an ambitious vision—one that challenges many assumptions about employment, intellectual property, personal branding, and the economics of the AI era.

If you’ve been wondering what comes after today’s wave of copilots and chatbots, this conversation offers a fascinating glimpse into what knowledge work may look like.

About Dan Pratl

Dan Pratl is the CEO of Quadron, where he is building infrastructure designed to treat human expertise as an asset that can be captured, verified, and deployed across the AI economy. Prior to founding Quadron, Dan worked across securities regulation, open-source technology, cryptocurrency, and enterprise software, giving him a uniquely interdisciplinary perspective on the future of work.

What happens when artificial intelligence stops being just another productivity tool—and starts changing how we think about work, creativity, organizations, and even what it means to be human?

In this thought-provoking episode of Signal & Noise, hosts Rio Longacre and Brett House sit down with entrepreneur and AI infrastructure founder Kyle Csik, Founder & CEO of Adaly AI, for a wide-ranging conversation that stretches from enterprise AI architecture to philosophy, economics, education, and the future of civilization. 

Kyle’s career has always sat at the intersection of technology, entrepreneurship, and systems thinking. After founding his first company at just 18 years old, he went on to build a career across programmatic advertising, retail media, and enterprise technology before launching Adaly AI, a company rethinking how organizations connect and reason across their data without relying on traditional data warehouses.

But this conversation goes far beyond software.

Inspired by Rio’s essay Machines of Loving Grace?—itself influenced by Anthropic CEO Dario Amodei’s optimistic vision for AI and E.M. Forster’s prophetic 1909 short story The Machine Stops—the discussion explores one of the biggest questions facing society:

Is artificial intelligence simply another technology cycle… or is it fundamentally changing what it means to be human?

Kyle argues that today’s large language models are incredibly powerful prediction engines—but they are not conscious, autonomous beings. Instead, their greatest value comes from augmenting human judgment, eliminating repetitive work, and freeing people to focus on creativity, relationships, and higher-order thinking.

Along the way, the conversation dives into:

• Why today’s AI models are extraordinary prediction systems—but not artificial general intelligence
• Whether humans are simply sophisticated prediction machines ourselves
• How every major technology—from writing to the internet—changed the skills humans needed to survive
• Why AI should augment human judgment rather than replace it
• The surprising productivity gains Kyle has achieved by building personal AI agents into his everyday life
• Why freeing people from administrative work could lead to stronger families, better education, and more creativity
• The future of enterprise AI and why today’s data infrastructure is holding organizations back
• Why Kyle believes traditional data warehouses represent an outdated computing model dating back to the 1960s
• How federated AI architectures could fundamentally change how businesses access knowledge
• Why context—not simply bigger models—is becoming AI’s greatest competitive advantage
• How AI could flatten organizational hierarchies while making companies dramatically more customer-centric
• The evolution of SaaS, enterprise software, and agentic workflows
• Why CIOs remain frustrated despite massive enterprise AI investments
• The growing disconnect between AI hype and practical implementation inside large organizations
• The risks of token-based AI pricing and enterprise vendor lock-in
• Open-source AI versus frontier models—and why many Fortune 500 companies are choosing differently than expected
• AI’s impact on education, scientific discovery, and innovation
• Data centers, energy infrastructure, nuclear power, and the coming compute economy
• Space-based computing, Dyson spheres, and what it might take for civilization to become truly AI-powered
• The balance between government regulation, private innovation, and maintaining competitive markets
• Why the biggest challenge may not be artificial intelligence—but ensuring humans continue to develop wisdom, curiosity, and judgment
• 
  

It’s an expansive conversation that blends practical enterprise strategy with philosophy, economics, and the long-term future of technology—exactly the kind of discussion that sits at the heart of Signal & Noise.

Connect with Kyle Csik

• LinkedIn: https://www.linkedin.com/in/kylecsik/
• Adaly AI: https://adaly.ai/

Gaming has grown far beyond entertainment. It is now one of the world’s largest industries—and one of technology’s most influential laboratories for understanding engagement, retention, monetization, community, and consumer behavior.

In this episode of Signal & Noise, Brett House and Rio Longacre sit down with Phylicia Koh, General Partner at Play Ventures, a global venture capital firm investing in gaming, consumer applications, and the technologies powering both.

Phylicia joined Play Ventures as its first employee after nearly a decade working across marketing, growth, product, and startup operations. Today, she invests in game studios, technology companies, and a growing category that Play Ventures calls “playable apps”: consumer applications that incorporate the operating principles and engagement models perfected by the gaming industry.

The central idea behind the conversation is simple: gaming is no longer becoming mainstream. It already is mainstream. Instead, everything else is becoming more like gaming.

From Duolingo and Robinhood to Discord, fitness platforms, productivity tools, and short-form microdramas, consumer apps increasingly rely on mechanics developed or refined by game companies. Streaks, rewards, progression systems, virtual goods, social communities, live events, and freemium monetization have become foundational elements of the modern digital experience.

The conversation also explores how Apple’s App Tracking Transparency framework forced mobile publishers to rethink their approach to growth. As deterministic user-level measurement became more difficult, leading publishers dramatically increased creative production and testing—sometimes producing thousands of advertising assets for a single title each month. Generative AI is now making that speed, volume, and personalization possible across a much broader range of industries.

The discussion then turns to agentic advertising and what the emerging ecosystem may still be missing. While much of the industry is focused on buyer agents and seller agents, Phylicia argues that the greatest opportunity could be a third participant: an agent representing the consumer.

A true consumer agent could understand an individual’s preferences, manage permissions, determine when advertising is welcome, filter irrelevant brands, transact on the consumer’s behalf, and protect that person’s interests across digital environments. In Phylicia’s view, the company that successfully builds a trusted and widely accessible consumer-agent layer could become one of the next trillion-dollar businesses.

Along the way, they discuss:

• Why gaming remains misunderstood despite its enormous economic and cultural influence
• Phylicia’s path from growth marketing to becoming Play Ventures’ first employee and a General Partner
• What separates a genuinely playable application from superficial gamification
• How progression, social interaction, virtual economies, and live operations drive retention
• Why microdramas are a powerful example of gaming principles entering entertainment
• How game companies measure cohorts, monetization, retention, and return on ad spend
• Why leading publishers produce thousands of new advertising creatives every month
• How AI is reshaping creative production, localization, personalization, and live operations
• What buyer, seller, and consumer agents could mean for the future of advertising
• Why women’s health remains one of the world’s largest underserved investment opportunities

This is a wide-ranging conversation about gaming, venture capital, consumer behavior, advertising, and AI. More than anything, it makes the case that if you want to understand where consumer technology is heading, you should pay much closer attention to what the gaming industry has already built.

Learn more about Phylicia Koh:
https://www.linkedin.com/in/phyliciakoh/

Learn more about Play Ventures:
https://www.play.vc/

Visit Signal & Noise:
https://www.signalandnoise.ai/

What if the best way to future-proof your career isn't learning another AI tool—but learning to think like a hacker?

In this episode of Signal & Noise, Executive Voice Krish Raja sits down with Alanna Laforet—technology executive, former Chief Revenue Officer, startup operator, blockchain pioneer, and advisor—to explore what it really takes to reinvent yourself in an era where AI is transforming every industry.

Alanna's career has never followed a traditional path. From starting as a Unix systems administrator and QA engineer at DoubleClick to helping shape advertising standards at the IAB Tech Lab, leading blockchain ventures, launching crypto media companies, advising AI startups, and building a portfolio career across multiple industries, she's consistently embraced curiosity over comfort.

Together, Krish and Alanna unpack why the people who thrive in the AI era won't necessarily be those with the best technical skills—but those willing to continually learn, experiment, and reinvent themselves.

They discuss:

• Why adopting a "hacker mentality" creates career resilience
• How curiosity—not job titles—has guided Alanna's professional journey
• The hidden lessons learned from startup failures, crypto winters, and industry disruption
• Why AI makes understanding technology more important, not less
• How to overcome fear and imposter syndrome when changing careers
• Why treating yourself as your own company changes how you think about work
• The rise of portfolio careers, fractional leadership, and multiple revenue streams
• Building a personal thesis that guides career decisions
• Why networking, community, and simply "leaving your house" may be the most underrated career advice today
• Practical advice for professionals navigating layoffs, AI disruption, and career transitions

This isn't just a conversation about AI.

It's a conversation about ownership, adaptability, and designing a career that's resilient no matter how technology changes.

If you're wondering how to stay relevant in the age of AI—or considering your own next chapter—this episode offers a thoughtful roadmap from someone who's reinvented herself time and time again. 

Guest: Alanna Laforet
Technology Executive | Startup Advisor | Fractional Executive | Blockchain & AI Strategist

Hosted by: Krish Raja
Executive Voice, Signal & Noise

Subscribe to Signal & Noise for conversations with the executives, founders, researchers, and innovators shaping the future of AI, advertising, marketing, media, and technology.

#AI #Careers #Leadership #FutureOfWork #CareerGrowth #ArtificialIntelligence #AdTech #MarTech #FractionalLeadership #Entrepreneurship #Blockchain #Innovation #SignalAndNoise #KrishRaja #AlannaLaforet

Artificial intelligence is transforming every industry—but what happens when it transforms an entire city?

In this episode of Signal & Noise, hosts Rio Longacre and Brett House sit down with Suma Nallapati, Chief AI & Information Officer for the City and County of Denver, to explore one of the most important—and often overlooked—applications of AI: improving the lives of citizens.

Rio and Suma first met during Mayor Mike Johnston's transition team, and this conversation brings together years of shared interest in technology, public service, and civic innovation. Rather than focusing on AI's impact on marketing or enterprise productivity, this discussion asks a bigger question:

How can AI make society better?

Suma shares how Denver has become one of America's leading AI-enabled cities, why she left the private sector to return to public service, and how city governments can embrace AI responsibly while maintaining public trust.

From AI-powered citizen services and smart cities to responsible governance and the future of work, this is a thoughtful conversation about technology's role in creating stronger communities—not just more efficient businesses. 

• Why Suma chose to leave executive leadership in the private sector to serve the people of Denver
• How Denver became one of the first cities in the U.S. to appoint a Chief AI Officer
• The story behind Sunny, Denver's multilingual AI assistant that helps residents access city services 24/7
• How AI is improving government operations while keeping humans firmly in the loop
• Why "transformational work should belong to humans, transactional work to bots"
• The importance of responsible AI, governance, and cross-functional oversight
• How Denver balances innovation with public trust and citizen privacy
• What "smart cities" really mean—and why they're about people, not technology
• Lessons government leaders can learn from the private sector—and vice versa
• Why AI adoption is becoming more about organizational change than technology itself
• How cities can prepare for an AI-native workforce
• The evolving debate around AI regulation, data centers, and public policy
• Why curiosity—not fear—is the most important leadership trait in the AI era
• Suma's vision for how AI can help create a more compassionate and resilient society 

Whether you're a CIO, public sector leader, technologist, policymaker, entrepreneur, or simply curious about how AI will shape everyday life, this episode offers a refreshing perspective on technology's highest purpose: serving people.

Guest: Suma Nallapati
Chief AI & Information Officer
City and County of Denver

If you enjoyed this episode, be sure to subscribe to Signal & Noise on YouTube, Spotify, Apple Podcasts, or wherever you get your podcasts.

#ArtificialIntelligence #AI #PublicSector #Government #SmartCities #DigitalTransformation #ResponsibleAI #Innovation #Leadership #CIO #ChiefAIOfficer #Denver #Technology #FutureOfWork #SignalAndNoise

Every AI breakthrough begins with something far less glamorous: clean, trustworthy data.

In this episode of Signal & Noise, Brett House and Rio Longacre sit down with Jer Tippets, Director of Digital Tagging & Implementation at Hyatt Hotels, to explore the invisible infrastructure that powers modern digital marketing. While everyone is talking about generative AI, personalization, and autonomous agents, Jer argues that none of it works without a solid data foundation—and after spending more than 15 years building customer data systems for global brands, he's uniquely qualified to explain why. 

Rio first met Jer when they were both speakers at Tealium's Digital Velocity Conference, where Hyatt shared its customer data strategy and philosophy of treating customers like humans. Hyatt has long been a Tealium customer, and Tealium—one of Signal & Noise's valued sponsors—has played an important role in helping leading enterprises modernize their customer data infrastructure. That shared connection made this conversation a natural fit and one we've wanted to have ever since.

Together, they dive into why poor data quality quietly kills AI initiatives, why organizations continue to collect mountains of unusable information, and why "collect everything" has become one of the biggest mistakes in modern marketing. Jer explains why first-party data is now one of the most valuable strategic assets a company can own—and why governance, identity resolution, and privacy have become board-level business issues rather than back-office technical concerns. 

The conversation also pulls back the curtain on Hyatt's approach to digital experience, exploring how one of the world's largest hospitality brands balances personalization with consumer trust across millions of guest interactions. From persistent customer identities and consent management to CDPs, synthetic audiences, and the promise—and reality—of AI-driven personalization, Jer offers a refreshingly practical perspective shaped by years of real-world implementation. Rather than chasing hype, he focuses on what actually delivers better customer experiences. 

Jer also shares why AI should be viewed as an amplifier—not a replacement—for human expertise, why data governance is becoming one of the most important competitive advantages organizations can build, and what the next generation of digital measurement will look like as AI agents increasingly become both the producers and consumers of marketing data.

If you've ever wondered why some brands consistently deliver seamless, personalized experiences while others struggle with fragmented customer journeys, this episode explains what happens behind the scenes—and why the future of AI may ultimately depend less on better models and more on better data.

• Why AI is only as good as the data beneath it
• How Hyatt approaches customer identity and digital experience
• Why first-party data has become a strategic business asset
• The hidden costs of poor data quality
• Privacy, consent management, and earning customer trust
• Why "collect everything" is the wrong data strategy
• The realities of CDPs, synthetic audiences, and identity resolution
• How AI is changing digital implementation and analytics
• Why governance is becoming a competitive advantage
• What marketers should do today to prepare for an AI-first future

Whether you're a CMO, MarTech leader, data engineer, product owner, analytics professional, or simply fascinated by the future of AI and customer experience, this conversation offers an inside look at the systems powering every click, every recommendation, and every personalized interaction.

Guest:
Jer Tippets
Director of Digital Tagging & Implementation
Hyatt Hotels

For nearly two decades, digital advertising has optimized around one primary objective: finding the cheapest possible way to reach the right person. Programmatic buying, identity signals, attribution, and performance marketing have created extraordinary efficiency—but they may also have trained the industry to systematically undervalue one of the most important variables in advertising effectiveness: the quality of the media itself.

In this episode of Signal & Noise, Rio Longacre and Brett House welcome back friend of the podcast Erez Levin for his third appearance and his second full-length conversation. Erez is one of the industry's most respected voices on media quality, attention, and advertising effectiveness. A former Google executive and now Founder of Emet Advisory Advisory, he recently co-authored CIMM's landmark report, Quality Matters: Navigating Quality in Media Buying and Measurement, an effort to establish a common industry framework for evaluating media quality.

The conversation explores a simple but powerful premise:

Not all impressions are created equal.

While the industry has become exceptionally good at optimizing for identity, CPMs, and short-term outcomes, it has largely ignored the environments where advertising actually appears. As deterministic identity signals become less reliable and privacy regulations reshape digital advertising, Erez argues that marketers must begin treating media quality as a first-class buying signal—not merely a brand safety filter.

Topics include:

• Why optimizing exclusively for ROAS and CPMs can quietly destroy long-term brand growth
• How the industry's obsession with attribution created a "race to the bottom" in media buying
• Why attention alone is an incomplete measurement framework
• The difference between audience quality, media quality, and creative quality—and why all three matter
• Why premium inventory isn't binary, but exists on a spectrum of value
• The hidden problems with averaging performance across wildly different media environments
• Why CTV has an opportunity to avoid repeating the mistakes of display advertising
• How marketers should rethink media planning as identity signals continue to weaken
• Why procurement incentives often work against advertising effectiveness
• Practical ways brands can begin incorporating media quality into buying decisions today
• The future of AI-driven media buying and whether automation will improve—or worsen—advertising quality
• Why marketers—not platforms or agencies—must ultimately lead this shift

The discussion also expands into broader industry questions, including the future of the open web, the role of private marketplaces, the economics of connected television, media governance, AI-generated advertising, and whether today's measurement systems are rewarding the wrong behaviors.

Throughout the conversation, Erez makes the case that quality is not a "nice to have." It is a pricing signal. One that may become increasingly important as AI, automation, and privacy reshape the advertising ecosystem.

If you've ever wondered why digital advertising often feels more optimized than effective—or why brands continue chasing cheaper impressions while premium publishers struggle to monetize quality—this conversation explains why.

Whether you're a CMO, agency executive, media planner, AdTech leader, publisher, or simply interested in where digital advertising is headed next, this is an essential discussion about one of the industry's most important strategic shifts.

Guest: Erez Levin, Founder, Emmett Advisory; former Google executive; co-author of CIMM's Quality Matters report.

Connect with Erez: https://www.linkedin.com/in/erezlevin/

This week, the future of digital advertising arrived.

Following Boostr and Vox Media’s landmark announcement of one of the industry’s first public agentic media buys using AdCP, Signal & Noise sits down for an exclusive conversation with Patrick O’Leary, Founder & CEO of Boostr, to unpack exactly what happened—and why it matters.

For months, we’ve argued that agentic trading wasn’t a distant vision. It was already beginning. This announcement is one of the clearest signals yet that AI agents are moving beyond copilots and into autonomous negotiation, with buyer and seller agents working directly together to execute media transactions.

This isn’t another AI hype discussion.

Patrick walks us through the transaction in detail: how the agents negotiated, what humans still controlled, how long the process took, what the pilot revealed, and why this could fundamentally reshape the economics of digital advertising.

We also tackle the bigger questions:

• Does agentic trading replace programmatic or evolve alongside it?
• What happens to DSPs, SSPs, and today’s ad tech ecosystem?
• How do identity, fraud prevention, brand safety, and measurement work in an agent-driven world?
• Which companies stand to benefit—and which business models are suddenly at risk?

At Signal & Noise, we don’t do hot takes. We bring together the people who are actually building the future and give them the time to explain what they’re doing, why they’re doing it, and what it means for the rest of the industry.

If you’ve been wondering whether agentic trading is real, this is the conversation you’ve been waiting for.

Guest: Patrick O’Leary, Founder & CEO, Boostr

If you enjoy conversations that go beyond the headlines, subscribe to Signal & Noise on YouTube, Spotify, Apple Podcasts, or visit SignalAndNoise.ai for more executive interviews, analysis, and original research.

Attribution was supposed to get easier. Instead, marketers today operate in a world of disappearing identifiers, privacy restrictions, walled gardens that increasingly measure themselves, and consumer journeys that unfold inside environments few advertisers can fully observe.

So how much confidence should we still have in the numbers? In this episode of Signal & Noise, Brett House and Rio Longacre sit down with Brian Quinn, President & GM of AppsFlyer, one of the most influential—but often least understood—companies in digital advertising.

For more than a decade, AppsFlyer has been at the center of mobile measurement, helping brands understand where users come from, which campaigns actually drive installs and engagement, and how to measure effectiveness in an ecosystem transformed by Apple’s ATT framework, signal loss, and evolving privacy regulations.

Together, we unpack one of the industry’s most important questions: Can attribution still be trusted?

The conversation explores why mobile remains the dominant yet underappreciated consumer environment, whether multi-touch attribution still has a future, and why marketers are increasingly turning toward incrementality, clean rooms, and econometric approaches to understand what really drives business outcomes.

We also discuss:

• Why AppsFlyer became much more than an attribution company• Whether the walled gardens are effectively grading their own homework• The lasting impact of Apple’s App Tracking Transparency changes• Why fraud remains a massive hidden tax on digital advertising• The growing role of clean rooms and independent measurement• Whether privacy has made advertising measurement better—or simply more uncertain• How AI may reshape attribution and optimization over the next five years• What a modern measurement strategy should look like in a world where signal loss may be permanent

Apps account for nearly 90% of mobile time, consumers spend hours each day inside apps, and marketers collectively spend more than $150 billion annually on mobile advertising. Yet many advertisers still struggle to explain how mobile attribution actually works—or what replaces it when deterministic signals disappear.

Brian offers a candid and practical perspective on where measurement is heading next, what marketers misunderstand about attribution, and what parts of today’s measurement stack may disappear entirely.

If you’ve ever wondered whether the industry’s most trusted metrics deserve your trust—or what comes after attribution—this episode is for you.

#SignalAndNoise #AppsFlyer #Attribution #Measurement #Privacy #AdTech #MarTech #MobileMarketing #CleanRooms #MTA #Incrementality #DigitalAdvertising

Colorado has become ground zero in one of the most important debates shaping the future of technology: how do we regulate AI without slowing innovation?

In this episode of Signal & Noise, Brett House and Rio Longacre sit down with Srinivas "Chinnu" Parinandi, Associate Professor of Political Science at the University of Colorado Boulder, to unpack the growing clash between government oversight, technological progress, and economic competitiveness.

The conversation begins with Colorado's controversial AI legislation and expands into a broader discussion about privacy regulation, federalism, interstate commerce, and the unintended consequences of state-by-state technology governance. Chinnu explains why populism on both the political left and right is driving increased scrutiny of technology companies, how fragmented regulation affects startups versus large incumbents, and why policymakers often struggle to keep pace with rapidly evolving technologies. 

The discussion also explores the influence of GDPR and other privacy frameworks, whether regulatory uncertainty is becoming a barrier to entrepreneurship, and how businesses can engage more constructively with policymakers rather than simply opposing regulation. Chinnu offers a nuanced perspective on whether America needs a national AI framework and why the answer may be more complicated than many in the tech industry assume. 

In the second half of the episode, the conversation turns to one of the most overlooked aspects of the AI boom: data centers. As AI workloads drive unprecedented demand for computing power, local opposition to data center projects is growing across the country. Chinnu explains the politics behind energy consumption, power generation, utility regulation, zoning battles, and the increasing tension between infrastructure development and community concerns. 

The episode closes with a discussion of whether America is entering an "anti-tech era," what role government should play in shaping emerging technologies, and whether the United States can strike the right balance between consumer protection and innovation in the age of AI. 

• Colorado's AI laws and privacy regulations
• SB26-134 and SB26-189
• GDPR, CCPA, and state-level privacy frameworks
• AI regulation and interstate commerce
• Federal vs. state governance
• Populism and technology policy
• Startup innovation and regulatory burden
• Data centers, energy, and infrastructure
• NIMBYism and local opposition movements
• America's AI competitiveness versus China
• The future of AI governance

Srinivas Parinandi is an Associate Professor of Political Science at the University of Colorado Boulder whose research focuses on political economy, American political institutions, public policy, technology governance, and the interaction between government regulation and business innovation. His work examines how political incentives and institutional structures shape economic outcomes and emerging technology markets.

#AI #ArtificialIntelligence #Privacy #GDPR #CCPA #Colorado #DataCenters #TechnologyPolicy #Regulation #PoliticalEconomy #SignalAndNoise #AdTech #Innovation #TechPolicy #AIRegulation

What happens when you stop trying to sound "professional" and start sounding like yourself?

In this Signal & Noise conversation, Executive Voice Krish Raja hosts his first solo episode with Justin Kramm, Founder & Creative Director of Shit Show Creative. The result is one of our funniest—and most surprisingly insightful—conversations to date.

Justin has spent more than two decades shaping creative work for brands including Nike, Red Bull, NVIDIA, Xbox, Uber, ESPN, and many others. More recently, he's built an enormous following on LinkedIn by doing something refreshingly different: embracing absurdity, authenticity, and humor to build a genuine community.

Together, Krish and Justin explore:

• Why authenticity attracts the right people—and the right opportunities
• How humor cuts through noise, tribalism, and corporate clichés
• Building a creative business that started as a joke and became something real
• What Cannes Lions reveals about creators, AI, and the future of marketing
• Why LinkedIn may be becoming the internet's most unexpectedly entertaining platform
• How AI should amplify creativity rather than replace it
• Why finding your authentic voice is ultimately how you find your tribe

Recorded while Krish was attending Cannes Lions and Justin joined remotely from Florida in the middle of the night, this episode blends sharp observations about the advertising industry with an honest conversation about creativity, community, and having the courage to be yourself.

It's funny. It's thoughtful. And it's a reminder that sometimes the best professional strategy is simply being authentically human.

Day 4 marks the final chapter of Signal & Noise’s first Cannes Lions together as a team—and what a way to close out an unforgettable week.

Across four days at Hearst House, we recorded conversations with some of the brightest minds in advertising, media, identity, AI, cybersecurity, consulting, and marketing transformation. Cannes 2026 felt bigger, busier, and more energized than ever, with AI moving from theory to implementation and nearly every discussion centered on what happens when intelligent systems begin participating directly in media, commerce, and customer experiences.

In this Day 4 compilation, Rio Longacre, Brett House, and Krish Raja sit down with an exceptional group of founders, CEOs, technologists, and industry operators to unpack the trends shaping the next decade of marketing.

Featured guests include:

• Michael Beebe, CEO of Dstillery, joined by David Bell, for a candid discussion on transparency, principal media, agency business models, ad tech taxes, and whether AI will finally expose which vendors are truly creating value.

• Guy Tytunovich, Founder & CEO of CHEQ, on fighting ad fraud in an era of AI agents, malicious bots, and non-human traffic—and why marketers may need entirely new approaches to identity and trust online.

• Mansoor Basha, CTO of Stagwell Marketing Cloud, on composable marketing systems, data infrastructure, and the evolving role of AI inside enterprise decision-making.

• Alina Vandenberghe, Co-Founder & Co-CEO of Chili Piper, discussing growth, customer experience, and how AI is reshaping B2B engagement.

• Justin Bell, Global CEO of Credera, sharing his perspective on transformation, enterprise AI adoption, and why consulting firms are increasingly helping clients rethink operating models—not just technology stacks.

• Rob McLaughlin returns to Signal & Noise for a wide-ranging conversation touching on curation, supply-side innovation, containerization, publisher monetization, and why Cannes remains the industry’s most valuable gathering.

And to close out the week, Lou Paskalis delivers one of our favorite observations from Cannes:

“The only thing worse than having to come to Cannes is not getting to go to Cannes.”

From fraud detection and AI agents to transparency, identity, curation, and the future of marketing as an enterprise operating system, Day 4 captures the optimism, curiosity, and collaborative spirit that made Cannes Lions 2026 one of the most exciting events we’ve attended.

Thank you to everyone who joined us at Hearst House, stopped by the studio, shared ideas, challenged assumptions, and helped make this such a memorable week.

This is the final Cannes Lions 2026 compilation episode from Signal & Noise—but the conversations are only getting started.

🎧 Available now on Apple Podcasts and Spotify.

📺 Individual interviews from Cannes Lions 2026 will continue to roll out on YouTube in the coming weeks.

#CannesLions #AI #AdTech #MarTech #Identity #Cybersecurity #AgenticAI #SignalAndNoise #HearstHouse #Advertising #MarketingInnovation

Day 3 at Cannes Lions 2026 may have been the hottest day in French history, but it also delivered some of the most thoughtful conversations of the week.

Recorded live from Hearst House in Cannes, this Signal & Noise compilation brings together founders, CMOs, product leaders, publishers, identity experts, and healthcare innovators to discuss what’s actually changing in advertising, media, data, and AI.

A recurring theme throughout the day was that we’re entering a new phase of the AI era. The prototypes are everywhere, but deployment is hard. Brands are still wrestling with fragmented data, trust, context, measurement, and figuring out how agentic systems fit into real business workflows. At the same time, publishers are rediscovering the value of premium relationships, healthcare marketers are embracing AI-driven disruption, and identity is becoming strategically important again.

🎙 Sarah Robertson – Chief Product Officer, Experian
Trusted data, identity as infrastructure, MCPs, consumer agents, and why product teams are being pushed to build capabilities the market isn’t quite ready to consume.

🎙 Christian Monberg – CTO, Zeta Global
Athena, agentic marketing, the new partnership with Palantir, and why brands still haven’t solved their data foundations despite years of investment in martech stacks.

🎙 David Sandström – CMO, Klarna
How Klarna transformed itself from a B2B payments provider into a consumer brand, designing financial experiences for real people instead of “tech bros,” and why shopping remains an emotional experience that agents won’t fully replace.

🎙 Jim Weiss – Founder & Chairman, Real Chemistry
The future of healthcare marketing, precision medicine, longevity, physician influence, and why healthcare may be the industry most ripe for AI disruption.

🎙 Matt Krepsik – CEO, MediaRadar
MMM, metadata, trust, context, speed, creative effectiveness, and how AI may finally make marketing measurement actionable in near real time.

🎙 Mathieu Roche – Founder & CEO, ID5
Identity as advertising infrastructure, the implications of the Publicis acquisition of LiveRamp, and what happens when consumer agents begin acting as extensions of ourselves online.

🎙 Melissa Gordon-Ring – Global President, Omnicom Media Group Health
Healthcare’s emergence at Cannes, AI’s impact on clinical development and commercialization, and why pharma marketers may ultimately benefit most from agentic technologies.

🎙 Lisa Ryan Howard – Global CRO, Hearst Magazines
Publishers are back. Hearst discusses premium audiences, first-party data products, AI-powered sales tools, direct relationships, and why quality content is becoming more valuable in an era of infinite AI-generated noise.

This was our third day together at Cannes Lions 2026, and the themes were impossible to ignore:

• AI is exposing weaknesses in existing technology stacks.
• Trusted data matters more than ever.
• Identity isn’t dead—it’s evolving.
• Publishers may finally have leverage again.
• Healthcare is becoming one of the most exciting categories in marketing.
• Creativity still outperforms algorithms when it comes to moving people.

More Cannes conversations are coming soon, including our Day 4 recap and additional individual interviews from Hearst House.

Subscribe to Signal & Noise on Apple Podcasts and Spotify, and watch individual sessions as they are released here on YouTube.

With Rio Longacre, Krish Raja & Brett House

Signal & Noise is on the ground at Cannes-Lions 2026.

This year marks the first time Rio Longacre, Krish Raja, and Brett House have attended Cannes together as a team under the Signal & Noise banner. While each of us has been to Cannes in previous years, experiencing it together—and recording conversations live from Hearst House—made this year’s festival something special.

And what a year to do it.

Cannes Lions 2026 felt bigger, busier, and more energized than ever. The familiar faces from advertising, media, and publishing were all here, but what stood out most was the influx of AI founders, product leaders, engineers, and technologists who are increasingly shaping the future of our industry.

This special compilation brings together our Day 1 opening conversation along with every interview recorded during Days 1 and 2 at Hearst House.

Across these sessions, we explored some of the biggest themes emerging from Cannes:

• Agentic media buying and autonomous analytics
• The transformation of agencies into AI-native operating models
• Self-service television and the future of premium video advertising
• AI-powered measurement, causal modeling, and MMM
• The reinvention of creative workflows
• Healthcare marketing, discoverability, and the changing physician experience
• The role of creators, influencers, and culture in modern brand building

🎙 John Hoctor — CEO & Co-Founder, Newton Research; Founder of Data Plus Math

John discusses analytics agents, causal modeling, MMM, and how Newton is helping agencies such as Horizon Media and Dentsu build toward an agentic future for planning, measurement, and buying.

🎙 Robyn Freye — President, Court Avenue

Robyn shares her perspective on enterprise AI transformation, organizational redesign, and what it means to build an AI-native agency designed for the intelligence economy.

🎙 Daniel Druger — VP Product, Comcast Advertising / Universal Ads

Daniel walks through Universal Ads, Comcast’s effort to bring self-service buying to premium television, making TV advertising accessible to millions of businesses that historically couldn’t participate.

🎙 Brian Vaughan — Executive Creative Director, Shadow

Brian explores how creativity is evolving in the age of AI, why culture-first campaigns still matter, and how PR, creators, celebrity partnerships, and social storytelling can work together to create moments that break through the noise.

🎙 Andrea Palmer — CEO, PHM (Publicis Health Media)

Andrea discusses healthcare marketing’s evolution from compliance-driven communications to discoverability, influence, and helping physicians and patients navigate an increasingly complex information ecosystem.

This compilation episode is available in full on Apple Podcasts and Spotify, while each individual conversation will also be released separately here on YouTube.

And we’re only halfway through the week.

Days 3 and 4 are coming soon, featuring even more founders, product leaders, agency executives, publishers, and technologists helping define what comes next for media, marketing, AI, and advertising.

Stay tuned.

🎧 Full compilation available on Apple Podcasts and Spotify
📺 Individual interviews dropping soon on YouTube

Featured Guests

📰 More Cannes coverage, articles, and episodes at SignalAndNoise.ai

For weeks, reports that Publicis was exploring a relationship with Hightouch following its acquisition of LiveRamp circulated as an industry scoop. Until now, no one involved had publicly confirmed it.

In this special Signal & Noise Signal Break, we sit down with Hightouch co-founder and CEO Tejas Manohar, who confirms that Hightouch has in fact been in discussions with Publicis.

That confirmation adds an important new dimension to one of the biggest stories in adtech this year. What would a partnership between Publicis, LiveRamp, and Hightouch mean for identity, activation, clean rooms, and the future architecture of customer data?

Rio Longacre, Brett House, and Tejas unpack the implications in a fast-moving, high-energy conversation between three practitioners deeply involved in modern marketing infrastructure. We discuss composable CDPs, warehouse-native activation, agentic workflows, and why the industry's center of gravity may be shifting yet again.

To be clear, Hightouch is a sponsor of Signal & Noise, but sponsorship played no role in our interest in covering this story or inviting Tejas to join us. This discussion was driven entirely by the significance of the news and our belief that it deserves broader industry attention.

This isn't the long-form Tejas episode we originally planned. That's still coming. Consider this an early dispatch—a Signal Break—to share an important confirmation with the market while it's still unfolding.

Topics discussed:
• Tejas confirms Hightouch has spoken with Publicis
• What a Publicis–LiveRamp–Hightouch relationship could look like
• Why composable architectures continue gaining momentum
• How identity, activation, and measurement may evolve post-acquisition
• The growing role of AI and agentic systems in marketing operations

If you're trying to understand where the future marketing operating system is headed, this is one conversation you won't want to miss.

Signal & Noise Exclusive

Publishers create the content. Publishers build the audiences. Publishers bear the costs.

Yet somehow, after two decades of digital advertising innovation, many publishers capture only a fraction of the value they create.

In this Signal & Noise exclusive, Rio Longacre and Brett House sit down with Keith Petri, SVP of Data, Identity & Supply at Viant, and Rich Hyden, SVP of Publisher Solutions at Viant, for a deep discussion on one of the most important questions facing the open internet:

Can publishers reclaim their economic future—or are the economics of digital media fundamentally broken?

This conversation coincides with Viant's major announcement of Viant Publisher Solutions, a new suite of capabilities designed to improve transparency, signal quality, monetization, identity activation, and supply-path efficiency for publishers while simultaneously improving advertiser outcomes. It represents one of the most significant publisher-focused initiatives launched by a major DSP in recent years.

But this episode goes far beyond a product launch.

Together, Keith and Rich unpack the realities of modern publisher monetization, the hidden inefficiencies inside today's programmatic supply chain, why signal quality increasingly determines revenue, and how identity has become one of the most valuable assets publishers possess.

The discussion explores how technologies such as SupplyIQ, Direct Access, Household ID, and IRIS_ID aim to create a more direct relationship between buyers and sellers while reducing friction, duplication, and information loss throughout the advertising ecosystem.

Along the way, the conversation tackles some of the biggest debates in advertising today:

• Why premium publishers continue to struggle despite creating enormous value
• Whether supply-path optimization helps publishers—or hurts them
• The role of identity in driving publisher revenue
• Why signal quality may be the new currency of digital advertising
• How CTV is reshaping publisher economics
• The growing power of walled gardens versus the open internet
• Why contextual intelligence is becoming increasingly important
• How AI and agentic media buying may transform programmatic advertising
• Whether DSPs can play a meaningful role in helping publishers thrive
• What the future of publisher monetization looks like over the next decade

Keith and Rich also share a fascinating perspective from inside one of the industry's most innovative DSPs, explaining why better advertiser outcomes and healthier publisher economics may no longer be competing objectives.

For anyone working in digital media, ad tech, publishing, identity, programmatic advertising, retail media, CTV, measurement, or AI-powered marketing, this is a conversation packed with practical insights and big-picture thinking.

The future of the open internet may depend on whether buyers and sellers can become more aligned.

This episode explores what that future could look like.

Guests

• Keith Petri, SVP Data, Identity & Supply, Viant
• Rich Hyden, SVP Publisher Solutions, Viant

Hosted By

• Rio Longacre
• Brett House

Topics Covered
Publisher Monetization • Programmatic Advertising • Supply Path Optimization (SPO) • CTV • Identity Resolution • First-Party Data • Household ID • Contextual Targeting • Publisher Data Strategy • AI Advertising • Agentic Media Buying • Open Internet Economics • Ad Tech Infrastructure • Signal Quality • Media Quality • Digital Advertising

#SignalAndNoise #AdTech #ProgrammaticAdvertising #Viant #PublisherMonetization #CTV #IdentityResolution #DigitalAdvertising #RetailMedia #AIAdvertising #OpenInternet #MarketingTechnology

Enjoy!

What happens when an early Amazon engineer who helped pioneer automated advertising leaves AdTech behind to build the world's fastest commercial airliner?

In this episode of Signal & Noise, Brett House and Rio Longacre sit down with Blake Scholl, Founder and CEO of Boom Supersonic, for a wide-ranging conversation about innovation, entrepreneurship, aviation, AI, and why some of the world's biggest opportunities are hiding in plain sight.

Before launching Boom, Blake helped shape early internet advertising at Amazon, worked through the hypergrowth years at Groupon, and experienced firsthand the rise of AdTech, recommendation engines, and large-scale customer acquisition systems. Then he made an unlikely leap: leaving software behind to tackle one of the hardest problems in engineering—bringing commercial supersonic flight back from the dead. 

The discussion explores what Blake learned from Amazon, why he believes Groupon missed a once-in-a-generation opportunity, and how short-term thinking can destroy even the most promising companies. Along the way, he shares stories about crashing Google AdWords, building the world's largest spam operation at Groupon, and getting some of his toughest lessons directly from Jeff Bezos. 

The conversation then shifts to Boom Supersonic's mission, the history of the Concorde, the regulatory decisions that stalled aviation innovation for decades, and Blake's controversial view that technological progress didn't slow because the problems became harder—it slowed because society stopped pursuing them. 

Blake also reveals the behind-the-scenes story of Boom's near-collapse, the seven-year pursuit of a partnership with Rolls-Royce, and how a failed engine deal ultimately led to one of the company's biggest breakthroughs: building its own propulsion technology and creating a new turbine business that may help fund the future of supersonic travel. 

The episode closes with Blake's thoughts on AI, entrepreneurship, and why the biggest impact of AI may not be job replacement—but the creation of millions of new builders capable of turning ideas into reality. 

• Blake Scholl's journey from Amazon and Groupon to Boom Supersonic
• Early AdTech, automated advertising, and internet growth stories
• Lessons from Jeff Bezos and Amazon's long-term thinking culture
• Groupon's rise, fall, and missed platform opportunity
• Why innovation in aviation stalled after Concorde
• Regulatory capture and the history of supersonic flight
• Building Boom Supersonic from scratch
• The failed Rolls-Royce partnership and Boom's engine strategy
• Turbine power generation, AI infrastructure, and data centers
• The future of commercial supersonic travel
• Why AI may create more entrepreneurs than ever before
• Long-term thinking, risk-taking, and building hard-tech companies

Whether you're interested in aviation, startups, AI, AdTech, or the future of innovation itself, this is a fascinating conversation about what happens when someone decides to stop optimizing digital systems and start building physical ones.

#SignalAndNoise #BoomSupersonic #BlakeScholl #Aviation #SupersonicFlight #AI #Entrepreneurship #Innovation #Amazon #Groupon #AdTech #MarTech #HardTech #Technology #FutureOfWork #CommercialAviation #ArtificialIntelligence #StartupLeadership #Engineering #DigitalTransformation

What happens when the technology that powers advertising becomes the thing consumers interact with directly?

In this episode of Signal & Noise, Rio Longacre and Brett House sit down with industry analyst Karsten Weide to explore one of the most important—and controversial—questions facing the digital economy: how AI will ultimately make money.

From advertising inside chatbots to agentic commerce, autonomous media buying, and the future of the open web, Karsten shares why he believes AI will reshape advertising more profoundly than any technology shift he has witnessed in more than three decades covering media, technology, and digital advertising. 

The conversation examines the rapid emergence of agentic AI across the advertising ecosystem, the growing role of automation in media planning and optimization, and why the traditional programmatic supply chain may be heading toward a fundamental transformation. Karsten explains how platforms, DSPs, SSPs, publishers, agencies, and brands are all racing to build their own agentic operating systems—and what that means for the future structure of the advertising market. 

The discussion also tackles one of the industry's most debated topics: advertising inside AI products. Will OpenAI, Google, Anthropic, and Perplexity eventually embrace advertising as a major revenue stream? What will AI-native ad formats actually look like? And can advertising coexist with the trust users expect from conversational interfaces? 

The conversation then turns to agentic commerce—the emerging world where AI agents shop, compare products, negotiate prices, and make purchases on behalf of consumers. If agents become the primary buyers, what happens to search advertising, performance marketing, and the broader digital advertising ecosystem? Karsten offers a provocative perspective on why this future may create a surprising renaissance for brand advertising, display, video, audio, and out-of-home media. 

Along the way, the group discusses:

• Why AI adoption in advertising is still in its earliest stages
• The rise of autonomous media buying and agentic workflows
• How Amazon, Google, Meta, and emerging ad tech players are approaching AI-powered advertising
• The future of SMB advertising and self-service media buying
• The battle between open standards and proprietary AI ecosystems
• Why the programmatic supply chain may look dramatically different in five years
• The opportunities and risks of AI-driven creative production
• Whether AGI is actually closer than most people think
• How AI could reshape agency operating models and industry employment

If you've been wondering how advertising, commerce, media buying, and consumer behavior evolve in a world increasingly shaped by AI agents, this episode provides one of the most thoughtful and grounded discussions available today.

The future of advertising may not be humans buying from brands. It may be agents buying from agents. And the implications are enormous.

What happens when a society can no longer agree on basic facts?

In this episode of Signal & Noise, Rio Longacre and Brett House sit down with Vanessa Otero, Founder and CEO of Ad Fontes Media and creator of the influential Media Bias Chart, to explore the growing crisis of trust, polarization, and information quality in the modern media landscape.

Vanessa's Media Bias Chart began as a personal project during the 2016 election cycle and has since evolved into one of the most widely recognized frameworks for evaluating media bias and reliability. Today, Ad Fontes Media has analyzed tens of thousands of articles, podcasts, and news sources to help consumers, educators, advertisers, and publishers better understand the fragmented information ecosystem. 

The conversation explores how the industry arrived at a moment where millions of people consume different versions of reality, why social media algorithms amplify outrage and identity over truth, and whether objective journalism is still possible in an era dominated by platforms, creators, and AI-generated content. Vanessa shares her perspective on the collapse of trust in institutions, the economics of journalism, and why the survival of high-quality reporting matters far beyond the news business itself. 

The discussion also dives into the business challenges facing journalism. As local newspapers disappear, digital advertising consolidates around a handful of technology platforms, and AI reshapes content discovery, many news orgs are struggling to sustain the costly work of original reporting. Vanessa explains why journalism functions as critical societal infrastructure, how advertising incentives have unintentionally weakened the news ecosystem, and why she believes advertisers have a role to play in preserving high-quality information. 

Rio and Brett challenge Vanessa on whether audiences actually want objective reporting, the role of algorithms in reinforcing worldviews, and whether we're witnessing a return to a more fragmented media environment reminiscent of the pre-broadcast era. Together they explore the tension between engagement and truth, the rise of podcasts and creator-driven media, and whether consumers are becoming more aware of the ways technology influences their perception of reality. 

The episode also examines the future of advertising against news content. Vanessa discusses Ad Fontes Media's work helping advertisers identify high-quality news environments, the importance of context and trust in media buying, and why she believes many brands have made a mistake by avoiding news altogether. The conversation touches on CTV, media quality signals, brand safety, and the emerging role of AI in both improving and undermining the information ecosystem. 

Whether you're a marketer, publisher, journalist, technologist, policymaker, or simply someone trying to make sense of today's media environment, this conversation offers a thoughtful and nuanced look at one of the most important challenges facing modern society.

• The origin story of the Media Bias Chart
• Why people increasingly live in separate realities
• Trust, misinformation, and disinformation
• The collapse of local journalism
• Social media, algorithms, and outrage economics
• Why advertisers stopped buying news
• The business model crisis facing publishers
• AI-generated content and the future of journalism
• Podcasts, creators, and media fragmentation
• News quality, media trust, and brand safety
• CTV advertising and Ad Fontes Media's partnership with Viant
• Whether objective journalism is still possible

Vanessa Otero is the Founder and CEO of Ad Fontes Media, the company behind the Media Bias Chart. A former patent attorney, Vanessa launched the chart during the 2016 election cycle to help people better understand media bias and reliability. Today, Ad Fontes Media provides media quality ratings used by advertisers, publishers, educators, researchers, and consumers seeking greater transparency in the information ecosystem.

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 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.

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.

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 acquiring LiveRamp.

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!

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

For the better part of two decades, the buy-side controlled the game.

Data. Decisioning. Optimization. Margin. Publishers? Commoditized. Intermediated. Squeezed.

But that era may be ending.

In this episode, Joe Root (Co-Founder & CEO, Permutive) returns to Signal & Noise with a sharper—and far more disruptive—thesis: AI is shifting the center of gravity of advertising back to the sell-side.

We unpack what happens when:

• Decisioning moves closer to the data
• Signal-rich environments outperform identity graphs
• And publishers stop selling impressions… and start selling outcomes

Because if the most valuable data lives on the sell-side—and AI can act on it in real time—then the entire AdTech stack gets rewritten.

The Death of the “Buy-Side-First” Internet
Joe breaks down why the traditional model—where DSPs optimize against thin, degraded signals—is fundamentally broken. By the time an impression reaches the bidstream, most of the signal is already gone. The result? Poor targeting, wasted spend, and a race to the bottom.

The Rise of Sell-Side Intelligence
Permutive’s approach flips the model: decisioning happens at the edge, inside publisher environments, where the richest behavioral and contextual data actually exists. 
This isn’t just better targeting—it’s a different architecture.

From Curation to AI-Driven Outcomes
What started as curation and probabilistic targeting is evolving into something bigger:
→ Real-time prediction
→ Continuous optimization
→ Outcome-based execution

We explore how AI turns fragmented signals into scalable performance—and why this unlocks a new commercial model for publishers. 

The “Outcomes Era” Explained
Selling impressions is easy. Selling outcomes is hard.
Joe explains what actually has to change—technically and commercially—for publishers to move from CPMs to measurable business results. 

Agency Business Model Reset
As AI erodes the billable-hours model, agencies are being forced into a new role:
→ Investment managers
→ Principal traders
→ Outcome owners

We dig into how this shift is reshaping incentives, margins, and how media gets bought.

Agentic Trading & the Future of the Market
If both buyers and sellers deploy AI agents, what happens next?
Do auctions disappear—or get demoted?
Does allocation move upstream—before an impression is ever served?
And who wins when media becomes negotiated instead of auctioned? 

This isn’t a conversation about incremental optimization.

It’s about who controls the advertising system in an AI-driven world.

Because if Joe is right:

• DSP-centric decisioning gets abstracted
• SSPs and publishers gain leverage
• And the open web—long written off—may have its strongest comeback yet

The future of advertising won’t be defined by who buys impressions fastest.

It will be defined by who controls signal, decisioning, and outcomes closest to the user.

And for the first time in a long time… that might be the sell-side.

Most companies don’t fail at strategy. They fail at execution=.

In this episode of Signal & Noise, Brett House and Rio Longacre sit down with Tom Amies-Cull—a seasoned operator who has spent two decades inside the most complex, high-pressure agency environments, including senior leadership roles across IPG, Dentsu, and Kinesso.

This isn’t a conversation about AdTech plumbing.
It’s about something far more fundamental—and far more broken:

How organizations actually work.
Or more accurately… why they often don’t.

Drawing from years inside the machine, Tom unpacks the uncomfortable truth behind transformation in large, matrixed organizations:
It’s not a strategy problem. It’s a coordination problem. It’s a leadership problem. It’s an operating model problem.

As he puts it: “Transformation usually fails not because companies lack strategy, but because they can’t convert intent into coordinated behavior.”

This is a candid, sometimes blunt breakdown of what actually gets in the way of change:

• Why most “transformations” are just reorgs in disguise
• How internal politics quietly kill execution
• The real reason employees aren’t change-resistant—they’re resistant to bad change
• Why strategy decks and org charts are not operating models
• How unclear decision rights create organizational paralysis
• The hidden role of middle management as the “connective tissue” of execution
• Why leadership teams say they want accountability—but often avoid it in practice

There’s a lot of industry noise right now about agencies evolving into platforms, operating systems, and AI-powered machines.

Tom brings this conversation back to reality:

Most organizations are further away than they think.

Not because the vision is wrong—
but because the underlying systems (people, incentives, culture, decision-making) aren’t built to support it.

The result?
Pockets of excellence… held together by heroic effort, not scalable design.

Everyone is talking about AI.

But Tom reframes it:

AI isn’t a technology problem.
It’s an operating model and leadership problem.

AI can accelerate planning, production, and activation—but it cannot fix:

• Fragmented P&Ls
• Misaligned incentives
• Poor leadership behaviors
• Broken decision-making structures

If those don’t change, AI just makes dysfunction happen faster.

We also explore why indie agencies and PE-backed firms may have an edge right now:

• Less structural debt
• Faster decision-making
• Clearer accountability
• Stronger focus on value creation

While legacy holdcos wrestle with complexity, challengers are moving faster—and with purpose.

This episode is about closing the gap between:

What companies say they are…
and what they are actually capable of doing.

Because in today’s environment, speed matters.
Clarity matters.
Execution matters most.

• Agency transformation
• Operating models and org design
• Leadership in complex organizations
• AI’s real impact on the industry
• The future of holding companies

…this is a must-listen.

📩 Connect with Tom:
Find him on LinkedIn or through his advisory work (linked in show notes)

🎧 Follow Signal & Noise:
Subscribe for more unfiltered conversations with operators shaping the future of media, advertising, and AI.

What happens when AI stops being a tool—and starts redefining what work actually is?

In this episode of Signal & Noise, Brett House and Rio Longacre sit down with Jennifer Borchardt—UX leader, systems thinker, and newly minted Signal & Noise Executive Voice contributor—to unpack one of the most urgent questions of our time: What does AI mean for jobs, identity, and society itself?

Drawing on decades of experience at firms like Sapient, Slalom, Wells Fargo, and U.S. Bank, Jennifer brings a rare perspective that blends design, behavioral science, and real-world systems thinking. This isn’t a surface-level conversation about productivity gains—it’s a deep dive into the structural shifts already underway.

Together, they explore:

• Why the labor-based economy may be fundamentally incompatible with AGI
• The rise of the “hyphenate worker”—and the slow death of specialization
• How AI is unbundling work, eliminating entry-level pathways, and reshaping career trajectories
• The uncomfortable truth about who benefits—and who gets left behind
• Why most companies are still wildly unprepared, despite the hype
• The growing tension between innovation, regulation, and power concentration
• And the deeper question few are asking: If work disappears, what happens to meaning, identity, and purpose?

Jennifer also reacts to major industry frameworks, including the OpenAI “Industrial Policy for the Intelligence Age” and the Stanford University AI Index, highlighting the gap between bold policy visions and real-world human impact.

This episode is equal parts optimistic and unsettling. Because while AI promises unprecedented productivity and wealth creation, it also forces us to confront a harder reality:

Work isn’t just income. It’s identity. And we’re about to rewrite both.

🎙️ About Jennifer Borchardt
Jennifer is a UX and digital transformation leader who has spent her career at the intersection of design, technology, and human behavior. She recently joined Signal & Noise as an Executive Voice contributor, where she explores the societal implications of AI and the future of work.

📖 Companion Article
Don’t miss Jennifer’s long-form piece on Signal & Noise:
“Architecting Resilience in the Intelligence Age” — a deeper exploration of the ideas discussed in this episode.

If you’re building, hiring, leading—or just trying to stay relevant—this conversation is required listening.

Because AI isn’t just changing how we work. It’s changing why we work.

This compilation episode captures the final day of POSSIBLE 2026 in Miami — where the tone shifts from hype to clarity. Filmed entirely on-site, these conversations reflect what leaders actually think after three days of meetings, launches, and late-night debates.The signal coming out of Day 3 is clear: The industry is moving from tools → systems, from workflows → agents, and from execution → orchestration. This is where AI stops being a feature — and starts becoming infrastructure.Who’s Featured:- Mano Pillai — Co-CEO & Co-Founder, HyperMindZ: Why AI needs a control layer to manage autonomous systems at scale.- Anthony Dominguez — Senior Director, Multichannel Marketing, Orange142: The reality of multichannel execution and why coordination is still broken.- Ian Maier — GM, AdTech, Hightouch: How the composable CDP model is reshaping activation and challenging legacy stacks.- Laura Foster — SVP, Marketing, GumGum: Moving beyond targeting to moment + mindset as the real driver of performance.- Michael Akkerman — Chief Business Officer, Digital Turbine: The untapped opportunity in in-app media and the importance of relevance.- Luc Benyon — Marketing Director at Adsquare: How platform dynamics and buyer behavior are shifting in a fragmented ecosystem.- Sebastian Pinzon — Data & AdTech Strategist: Why interoperability and data infrastructure are becoming non-negotiable.- Joanna Drews — Co-Founder and CEO at HyphaMetrics: Bridging strategy and execution in a rapidly changing landscape.- Joshua John — Head of DSP Strategy, at Yahoo!: The future of DSPs in an agentic, automated workflow world.- Crystal Wallace — COO at Omnicom's Kinesso: The gap between transformation strategy and real execution.- Adam Woods — Chief Product Officer, Incubeta: Rebuilding the agency model around AI-native operating systems.Key Themes* Agentic AI and autonomous media execution* Control layers, governance, and orchestration* The collapse of legacy AdTech workflows* From point solutions → integrated systems* Why timing, context, and signals matter more than everFinal Thought: Day 3 wasn’t about what’s possible. It was about what’s already happening —and how fast the rest of the industry needs to catch up.

Day 2 of POSSIBLE in Miami was Signal & Noise at full speed—filming live from the Press Room, sitting down with some of the sharpest operators across adtech, martech, and AI. If Day 1 was about setting the stage, Day 2 was about how it actually works—the systems, tradeoffs, and rewiring happening underneath the industry.

Here’s who we spoke with—and what they unpacked:

• Adam Heimlich (Chalice AI) — Agentic media buying, containerized decisioning, fixing broken DSP models
• Ana Mourão (Black & Decker) — First-party data activation, martech–adtech convergence, retail media
• Mike Finnerty (Mutinex) — AI-powered MMM, incrementality at speed, killing “great but late” measurement
• Xander Kotsatos (Aqfer) — Zero-copy data, infrastructure economics, data pipelines as the new moat
• David Nyurenberg (InterMedia Advertising) — Digital evolution, agency strategy, performance realities
• Doug Lauretano (Tuple) — DSP reinvention, supply path inefficiencies, unlocking “lost” inventory
• Neej Gore (Zeta Global) — Identity graphs, decisioning systems, AI as the marketing brain
• Sara Martinez (Tracer) & Vinny Rinaldi (Hershey’s) — Semantic layers, measurement clarity, brand execution
• Jill Randell (Fuse) — AI for insights, fixing broken dashboards, trust in data
• Tom Koch (TwelveLabs) — Video understanding AI, multimodal models, unlocking unstructured data 
• Todd Ulise (Nomix Group) — AI, Synthetic Creators, and Commerce at Scale

The big themes from Day 2:

This wasn’t surface-level AI hype. The throughline was clear:

• AI is moving from tools → systems → decisioning layers
• Data is still the bottleneck (and the battleground)
• The stack is collapsing into “operating systems”
• Measurement is being rebuilt from first principles
• The open web vs. walled gardens fight is evolving—not ending

Across every conversation, one idea kept coming up:

The winners won’t just use AI—they’ll re-architect around it.

From agentic trading models to real-time MMM, from identity graphs to video intelligence, Day 2 showed what the industry looks like when you zoom past the buzzwords and into the machinery.

And it’s clear:

The future of marketing isn’t just automated—it’s negotiated, orchestrated, and deeply data-native.

This compilation brings together the sharpest insights, debates, and moments from the floor.👉 Want the full conversations?Each interview is available individually on our YouTube channel.About Possible 2026Possible is one of the fastest-growing gatherings in media, marketing, and technology—bringing together brands, platforms, agencies, and innovators to define what’s next. More from Day 3 coming soon.

Recorded live from the MadConnect Mansion in Miami, this special Signal & Noise episode captures Day 1 of Possible 2026—bringing together some of the sharpest operators across advertising, media, data, and AI.

Across a full day of rapid-fire interviews, Rio Longacre, Brett House, and Krish Raja sat down with industry leaders to unpack what’s actually happening beneath the surface of the hype cycle.

• Dave Rosner — On the untapped power of publisher data and the buy-side blind spot
• Pete Blackshaw — On brand trust in an AI-driven discovery landscape
• Patrick Duggan — On AI monetization and the race to profitability
• Erez Levin — On ad quality, attention, and the race to the bottom
• Aubriana Alvarez Lopez — On orchestration, AI, and fixing the adtech operating model
• Nathan Lindberg — On gaming, creators, and the next media frontier
• MK Marsden — On AI’s impact on sales and the future of the revenue engine
• Sarah Caputo — On CTV monetization, frequency, and the viewer experience

From AI-driven operating models and monetization strategies to CTV fragmentation, ad quality, and the rise of new creator ecosystems—Day 1 of Possible made one thing clear:

The industry isn’t lacking innovation—it’s struggling to operationalize it.

This compilation brings together the sharpest insights, debates, and moments from the floor.

👉 Want the full conversations?
Each interview is available individually on our YouTube channel.

Possible is one of the fastest-growing gatherings in media, marketing, and technology—bringing together brands, platforms, agencies, and innovators to define what’s next. Day 1 set the tone: AI is here, but execution is the real battleground.

More from Day 2 coming soon.

What does it actually mean to be “full stack” in a world where AI is starting to write the code for you?

In this episode of Signal & Noise, Lucas Longacre sits down with Viktor Williamson for a deep, practitioner-led conversation on the evolution of software engineering—from fundamentals to the fast-emerging agentic future.

This marks Lucas’s second long-form appearance on S&N and builds on his work as an Executive Voices contributor, bringing a product-led perspective to a rapidly shifting technical landscape.

Together, Lucas and Vik break down:

• What “full stack” actually means—and why it’s becoming more critical than ever
• How modern frameworks like Next.js are reshaping development paradigms
• Why performance, flexibility, and cross-functional thinking are redefining engineering roles
• The real impact of AI and agentic coding on how software gets built
• Why engineers are shifting from writing code → reviewing, guiding, and orchestrating it
• The growing importance of accessibility, security, and human-centered design in modern systems

But this isn’t just a technical conversation—it’s a philosophical one.

As AI accelerates development, the question isn’t whether engineers will be replaced. It’s whether they can evolve fast enough to stay relevant.

“We’re not doing less work. We’re being asked to do more—with higher expectations and faster timelines.” 

From personal projects and “disposable apps” to the future of software as hyper-personalized infrastructure, this episode explores where the craft of engineering is headed—and what it means to stay human in the loop.

• Full stack as a mindset, not just a skillset
• Agentic coding and the rise of AI-assisted development
• From builders to orchestrators: the new role of engineers
• Personal software, micro-apps, and the death of bloated platforms
• Accessibility and security as non-negotiables
• Human judgment in an automated world

Viktor Williamson is a full stack engineer, educator, and mentor with a passion for human-centered development. With roots in front-end engineering and deep experience across the stack, Vik brings a rare combination of technical depth, communication skill, and real-world perspective on how engineers learn, build, and evolve.

Everyone agrees first-party data matters. So why is almost none of it actually showing up in media?

In this episode of Signal & Noise, Rio Longacre and Brett House sit down with Rob McLaughlin, Founder & CEO of AUDIENCES, to unpack one of the biggest disconnects in modern marketing: the gap between data strategy and data activation.

Despite years of investment in CDPs, clean rooms, cloud migrations, and identity graphs, less than 3% of media spend is actually informed by first-party data. 

That’s not a tooling problem. It’s a model problem.

Rob breaks down why the industry has been stuck—and what needs to change:

• Why first-party data is widely understood… but rarely used
• Where activation actually breaks (hint: it’s not just tech)
• How data movement, cost, and org friction quietly kill execution
• Why brands are still paying a “tax” to use their own data
• And why the future isn’t more platforms—it’s less movement, more signal

Marketers didn’t fail to invest. They failed to connect.

Most brands now have:

• Cloud infrastructure
• Massive first-party datasets
• Sophisticated media partners

But the “last mile”—getting that data into live campaigns—is still fragmented, expensive, and slow. 

The result? Campaigns ship without it. Rob’s thesis is simple—but disruptive: Stop moving data. Start moving signals.

AUDIENCES flips the model:

• Keep data inside the brand’s cloud
• Activate directly from the source
• Eliminate onboarding, duplication, and unnecessary cost

This isn’t just cleaner architecture—it changes:

• Privacy dynamics (no data copying)
• Cost structure (no per-record tax)
• Speed of activation (real-time, not batch)

Forget the buzzwords. This is where it works:

• Suppression → Stop wasting spend on existing customers
• Seed audiences → Outperform platform-native targeting
• Lookalikes → Scale what actually drives value
• Retail media & supply-side data → Unlock real reach

And yes—brands are still getting retargeted with products they already bought.

This episode goes beyond tactics into structure:

• Why the future agency looks more like an operating system than a service layer
• Why identity + infrastructure is becoming the real differentiator
• How the balance of power is shifting back toward data owners
• And why agencies without deep data integration risk becoming assemblers—not operators

• “CDPs are irrelevant in a cloud-native world.”
• “There should not be a tax on activating your own data.”
• “The problem isn’t that brands lack data—it’s that they can’t use it.”

• CMOs and Heads of Media trying to unlock real performance from 1P data
• Data & platform leaders stuck between cloud investment and activation reality
• Agency leaders navigating the shift from services → infrastructure
• Anyone tired of hearing “first-party data is the future” without seeing results

Explore AUDIENCES: https://weareaudiences.com
Connect with Rob McLaughlin: https://www.linkedin.com/in/robanalytics/

If you’re building at the intersection of data, media, and AI—this is your playbook.

Subscribe to Signal & Noise on:

• Spotify
• Apple Podcasts
• YouTube

🔍 What You’ll Learn⚡ The Big Idea: The Last Mile Is Broken🧠 A Different Model: Signals, Not Datasets🚀 Where First-Party Data Actually Wins🏗️ What This Means for Agencies & HoldCos💥 Hot Takes from Rob🎯 Who This Episode Is For🔗 Learn More🔊 Listen & Subscribe

Everyone says they want growth. Very few organizations are actually built to deliver it.

In this episode of Signal & Noise, we sit down with George Musi—former executive across Publicis, WPP, IPG, and Horizon, now an advisor to Fortune 500 and high-growth companies—to unpack why growth consistently breaks inside large organizations.

This is not a conversation about frameworks or slideware.
It’s about what actually happens when strategy hits reality.

George brings an operator’s lens to one of the biggest disconnects in the industry right now: companies are overflowing with ideas, AI roadmaps, and transformation narratives—but still struggle to execute consistently. 

Why growth is structurally hard
Growth doesn’t fail because of a lack of strategy—it fails because of how organizations are wired. Incentives, silos, and decision-making systems break execution long before ideas do.

The HoldCo → Operating System shift (and what’s real vs narrative)
Every agency claims to be building an “operating system.”
George explains why most of these are still fragmented point solutions—and what a true system actually requires.

Why agencies are colliding with consultancies and platforms
As agencies move into data, tech, and integration, they’re stepping directly into the territory of firms like Accenture and Deloitte—while also competing with platforms like Google and The Trade Desk.

AI is not a tool problem—it’s an operating model problem
Most companies are layering AI on top of broken systems.
The result: more noise, not more output.
Real impact requires rebuilding how knowledge, workflows, and decisions actually operate.

The real future of the agency model
The FTE-based, labor-driven model is under pressure.
What replaces it? Outcome-based partnerships, deeper expertise, and a shift toward “growth architects” over execution vendors.

Why institutional knowledge is the ultimate advantage
Data is commoditized. Models are commoditized.
The real differentiator is the ability to capture, retain, and compound organizational knowledge over time—and most companies are terrible at it.

• Strategy isn’t the problem. Execution systems are.
• AI amplifies what already exists—it doesn’t fix it.
• The agency model is being reshaped from labor → leverage.
• The winners will be those who build systems of intelligence, not just tools.
• Growth will be owned by those who can connect strategy, operations, and execution into a single system.

George also joins Signal & Noise as part of our Exec Voices platform—bringing a no-BS perspective on growth, go-to-market, and what actually works inside complex organizations.

If you care about where agencies, consultancies, and platforms are heading—and what it really takes to scale—this is a must-listen.

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What we cover:Key takeaways:

Most B2B companies don’t fail because the product is bad.
They fail because the system around it is broken.

In this episode of Signal & Noise, we sit down with Marc Sabatini, Co-Founder and Chief Commercial Officer at HighSignals, to break down what Brett has been calling the GTM Slop Problem—and why so many launches stall before the market even has a chance to decide.

Marc has spent 30+ years operating at the intersection of product, sales, marketing, and customer success—the exact place where strategy either turns into revenue… or quietly falls apart. 

This is not a theory episode.
This is how go-to-market actually works—or doesn’t—in the real world.

1. Why GTM breaks before the market even decides
The biggest failure point isn’t competition.
It’s internal:

• Misaligned teams
• Fuzzy narrative
• No shared system
• No clear ICP

As Marc puts it: “It’s not just slop from inexperience. It’s slop from lack of coordination.” 

2. The Six Dimensions of GTM (and where they fall apart)

We walk through the six dimensions every company thinks they have dialed in:

• Narrative (not messaging—commercial logic)
• ICP & targeting (focus vs. “sell to everyone”)
• Competitive readiness
• Offer & proof
• Field enablement
• Activation & measurement

The takeaway:
Most companies don’t have weak pieces.
They have weak connections between the pieces.

3. Problem Market Fit vs. Product Market Fit vs. Platform Market Fit

One of the most important frameworks in the episode:

• Problem Market Fit → You can sell, but it’s messy
• Product Market Fit → You can scale
• Platform Market Fit → You can compound

Most companies get stuck in the first phase—generating revenue without ever building a system that scales.

4. Why “great products win” is a myth

The uncomfortable truth:

The best product rarely wins.
The best go-to-market does.

Even strong products fail when:

• Narrative is unclear
• Proof is weak
• Sales is improvising
• Customer success is disconnected

Or simply:
Too much activity. Not enough coherence.

5. Partner-Based Buying is changing everything

Buyers aren’t buying alone anymore.

Agencies, SIs, cloud partners, and ecosystems are now:

• Influencing decisions
• Validating vendors
• Acting as gatekeepers

Which means:
You’re not just selling to the end buyer.
You’re selling to the entire buying system.

And that changes your proof stack completely.

6. Why most companies fail entering the US market

Marc breaks down a common pattern:

• Too broad ICP
• Weak local proof
• Misread buyer expectations
• Activity without traction

The result:
Burned time, burned budget, and burned field trust.

7. The real job of GTM: building a system, not running campaigns

This is the core idea of the episode:

GTM is not marketing.
GTM is not sales.
GTM is not a launch plan.

It’s a connected commercial system.

And when that system breaks:

• Messaging gets blamed
• Sales gets blamed
• Product gets blamed

But the real issue is lack of alignment and orchestration.

There’s more product being built right now than ever before.
AI has lowered the barrier to creation.

But it hasn’t solved:

• Positioning
• Differentiation
• Commercial execution

If anything, it’s made the problem worse.

More products.
More noise.
More GTM slop.

• Founders trying to turn product into revenue
• CROs, CMOs, and GTM leaders fixing broken systems
• Anyone launching B2B SaaS or AI products
• Operators tired of “more activity” being the answer

If there’s one idea to walk away with, it’s this:

A launch is not ready just because the product is ready.
It’s ready when the system around it is coherent.

Most companies never get there.

In Part 1, we talked about bots. In Part 2… we follow the money.

Brett and Rio sit down again with Dr. Augustine Fou, and this time the conversation goes deeper—and gets a lot more uncomfortable. Because the real issue isn’t just fraud. It’s the system that quietly profits from it.

We break down what actually happens between the moment a marketer places a bid and the moment a publisher gets paid. The gap is bigger than most people realize. In one example, a $1.50 CPM turns into just $0.15 for the publisher. In another, increasing your bid doesn’t get you better inventory—it just means someone in the middle keeps more of your money. And a lot of what gets reported as “performance” can be completely fabricated—clicks, conversions, even analytics data .

This episode is about that hidden layer most teams never see: take rates, pass-through opacity, spoofed supply, and the growing gap between what advertisers pay and what publishers actually receive.

Then we turn to what’s coming next: agentic AI.

Because if your inputs are broken, automation doesn’t fix it—it just scales the problem faster. The same systems that already struggle with transparency are now being handed more autonomy, more budget, and more control.

We get into why blended averages hide the truth, why the industry’s long-standing “1% fraud” narrative doesn’t hold up, and why so many optimization systems are quietly steering budgets toward cheaper, lower-quality inventory. We also unpack the difference between “attention” and actual human engagement—and why so much of what’s sold today is closer to performance theater than real outcomes.

By the end, this isn’t just a critique—it’s a playbook.

If you’re a marketer, this episode will change how you think about where your dollars are going, who’s extracting value in the supply chain, and what you can do—right now—to take back control.

Because the real question isn’t:“Is there fraud?”

It’s: “Who’s getting paid—and how much are they taking?”