In this episode of the Paid Search NYC Podcast, host Matt Shenton is joined by Navah Hopkins from Microsoft to break down what Copilot actually is, how it works, and what it means for marketers navigating an AI-driven search and discovery landscape.We go from high-level Copilot use cases and model selection, through to what AI shopping journeys look like in practice, why feed hygiene now matters more than ever, and how Microsoft is bringing Copilot into ad creation and campaign workflows.If you’re trying to understand how AI assistants are changing discovery, commerce, and paid media execution, this is a very practical place to start.🔑 Key Insights from the Episode- What Copilot is — and what it isn’t- The difference between Smart, Think Deeper, Study & Learn, and Search- How to think about model choice depending on the task- Why thread memory matters differently across Copilot modes- How AI assistants move from discovery to transaction in one journey- Why feed hygiene and crawlability are critical for AI commerce- What happens when product feeds aren’t accessible to AI systems- Why brand still matters in upper-funnel AI discovery- How ads show up inside Copilot — and when they don’t- What Microsoft Ads Copilot tools can do across creative and campaign workflows- How Ad Studio helps with brand-safe creative generation- Why AI should be treated as support for marketers, not a replacement⏱ Key Sections & Timestamps00:23 – Intro01:37 – What Copilot is and why it matters04:21 – Returning to threads and how Copilot memory works07:39 – Smart vs Think Deeper vs Study & Learn vs Search09:49 – Which Copilot modes work best with longer threads11:02 – Testing Copilot with a real prompt12:39 – Organic results, product discovery, and early shopping intent16:23 – Why these results are still organic20:13 – Discovery journeys, upper funnel behavior, and advertiser implications21:22 – Why brand and feed hygiene matter more than ever27:59 – Why some shopping results are organic, not ads29:26 – Where transactional intent changes the experience31:17 – What ad inventory can show inside Copilot32:01 – Reporting and measurement limitations today33:34 – Copilot Labs and creative use cases36:15 – Ad Studio, brand kits, and AI-assisted creative workflows40:19 – Using Copilot inside PMAX and campaign creation43:37 – Rewriting messaging and creative with Copilot44:25 – Different levels of control for different marketer workflows45:42 – Final thoughts: AI in service of humanity47:19 – Outro

Most conversations around Google Ads automation focus on tools.This episode is about the system behind it.In this conversation with Nils Rooijmans, we break down how to automate e-commerce Google Ads using scripts, AI and SOPs — and why most advertisers are still thinking about automation the wrong way.We cover how to structure your accounts, where automation actually adds value, and how to combine scripts with AI to scale performance without scaling headcount.Key Insights:- Why most Google Ads automation focuses on the wrong things- How to turn PPC into a system using SOPs- The role of scripts vs AI in modern PPC workflows- How scripts handle 60–80% of account work- Why Performance Max often optimizes for spend, not profit- The “exploration vs exploitation” approach to scaling- How to structure Standard Shopping campaigns for control- Using scripts for monitoring, alerts and decision support- Why AI should be treated like an “intern”- How to scale accounts without scaling teamsTimestamps:00:00 Scripts vs agents00:31 Introduction01:31 How Nils built a script-driven PPC agency03:19 Why you do not need coding skills to use scripts04:24 What the team actually does in a script-led agency08:25 Why SOPs matter before AI10:20 What Google Ads scripts actually are12:54 E-commerce Google Ads automation examples14:38 Profit-first conversion tracking17:07 Using return-rate prediction in e-commerce19:05 Feed optimisation and why Nils pushes back on Performance Max22:07 The standard shopping gatekeeper structure25:52 Exploration vs exploitation in Google Ads27:11 Bidding strategy within the gatekeeper setup29:18 Are scripts agentic?32:01 Why the future is scripts + AI33:31 Final thoughts

In this episode of the Paid Search NYC Podcast, host Matt Shenton is joined by Stewart Dunlop (Founder, PPC.io) to break down what AI is actually good at today - and where it still falls short.We go beyond the hype into real workflows: how to use tools like ChatGPT and Claude Code effectively, why context is the most important input, and how marketers can start building repeatable systems using AI - not just one-off prompts.Stewart also shares a behind-the-scenes look at building PPC.io - a platform designed to run AI agents across paid media accounts — and what it takes to turn AI from a novelty into something genuinely useful.🔑 Key Insights from the Episode- Why most AI outputs are weak (and how to fix it with better context)- The difference between chat-based AI and agent-based workflows- How tools like Claude Code enable faster, more scalable execution- Why “skills” and structured instructions outperform one-off prompts- How to turn your SOPs into repeatable AI workflows- Where AI adds the most value (and where it still needs human input)- Why documentation is the biggest unlock for using AI effectively- How agencies can use AI to scale work across multiple clients- The concept of “roundtable agents” and stress-testing decisions- Why execution is becoming table stakes — and where real value shifts⏱ Key Sections & Timestamps00:00 – Intro01:30 – AI hype vs reality: where it actually works05:00 – Why context is everything in AI prompting08:30 – Improving outputs with better inputs12:00 – Chat AI vs agent workflows explained16:00 – Introduction to Claude Code20:30 – Building workflows inside VS Code26:00 – Using AI for landing pages and reporting32:00 – Where AI still falls short36:00 – The importance of documentation and SOPs40:00 – From workflows to agents: the next step 45:00 – Building PPC.io (behind the scenes)50:00 – Testing agents, prompts, and models55:00 – Multi-agent systems and “roundtable” decision making59:00 – Final thoughts on AI in marketing

In this episode of the Paid Search NYC Podcast, host Matt Shenton is joined by Edwin Choi, Founder of Jet Fuel Agency, to break down one of the biggest challenges in paid social today: creative fatigue - and how Meta’s evolving algorithm (including Andromeda) is changing the rules.

We go deep into how high-performing teams are approaching creative at scale — from forecasting creative demand and measuring fatigue, to building systems that reduce manual work and unlock better decision-making. Edwin also shares how AI is reshaping research, analysis, and execution — and what skills actually matter in modern performance marketing teams.

🔑 Key Insights from the Episode

Why “execution is table stakes” in the age of AI

How top teams forecast creative demand based on budget, decay rates, and win rates

What creative fatigue actually is — and how Meta measures and penalizes it

The concept of “ad concentration risk” and why it can quietly kill performance

How to measure fatigue using frequency, CTR decay, and performance trends

Why creative diversity (not just volume) is critical under Meta’s Andromeda update

How Meta now pre-processes and categorizes ads before they even enter auction

The shift from audience targeting → creative-led targetingHow to structure ads around personas, hooks, and emotional drivers

What “mental agency” is — and why it’s the most important hiring trait today

⏱ Key Sections & Timestamps

00:00 – Intro

00:20 – Edwin’s background & building Jet Fuel Agency

02:30 – Lessons from hiring (and firing) agencies

05:00 – Why execution is now table stakes

06:00 – The 3 AI buckets: research, analysis, execution

11:00 – Hiring for “mental agency”

15:30 – What is creative fatigue?

18:50 – How to measure fatigue (frequency, decay, trends)

21:30 – Forecasting creative demand from data

24:40 – Benchmarking and improving mature accounts

26:50 – Creative-led targeting & personas

31:00 – Meta’s Andromeda update explained

33:30 – Why ads fail before they even launch

34:40 – Ensuring creative diversity at scale

38:00 – Final takeaways

In this in-person episode of the Paid Search NYC podcast, we break down what the latest data from a 21 million–SERP study reveals about AI Overviews in Google Search - and why it’s already having a measurable impact on paid search performance, brand campaigns, and click behavior across retail, travel, finance, healthcare, and automotive, alongside a look at how ad fraud and brand impersonation are evolving in an AI-driven search world.

We’re joined by Phillip Thune (CEO, Adthena) and Shlomi Beer (CEO, Impersonally) to unpack large-scale SERP trends and examine the changing nature of ad fraud, phishing, and brand abuse as AI reshapes search experiences. From CTR declines to brand safety risks, this episode connects the dots between performance, visibility, and trust in modern search.

Key insights covered

- AI Overviews are appearing more frequently across industries - including shorter, higher-volume queries

- When AI Overviews appear above ads, paid search CTR can drop 20–40%

- Brand search is no longer “safe” - AI Overviews now appear on ~20% of branded queries

- AI Overviews are accelerating funnel compression, especially on long-tail searches

- Retail, finance, healthcare, travel, and automotive show very different AI Overview behavior

- Ad fraud is expanding beyond affiliate abuse into impersonation, phishing, and fake support ads

- AI tools make it easier for bad actors to create realistic ads and fake websites at scale

- Fraud on brand terms can directly inflate CPCs and waste paid search budget

- Early signs show ads starting to appear inside AI Overviews, raising new measurement questions

- Visibility and monitoring are becoming as important as bidding strategy in paid search

Topics & timestamps

00:36 – Introduction & episode overview

01:45 – Reviewing the original large-scale AI Overview study

03:45 – Why AI Overviews reduce paid search CTR by 20–40%

05:29 – Key takeaways from the latest AI Overview data

06:14 – AI Overviews appearing on shorter search queries

08:12 – Industry differences: retail, finance, healthcare, travel & automotive

10:23 – Why AI Overview prevalence fluctuates week to week

12:16 – Retail search behavior & funnel position

18:28 – Monetizing conversational search & funnel compression

19:16 – Brand vs non-brand search: what’s changed

24:34 – How advertisers should respond to AI Overview volatility

27:16 – Pivot to brand protection & ad fraud trends

29:31 – Impersonation, phishing & fake support ads explained

33:31 – Real-world examples of large-scale Google Ads fraud

37:12 – SEO poisoning, AI content & trust issues

44:47 – Ads inside AI Overviews: what we’re starting to see

49:51 – Will AI Overviews become a new ad inventory?

54:14 – Final thoughts: visibility, trust & what comes next

In this episode of the Paid Search NYC Podcast, I’m joined by Alexandre Airvault, CEO of Blobr.io, to break down how AI agents are being used today to automate and optimize Google Ads - beyond scripts and rules.We go deep into real-world use cases: AI-driven root cause analysis, agent-based optimization for keywords and ad copy, and how these systems are changing the day-to-day role of paid search practitioners - shifting time away from manual work and toward strategy, creativity, and confidence in decision-making.🔑 Key Insights from the Episode- How AI agents differ from traditional Google Ads scripts- Using AI for root cause analysis (the why behind performance changes)- How agents connect account changes with KPI movements automatically- Why “deterministic rules” break down at scale in modern PPC accounts- Real examples of AI agents for negative keywords, ad copy, and extensions- How contextual signals (volume, intent, materiality) shape agent decisions- The role of human judgment vs automation in AI-driven PPC workflows- Why confidence in decision-making may be AI’s biggest hidden benefit- How paid search roles are evolving toward strategy and creativity⏱ Key Sections & Timestamps00:00 – Intro00:20 – Episode start & Alexandre’s background02:30 – What Blobr does & the root cause analysis problem04:50 – From automation to AI: Blobr’s pivot09:50 – How root cause analysis works in practice16:20 – Handling multiple changes & noisy PPC data21:20 – Structuring insights for different stakeholder levels25:20 – Introducing AI agents in Google Ads29:05 – Agent categories: ads, keywords, extensions31:50 – Negative keyword agent (real example)36:20 – AI agents vs Google Ads scripts39:20 – How AI changes the paid search role41:20 – Final thoughts & where to learn more

Chris Chambers is the Head of Paid Search at Understory, a B2B marketing agency leading the way in AI-driven automation. He’s also one of the most active voices on LinkedIn showing real examples of how ChatGPT-5, Agent Mode, and Custom GPTs are reshaping PPC workflows.

In this episode of Paid Search NYC, we explore:

⚙️ What Agent Mode is - and how it actually takes action across apps and Google Ads

📊 Automating campaign tasks like budget pacing, reporting, and KPI alerts

📈 How to build repeatable GPT “projects” that act like virtual assistants

🔄 Connecting ChatGPT to Slack, Gmail, and Google Ads for real-time automations

🧩 Why context is the most important variable in getting accurate, useful outputs

🚀 The rise of Custom GPTs - and how to find or build ones for paid search

If you’re a PPC marketer, agency founder, or curious about where automation and AI are heading, this conversation will show you how to turn ChatGPT from a chat tool into an actual operator.

Andy Chadwick is the co-founder of Keyword Insights, an SEO SaaS platform, and an agency owner helping brands build topical authority.

He’s also an international speaker (MozCon, Search & Stuff) and one of the sharpest voices in the SEO industry today.

In this episode of Paid Search NYC, we explore:

📊 Topical authority: what it means and how Google’s May 2024 leak confirmed its importance

🧩 The evolution of Keyword Insights from keyword clustering tool to full content suite

🤖 How AI Overviews & LLMs are reshaping SEO strategies in 2025⚡ Why AI rewards consensus over originality — and what that means for content creators

🧠 Andy’s concept of “inception marketing”: getting your brand into ChatGPT’s memory

🔄 Why SEO fundamentals (links, content, UX) still matter more than hacks

If you’re an SEO professional, content strategist, or just curious about how AI is changing search, this deep dive with Andy will give you actionable insights and a glimpse into the future of digital marketing.

📌 Chapters:

0:00 Intro

1:20 Andy’s journey: agency, SaaS & Keyword Insights

6:00 How keyword clustering evolved into topical authority

10:00 Google’s May 2024 leak & why it matters

14:00 Where to draw the line on content tangents

18:00 Vector embeddings & semantic search explained

22:00 SEO fundamentals vs AI consensus

28:00 Personalization in LLMs & audience targeting

30:00 “Inception marketing” & the future of SEO tactics

32:00 Andy on TikTok & experimenting with new channels

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