Deeper Diligence

About Deeper Diligence

Headlines move markets. Claims move businesses. We investigate both. Every episode breaks down the evidence behind the biggest stories in AI, advertising, ecommerce, and digital media, showing what can be verified, what can’t, and what everyone else missed.

Publisher

Daypart

Category

business · news

Language

en

Explicit

No

First episode

15 Jul 2026

Latest episode

13 Oct 2026

Latest episodes

9 episodes in the feed.

1. 

   13 Oct 2026

   S1E9 - Ad Fraud and AI:Combating Bots with Andrew Barrow and Jeromy Sonne

   Episode Notes Ad Fraud, Bots & AI Agents: How to Stop Paying for Fake Traffic (w/ Andrew Barrow, RevenueArc) On Deeper Diligence, host Bogdan Patynski and co-host Jeromy Sonne (CEO of Daypart) talk with Andrew Barrow, former holdco business development leader and founder of RevenueArc, about the under-discussed reality of ad fraud, bots, verification, and why AI is making the problem worse. Barrow explains how bots now mimic real users by interacting with ads via virtual machines, argues against the idea of advertising to AI agents because major LLMs are instructed to ignore ads, and recommends focusing on GEO/AEO and being a trusted reference source instead. He describes RevenueArc’s approach to avoiding fraud and brand-safety risks by skipping the open web, buying only PMPs, manually vetting sites and apps, and using targeted propensity modeling (Arc Foresight). He advises practitioners to turn off open exchanges, extended networks, and Google PMax, expect lower traffic but better quality, and test tools by watching for reduced bounce rates and improved performance. 00:00 Ad Spend Gone Wrong 00:20 Meet the Hosts 00:40 Guest Intro RevenueArc 01:45 Fraud in Advertising 02:06 How Bots Hide 03:22 Agents Versus Bots 04:11 Why LLMs Ignore Ads 06:55 Avoiding Open Web 08:13 Should Agents See Ads 12:18 Bot Fraud Origins 13:50 RevenueArc Playbook 15:32 Arc Foresight Targeting 16:42 Quick Fixes Today 18:04 Testing Future Solutions 19:41 Contact and Wrap Up This podcast is powered by Pinecast (https://pinecast.com).

2. 

   6 Oct 2026

   S1E8 - Building Brand Credibility in the AI Search Era (with Heather Physioc, VML)

   Episode Notes Host Bogdan Patynski and co-host Digital_Chadvertising interview Heather Physioc, Chief Discoverability Officer at VML, about credibility, verification, and brand discoverability across search and AI-driven platforms.  Heather talks about credibility which is cumulative and outlines three early steps: verify identity via real human signals and authoritative connections, publish high-quality original work with a clear point of view, and earn third-party validation.  They discuss fake audiences, temporary verification badges, black-hat tactics, and how scaling AI-generated content can backfire by looking spammy and damaging trust.  The conversation covers repetition vs corroboration, concerns about Google AI Overviews accuracy, emerging liability (including a Munich court ruling holding Google responsible for false AI overview claims), declining organic traffic, AI shaping brand narratives using off-site sources, the rise of junk sites, and a shift from open-web search to “cozier communities” like Reddit and Substack. Heather shares that LinkedIn is the easiest way to reach her. 00:00 Welcome And Introductions 00:56 March Chadness Explained 01:40 Building Credibility Fast 04:29 Fake Followers And Search Spam 05:21 Meaningful Verification Signals 07:27 Black Hat SEO And AI Content 10:22 Spotting Guru Credibility 14:03 AI Overviews And Misinformation 15:19 Who Is Liable For AI 18:38 Brand Search In The AI Era 20:54 Junk Sites And Vibe Coding 22:00 Incentives Investors And Ads 24:27 Cozy Communities Replace Search 27:11 Where To Find Heather This podcast is powered by Pinecast (https://pinecast.com).

3. 

   9 Sept 2026

   S1E7 - AI Is Irrelevant. Whoever Has the Most Data Wins

   Episode Notes In a Deeper Diligence guest episode, privacy researcher and former FTC researcher Shoshana Wodinsky explains what advertisers actually know about consumers—and how much of the data economy is built on inference rather than fact. Wodinsky breaks down how browsing behavior, purchases, device IDs, location signals, Wi-Fi logins, and other data points become audience profiles, including how a segment supposedly identifying 80,000 pregnant women ultimately traced back to devices that had simply passed near a Babies R Us. She also discusses behavioral targeting in sports betting, why AI may scale questionable advertising inferences rather than solve them, and a case where device and location data were used to identify and track a Catholic priest. The conversation examines the difference between knowing and inferring, why supposedly anonymous data can still identify people, what consumers can realistically do to protect themselves, and why meaningful consent remains difficult in modern advertising. Subscribe to The Diligence Report: https://newsletter.daypart.ai/ (https://newsletter.daypart.ai/) Follow Shoshana Wodinsky: LinkedIn: https://www.linkedin.com/in/swodinsky (https://www.linkedin.com/in/swodinsky) X / Twitter: https://x.com/swodinsky (https://x.com/swodinsky) 00:00 Welcome and Guest Intro 01:19 What Advertisers Actually Know About You 02:24 The “80,000 Pregnant Women” Audience 03:34 How Advertisers Infer Your Location 04:55 Gambling and Predatory Targeting 05:51 How Sports Betting Sites Nudge Behavior 07:05 Does AI Make Ad Targeting Better? 07:27 “Whoever Has the Most Data Wins” 08:35 When Advertising Inferences Get It Wrong 09:26 Why Targeted Ads Can Still Miss 10:12 Why Big Platforms Are Hard to Investigate 11:30 How Ad Tech Was Used to Identify a Priest 12:20 How “Anonymous” Data Can Reveal Your Identity 13:24 The Real-World Consequences of Location Data 14:16 Can You Actually Protect Your Data? 15:19 What Privacy Policies Reveal 16:22 Why Consumers Can’t Solve Privacy Alone 17:24 Can Targeted Advertising Exist Without Surveillance? 17:43 Consent and “No Means No” 18:26 Closing and Where to Find Shoshana This podcast is powered by Pinecast (https://pinecast.com).

4. 

   2 Sept 2026

   S1E6 - AI Ads, FTC Rules, and State Disclosure Laws: Staying Compliant with Synthetic Performers with Robert Freund

   Episode Notes Links: newsletter.daypart.ai (https://newsletter.daypart.ai) robertfreundlaw.com (https://robertfreundlaw.com) Robert Freund, an advertising and e-commerce lawyer, explains that most AI advertising risk comes from applying longstanding FTC Act Section 5 truth-in-advertising principles and endorsement rules rather than new federal AI-specific laws. He highlights emerging state disclosure requirements for AI-created “synthetic performers,” including New York’s law (with AG-only enforcement and stated penalties of $1,000 for a first violation and $5,000 for subsequent violations), Hawaii’s similar law, and a pending California version. He warns that disclosures don’t cure underlying deception, such as AI-generated before/after images or AI avatars giving “experience-based” testimonials, and notes deepfakes can trigger publicity rights and copyright issues. Responsibility generally remains with the advertiser and other parties who materially contribute, even when platforms generate creatives. Recommended practices include identifying applicable risks, strengthening e-commerce terms of sale (including arbitration/class action waiver), negotiating indemnities/limits with vendors, and auditing content before scaling. 00:00 Welcome and Guest Intro 00:10 Rob’s Legal Background 01:19 AI Ads and Existing FTC Rules 04:20 State AI Disclosure Laws 08:27 Will Enforcement Expand 10:51 Who Gets Targeted 14:24 Creative Liability Checklist 18:36 Platform Tools and Shared Liability 21:33 Penalties and Damage Ranges 25:35 Compliance Best Practices 27:35 Where to Follow Rob This podcast is powered by Pinecast (https://pinecast.com).

5. 

   25 Aug 2026

   S1E5 - Ads for AI Agents: How OpenAds.ai serves ads to AI Agents, Measures ROAS, & What Perplexity’s Block Means

   Episode Notes Bogdan Patynski and Digital Chadvertising hosts Deeper Diligence with Steven Liss, co-founder and CEO of OpenAds.ai (http://OpenAds.ai), discussing “ads for agents,” where sponsored information is served to AI crawlers (e.g., ChatGPT, Claude, Gemini) and can surface in user answers. Steven explains the market pull from brands wanting visibility in AI results, publishers’ monetization pressure as referral traffic drops, and why ads-to-agents could be an alternative to pay-per-scrape models. We cover Perplexity’s stance on blocking such ads, likely responses from major labs, and publisher counter-leverage. Steven also shares experiments using CDN detection to serve different agent-specific content, emphasizing that success depends on retrieval/SEO and that performance measurement can be done via referral codes and cost-per-action pricing. We also discuss disclosure, standards, gray-hat manipulation risks, and why legacy DSPs struggle with purely contextual, non-identity targeting. 00:00 Show Premise 00:27 Meet Steven Liss 01:11 Ads For Agents 01:58 Why Now 03:20 Platform Pushback 04:29 Who Pays 05:15 Blocking And Evasion 06:29 Trust And Relevance 08:10 Experiment Setup 09:58 Prompt Sensitivity 11:53 Agent Autonomy 12:51 Measuring With Codes 13:34 B2B Opportunity 14:30 Programmatic Future 15:28 DSP Challenges 17:42 Pricing And CPA 18:36 Power Dynamics 22:46 Standards And Disclosure 23:48 Gray Hat Risks 25:37 DSPs And Budgets 28:46 Fraud Watchouts 30:15 Agent Internet Future 31:05 Where To Follow This podcast is powered by Pinecast (https://pinecast.com).

6. 

   19 Aug 2026

   S1E4 - Understanding Attribution and Ad Fraud in 2026 with Dr. Fou

   Episode Notes Bogdan and co-host Digital Chatvertising interview Dr. Augustine Fou of Fou Analytics, a longtime digital marketer and ad-fraud researcher (ex‑McKinsey, American Express, Interpublic, Omnicom; PhD MIT), on why pixel attribution dominates because it’s easy and bundled with platforms, yet is oversimplistic and easily gamed. Fou explains better validation via holdout/“turn‑off” experiments (citing eBay’s paid-search test) to measure incremental impact using traffic and sales velocity, and recommends repeating on/off cycles for causal evidence. The discussion covers fraud seasonality spikes around budget deadlines (Q4, month/quarter ends) and in movie and political spending, how programmatic exchanges enabled fraud via fake sites and low-value apps, and why AI mainly lowers barriers rather than creating a new fraud wave. He details attribution gaming (e.g., footfall credit via mass device exposure) and advises reconciling platform-attributed conversions against real sales. Fou argues for evidence-based verification and transparent supporting data, contrasting legacy verification approaches, and offers common-sense ways to spot MFA sites. 00:00 Welcome and Introductions 01:27 Why Pixel Attribution Wins 04:25 Beyond Pixels Holdout Tests 07:01 Budget Pressure and Incrementality 08:57 Fraud Seasonality in Q4 11:51 Who Are the Bad Guys 16:24 Old Fraud Still Thrives 18:05 AI and Modern Fraud Tricks 20:01 How Attribution Gets Gamed 25:17 DIY Truth Checks for Nontechnical Teams 28:58 Turn Off Experiment Playbook 30:19 Verify Traffic and Vendor Claims 37:04 Spotting MFA Sites Fast 40:04 Wrap Up and Where to Reach Dr Fou This podcast is powered by Pinecast (https://pinecast.com).

7. 

   3 Aug 2026

   S1E3 - Decoding Attribution: A Deep Dive with Eric Tilbury

   Episode Notes In this episode of Deeper Diligence, Digital_Chadvertising and guest Eric Tilbury (VP of Programmatic & Solutions Engineering at Inuvo) break down how ad attribution works and why it often fails. They explain user-ID and pixel-based attribution, including view-through vs click-through conversions, and discuss alternative approaches like media mix modeling and incrementality testing (e.g., geo lift). The conversation covers how privacy changes (like Safari blocking third-party trackers) and cross-device behavior make deterministic attribution fragile, while black-box platforms and incentives to hit low CPA/ROAS can distort results. They discuss common gaming and fraud tactics such as ID bridging, affiliate and click manipulation, and bottom-funnel retargeting that captures outsized credit, leading to budget misallocation. Practical advice includes defining measurement strategy first, testing vendors, building in-house measurement protocols, and using transparency and independent verification to evaluate ad tech. 00:00 Welcome and Guest Intro 01:51 What Attribution Means 03:27 View Through vs Click Through 04:16 Pixels and Tracking Basics 05:43 Other Attribution Methods 07:50 When Attribution Breaks 08:59 Incrementality and MMM 11:50 Why Deterministic Misleads 14:39 Easy Button Incentives 18:42 Privacy and ID Limits 22:17 ID Bridging and Fraud 28:08 Retargeting Reality Check 31:42 CFO and Investor Playbook 33:36 Transparency and Vetting 35:26 Future of Measurement 37:03 Wrap Up and Where to Find Eric This podcast is powered by Pinecast (https://pinecast.com).

8. 

   23 Jul 2026

   S1E2 - The Google's Gamble: Liability and Control in AI-Driven Advertising

   Jeromy, Bogdan and Lee discuss Google integrating AI more deeply into its ad campaign workflow while changing terms so advertisers are liable for AI-generated mistakes, arguing Google gains control without responsibility and predicting potential lawsuits and backlash, especially from professional advertisers. They explore why Google may be pushing this, including internal incentives to grow YouTube advertiser counts, and argue better measurement tools for incremental lift would drive adoption more sustainably. The conversation shifts to an Australian dock workers’ union demanding a 28-hour workweek as automation and AI increase productivity, questioning whether claimed gains are real and where they actually occur. They then cover a Texas Tribune investigation into Texas data centers using permitting tactics for turbines and diesel generators, noting emissions tied to gas plants serving data centers and debating hype versus reality of build-outs. Finally, they discuss Apple reportedly considering acquiring Prism ML to run a compressed 27B-parameter model locally on iPhones, emphasizing privacy and potential market shifts if Apple executes. 00:00 Welcome and Introductions 01:08 Google Ads AI Liability 06:27 Why Google Pushes YouTube 09:20 Measuring CTV Lift Properly 12:09 Australia 28 Hour Workweek 13:14 AI Productivity Reality Check 21:28 Texas Data Centers Emissions 27:59 North Korea Linux Tangent 30:42 Apple Local AI Comeback 37:00 Wrap Up and Subscribe This podcast is powered by Pinecast (https://pinecast.com).

9. 

   15 Jul 2026

   S1E1 - Inside the Phia Affiliate Fraud Scandal: An Interview with Ben Edelman

   Episode Notes Ben Edelman on Phia’s Alleged Forced Clicks: How Shopping Plugins Commit Affiliate Fraud In a Deeper Diligence guest episode, ad-fraud researcher Ben Edelman discusses his investigation into Phia and the Bloomberg coverage alleging affiliate fraud through “forced clicks” and standdown-rule violations. Edelman explains the three-step affiliate marketing bargain (show link, user clicks, user buys) and how forced clicks skip the user click by using Phia’s iOS plugin to open an invisible tab that loads an affiliate link and closes it, positioning Phia as “last click” for commission—often costing merchants, and sometimes other affiliates like review publishers. He also describes standdown rules requiring shopping plugins to stay out of the way when another affiliate referred the user, and says Phia tracked competitor affiliate links yet did not stand down. Edelman questions Phia’s claim this behavior was a bug, cites past cases including eBay prosecutions and Honey-related evidence, and argues enforcement can deter fraud. 00:00 Welcome and Guest Intro 01:14 Ben Edelman Background 01:40 Why Phia Drew Attention 03:18 Cookie Stuffing vs Forced Clicks 04:14 Affiliate Marketing Basics 07:21 Networks and Incentives 09:40 Phia Forced Clicks Explained 12:34 Stand Down Rules Violations 14:31 Real World Harm Examples 17:09 Bug Claim and Intent 21:27 Past Major Fraud Cases 23:27 Honey Investigation Lessons 24:55 Stopping Fraud Systemically 27:02 Future Misconduct and Wrap Up 28:11 Closing Thanks and Contact This podcast is powered by Pinecast (https://pinecast.com).

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