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