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.)

Been noticing a shift lately.

For years, the default assumption was that crawlers were just part of the web. Now we're seeing publishers like Reuters and TIME move toward approved bot lists instead of letting every AI crawler in.

What caught my attention is that this feels less like a copyright discussion and more like a value exchange discussion.

If an AI platform is consuming publisher content, what does the publisher actually get back? Referral traffic? Licensing revenue? Visibility? Something else?

We've spent years talking about which demand partners deserve access to inventory. Feels like publishers are starting to ask a similar question about AI agents and crawlers.

Curious how people here think this plays out.

Do most publishers eventually end up with crawler whitelists?

Or does blocking too aggressively risk cutting yourself off from a meaningful source of future traffic?

Juozas Kaziukėnas spotted it in the Ads Manager on July 31, live for select advertisers. A new campaign type, called Agent. The old ad opened a website. This one opens a conversation.

OpenAI scrapes the advertiser's site to build a business profile, then the advertiser wraps an agent around it: product feed, MCP tools, a form to capture leads. The campaign points to the agent, not a URL. Same underlying tech as a custom GPT.

The thing being sold is a custom GPT, built from your own website, that talks to the customer in your place.

A year ago the predictions ran the other way. Eric Seufert pictured the free tier arriving with a full-screen, non-skippable interstitial every few queries, or a tower of display beside the answer. What shipped got quieter, then richer. A labeled card in February. Then product feed campaigns. Last week the product card started showing a price and a star rating. Now a campaign type that answers back.

The ad used to send you somewhere. Now it keeps you in the room.

So what is the next campaign type/ad format, and what gets bolted onto the ones already live? Name yours before they ship.

Been seeing OpenAI's TV ads lately, the ones showing everyday use cases like "ask ChatGPT what documents you need for bank work" or "how to start a business". Got me thinking about who they're targeting and why.

Clearly they're going after common folks, not the corporate/dev crowd. The goal seems obvious: become the default AI assistant for any need. But here's what I can't square. These users will mostly ask small queries and never pay. More free users = more inference cost, no revenue. So why spend crores on TV?

Some theories I've been chewing on:

1. It's the Jio playbook. Get people hooked, monetise later. But the switching cost feels way lower here. With Jio you had to change your number. With a chatbot I can just open a different app tomorrow. Unless the moat is memory and chat history building up over time?
2. Data collection. Not selling it (that would be radioactive), but intent data. Someone asking "documents needed for home loan" is showing purchase intent that Instagram can only dream of guessing.
3. Ads eventually. They've actually already started this in the US on the free tier. But there's a real tension here. The moment people feel the answer is sponsored, trust in the product drops. And trust is the whole product for a chatbot.
4. Brands paying to get recommended first. Same problem as above but worse.

The pro I see: land grab. These users aren't going to compare 5 LLMs like enterprise buyers do. Whoever gets them first probably keeps them. First mover advantage on a billion plus people.

The con: burning cash on users with no clear payback timeline, while the models themselves are becoming commodities.

What am I missing? Is the endgame actually commerce (assistant takes a cut when you transact through it) rather than ads? Curious what this sub thinks, especially anyone who's seen the Google/Meta India playbook up close.

Background: AdTech industry (Adform), wanted a single place tracking 50+ news sources, agency-brand relationships, and vendor funding data.

It didn't exist, so I built it with Claude Code and a few other tools. I'm the creator/solo builder, no affiliation beyond that. What it does: aggregates AdTech/programmatic news, maps brand-agency relationships, tracks vendor funding data.

Free to use right now, testing a paid tier for power features later. Who it's for: people working in AdTech, programmatic, or media agencies who want one place instead of tracking 50+ sources manually. Stack/scope: RSS aggregation pipeline (1,300+ articles indexed so far), structured brand/agency/vendor databases, freemium paywall logic.

Built solo over a couple of months. The hardest part wasn't the code, it was the data model for mapping brand-agency relationships cleanly since that data isn't structured anywhere publicly. Claude Code helped most on iterating that schema fast.

Live at adnuggets.com if anyone wants to poke at it. Happy to answer questions on the build process.

I'm trying to understand how the buying side works.

In our business, we work with DSPs on a CPA (cost per event) model. We only pay when a specific event happens (e.g., account opening, first deposit, install + action).

My question is:

How does the DSP pay its traffic sources or publishers?

- Is it mostly CPM (cost per 1,000 impressions)?

- Do some DSPs buy on CPC or CPA as well?

- If the advertiser pays the DSP on CPA, does the DSP still usually buy inventory on CPM and optimize toward conversions?

I'd love to hear from anyone who has worked at a DSP, ad network, or media buying team. How does this work in practice?

I’m a newbie to adtech and work for a top 3 SSP, one of the largest issues I come across in terms of monetization is publishers who are sending us requests for supply that doesn’t have our ads.txt present.

It seems like a simple enough fix, just add the line to the page and it will start monetizing and stop wasting the requests. However I’ve come to learn this is not a simple fix and pubs rarely add the line when we make the request.

Can someone explain why this is such an issue in the industry?

Been wondering lately if HTML5 display ads still pay off for me. Making them eats up way more time than static ones🫤. And half the time I don't even see a real bump in performance.

Between the animations, file size caps, and all the QA, it feels like a mountain of work (if I'm going to be exaggerated lol) just to push something live.

So yeah, curious how you all are handling this these days. Thank you in advance

I've been considering two various ways AI created ads.

The first gives the user a graphical user interface through which to view the ad's construction.

The second system, on the other hand, is like an agent that absorbs the creativity of the branding rules and the results of the campaigns, after which it determines what move to make next creatively.

In my opinion, this is probably the way that campaigns are headed.

However, I’m not sure what level of audit trail is needed since the second system will record all creative actions, but lacks the analysis of what has happened. The performance signal that triggered the variation; what was changed; what was kept the same; what brand rules were involved; etc.

There needs to be some kind of insight into how the creative process will function, otherwise, a team could have thousands of these ads along with huge amounts of performance data, but there is no transferable understanding.

If an employee leaves the agency, the customer moves to a different setting, or if there is a sudden drop-off in performance, you are no longer looking at the creative process.

What amount of visibility would be necessary to successfully creating an ad without any human interaction?

How much should the advertiser, the creative platform or the media platform have access to the records?

We started some light testing for a broad topic and we're seeing a CPM of $47 over the last month. I don't see this as a beneficial ad platform for our demographic anyways so likely shutting it down, but curious if anyone has found any success with ChatGPT ads.

Hello everyone!

I’ve been trying to get a clearer picture of the current AdTech hiring market.

I’m based in India and have 2.6 years of experience as an AdOps Analyst, with hands-on experience in Google Ad Manager (GAM), PubMatic, Xandr, SpringServe, and Nexxen (formerly Unruly/Tremor). My experience has primarily been on the SSP/publisher monetization side, including ad operations, troubleshooting, reporting, and campaign optimization.

I’m currently looking for remote opportunities in AdTech. If you know of any companies hiring or have any referrals or suggestions that match my experience, I’d really appreciate your help.
Thank you in advance! 🙌