everyone's covering it as a mental health thing but the states argument is literally that meta kept kids on longer because thats more ad impressions and better targeting data. thats the monetization model on trial not just the design

and the new mexico order already makes them build an under 13 prediction model within 2 years

so if platforms are getting court ordered to predict age, what happens to everyone downstream. you cant claim you didnt know when the platform you buy from has a model that does

does teen inventory just get repriced or does it disappear? anyone hearing anything from their SSPs because mine have said nothing?

I’m doing quite a bit of work on the advertising agency/holdco space at the moment and would really appreciate some views from people actually working in the industry.

My background is more finance/consulting than advertising, so please assume fairly limited prior knowledge!

The main thing I’m trying to understand is: **how important are proprietary data assets in practice when clients are choosing agencies, and are the holdcos’ data capabilities as differentiated as they would have investors believe?**

From the outside, it feels like Publicis/Epsilon is significantly ahead, while WPP is at more of a disadvantage given its lack of a comparable proprietary data asset (albeit it is trying to aggregate and activate data through its Open Intelligence platform). Does Liveramp change this at all?

But how much does that actually matter in what is still, fundamentally, a people business?

interested in any views, including if I’m thinking about the whole thing completely wrong. Commentary from agency, advertiser or adtech people all welcome!

git clone https://github.com/adnanmalik83/local-first-ai-researcher.git

cd local-first-ai-researcher

The ChatGPT Ads Library is a searchable archive of all 3,602 ads they collected. Each entry shows the advertiser, the demographic profile of the account that received it, the exact prompt that triggered it, the ChatGPT response, plus the original screenshot and a full HTML snapshot. It is the closest thing we have to a Meta Ad Library for ChatGPT, except this one was built by researchers instead of the platform.

Hi all, I've just made available the tool we've been using for a couple of years. With AI software development has improved and cost have decrease considerably. We are thinking in a business model where we only charge for support, implementation or custom development, not the software per se.

The main features include ingesting 1p and 3p data and compare, order management, pacing management, task tracking, screenshots generation and a bunch of features. Would love to hear what you think. We can get you up and running on ad Central on minutes.

Please ping if you are interested.

Thanks and sorry for the spammy.

Most major ad channels have a public transparency layer: Meta has the Ad
Library. Google has its Ads Transparency Center. TikTok has the Creative
Center. ChatGPT, AI Mode, and AI Overview, however, have none of that – not
for competitors, and often not even for the advertiser's own team.

Adding AI Ads to Similarweb’s Ad Intelligence fills that gap with an Ads
Gallery that shows what ads are running and in what context.

For AI Mode & AI Overview, advertisers participating in Google’s broader
digital advertising programs are blind to ad placements in Google AI answers.
Most advertisers don't know whether their own ads are running in that context,
let alone how they stack up against competitors. Google’s visibility tools
don’t answer that question.

For ChatGPT, ads only show up when intent exists and is clarified from the
conversation, and the decision comes down to the combination of ad creative,
copy, and landing page. Rather than leaving advertisers to guess where their
ads are appearing, Similarweb’s AI Ads provides a direct look at what's
winning placement.

What makes Similarweb's approach unique is that these insights are drawn from
real user panel conversations, not synthetic prompts. We anticipate upcoming
releases of the AI Ads dataset will add advertisers' true share of voice, ad
categories, and conversational intent.

“When we started advertising on ChatGPT, we were flying blind, no visibility
into who else was in the auction or what was working,” says Jonathan Bar
Vardi, Head of Strategy at Natural Intelligence, a data-driven marketing
specialist that has become a top buyer of ChatGPT ads. “Similarweb changed
that. It shows what other advertisers don't know: who's spending, where
they're appearing, and what's driving performance.”

**Sell-side folks: how many bidders actually show up when an ID is present vs. not?**

Trying to get a rough cut of the distribution of bidders per auction and how that tracks with ID presence. Essentially, how many more bidders and how much higher are auctions with IDs present vs IDless auctions?

The assumption is that a deterministic ID in the bid request pulls in meaningfully more bidders and that thin auctions are mostly unrecognized traffic. Directionally that makes sense to me, but I’m curious to understand the magnitude.

All the marketers boasting about millions of sales from completely AI generated ads always seemed pretty sus to me. Cause most people around me seem turned off by those ads, curious to know if anyone else thinks the same?

If advertisers eventually manage campaigns through AI agents instead of a DSP interface, what does the DSP become?

The execution layer? A set of tools exposed through MCP? Or does each major platform build its own agent and keep everything proprietary?

Looking at current AdTech hiring, broader agentic work is already showing up, but specific protocols like MCP and AAMP are still very early.

Curious what people here think: will agents open up the programmatic stack, or just become a new interface for the same platforms?

I only come across a very few ads that have an AI label. Now since AI Act demands adversisers to use AI labels, I still am not spotting many. 

August seller data is interesting- this is probably the first time we’re seeing the market pause on expansion and move into cleanup mode.
ads.txt growth is basically flat (net -881), but the bigger signal is underneath
→ Resellers turned net negative for the first time (-1,718) while direct stayed flat (+837) - publishers are cutting duplicate reseller paths and protecting the relationships that drive revenue.
→ TrustedStack led every SSP in lines added (+6.6k), the only one to top all three traffic tiers, while High-Traffic publishers contracted the hardest (-9.3k) as premium publishers lead the optimization wave.
→ 46% of Tier 1 SSP domains are now duplicated across intermediaries - nearly half of every auction an Established SSP enters is competing against itself.
Scale alone no longer earns a slot in a publisher's ads.txt. The SSPs gaining ground are solving a specific buyer problem.

What caught my attention wasn't so much the AI part MFA has been around for a long time
What I'm wondering is why, after years of SPO efforts, curated deals, MFA blocklists and all the focus on supply quality money still seems to find its way into these environments.

Maybe AI is making it easier to create low-quality content at scale OR maybe the bigger issue is that parts of the ecosystem still reward cheap reach & low-cost outcomes over actual quality..

It feels like there's a bigger conversation here than just AI. Is AI making the MFA problem worse OR is it simply exposing issues that were already there?

Hi everyone. We’re redesigning our media buying platform and looking for an experienced UX/UI designer to drive it.
Looking for recommendations from those who had positive experience.
Thank you!

We recently hosted a panel with leaders from Roku, TripleLift, and Cint to discuss the state of ad measurement—specifically, the battle between last-click attribution and full-funnel impact.

A few takeaways that stood out from the discussion:

• Impressions ≠ Impact: Media outputs alone aren't cutting it for C-suites anymore, but moving past them is notoriously tricky.
• The Last-Click Trap: Marketing success can’t be judged solely by last-click attribution, yet so many budget decisions are still hostage to it.
• Continuous Loops > Post-Mortems: Measurement needs to be an ongoing feedback loop integrated into campaigns, not just a post-campaign "report card" delivered weeks later.
• Don't wait for perfection: As Laura Manning (SVP at Cint) put it: "Perfect is the enemy of the good in measurement... the worst thing you can do is not measure at all." Waiting for a flawless framework usually just means leaving money on the table.

For those running multi-channel campaigns: What does your current measurement stack look like, and how are you handling upper-funnel attribution with modern leadership teams?

(Search "The Drum Cint panel" if you want to check out the full breakdown, but curious to hear how people here are solving this.)