u/u_of_digital

Registration: https://www.adexchanger.com/go/programmatic-ai/

It shows up everywhere in AI conversations, and the meaning can feel a bit fuzzy. A clearer way to think about it: design systems where humans step in at the right moments.

That might look like:
→ Letting AI optimize bids continuously
→ But flagging any major shift (ex: ±50%) for review
→ Letting AI manage campaign pacing in real time
→ But routing material budget changes to a human approval layer

Not providing constant oversight. Just smart guardrails. It’s learning how to design systems where humans and AI work together intentionally, with the right balance of speed, accountability, and oversight.

Sometimes, you need to "graduate" from unconstrained automation and put your hands back on the wheel. Check out those cases.

OpenAI hiring TTD’s chief strategy officer as VP of partnerships for monetization is a pretty loud statement about where it wants to sit in the ads ecosystem: not as “a cool new channel,” but as a platform that expects proper demand, distribution, and infra deals, at TTD scale.

At the same time, OpenAI is stacking the commercial bench with people like David Dugan, talking very openly about a partner-led, agency-friendly, AI‑first ad product.

Jacobson keeping her board seat while TTD scrambles for a successor just underlines how strategic she is to both sides. But it lands very differently once you add a CMO exit in April, a CFO gone after six months, and three directors resigning in the same timeline.

On the surface, it’s simple: you can now use LinkedIn signals like job title, industry, and seniority to reach B2B viewers on streaming TV through Amazon, via Microsoft’s Monetize SSP. LinkedIn and Amazon posts are already full of “B2B CTV breakthrough” talk, with people fantasizing about targeting CFOs on their smart TVs. But once you look at the fine print, it’s US‑only, CTV‑only, managed accounts, high minimum spend, and limited to three audience fields – none of the richer stuff like company size, skills, or matched lists.

CTV is turning into the meeting point for retail media, identity, AI, and programmatic infrastructure. And Amazon absolutely feels what’s coming, especially now that LinkedIn already wired similar data into The Trade Desk earlier this year for CTV. 

Microsoft owns LinkedIn and Microsoft Monetize, Amazon DSP rides that supply the path, and suddenly, you’ve got one chain that touches the audience, the pipes, and the screen. That’s where this starts to look less like a neat data partnership and more like infrastructure consolidation.

Very few players can mix purchase data, streaming inventory, DSP, identity, AI, and cloud at a global scale. Amazon can, and that part matters more than any shiny news about targeting.

I’m researching how different AI assistants handle shopping flows, and I’m curious about Baidu’s Ernie Bot specifically.

I’m wondering if Ernie Bot’s chat interface has anything similar:

• Does Ernie Bot show product cards with images and multi‑merchant pricing directly in the chat?
• Or is it still mostly text answers + links, with shopping handled by regular Baidu Search ads / shopping blocks outside the chat?
• If you’ve used it for real shopping queries (e.g., phones, home appliances, etc.), what did the UI actually look like?

Screenshots or detailed descriptions would be super helpful. 

Thank you!

On the other side, AI search engines are rolling out shopping research and instant checkouts, trying to compress the whole funnel into one conversation. But people in ecommerce circles are already saying, the LLMs are just rearranging that search layer, not replacing social and influencers.

 I think the agentic shopping problem is fundamentally a data problem, not a UX problem. General AI assistants (horizontal, third-party) consistently fail at basic requirements like showing accurate prices or confirming product availability. They're missing purchase history and user preferences entirely.

Horizontal general-purpose assistants ( ChatGPT, Claude, Gemini) work by scraping whatever they can find online. That approach means working with stale data, inconsistent formatting, and no guarantee that what they're telling you matches current reality.

Vertical commerce platforms (Shopify, Criteo, Instacart) run on structured feeds that sellers update directly in real-time. The one solution is catalog-powered AI search.

Who’s involved?
OpenAI, retailers, and ecommerce brands, plus Criteo as the first adtech partner piloting this with retailers that juggle thousands of SKUs.

What happened?
Retailers used to upload catalogs into ChatGPT just to power pricing and availability, but not ads, so campaigns had to be built product by product. Now OpenAI’s product feed turns names, images, and attributes into ad units automatically, which cuts a lot of creative grunt work for large catalogs. The system can handle up to around a million SKUs per advertiser, with controls for which products are allowed to show, and the structure lines up with Google Shopping feeds, so most brands can reuse what they already have.

Food for thought
OpenAI is stepping into a different lane than Google, Meta, or Amazon here: ChatGPT surfaces these ads from conversational intent inside the chat, rather than leaning on search history, social signals, or browsing behavior.

Slowly but surely, AI is turning the internet into one big, always‑on mall...

Amazon is teaming up with Shopsense on a new content‑to‑commerce partnership, and the interesting bit isn’t just ‘AI ads.’ It’s that this setup turns a lot of regular content into a kind of stealth storefront plugged into Amazon’s pipes. Publishers can use Shopsense through Amazon’s ad stack so that articles, images, and clips get scanned, recognized, and instantly turned into product listing ads that match the look and feel of the page. And the agents under the hood decide what to show, sometimes even pushing higher‑ticket items when they see people actually engaging with them.

What makes this stand out is the broader pattern:

The Trade Desk already teamed with Shopsense to make more of the open web shoppable. YouTube’s already talking about this kind of AI‑driven shopping layer, but they keep dodging any real timeline, which kind of tells you they know it’s a sensitive move. While many social platforms are trying to sneak a mall into your feed. Meanwhile, users keep saying they’re exhausted by feeds that feel like nonstop shopping, and there’s growing skepticism about AI-heavy ad experiences.

So we’re kind of stress-testing a line here: at what point does “relevant commerce” just become background noise people tune out?

If you were running a mid-sized publisher, would you flip this on for the extra revenue or hold back to protect the reading experience?

Go down the rabbit hole:
https://stratechery.com/2025/an-interview-with-youtube-ceo-neal-mohan-about-building-a-stage-for-creators/#monetization
https://www.adweek.com/programmatic/the-trade-desk-shopsense-ai-shoppable-ad-inventory/
https://www.thetradedesk.com/press-room/the-trade-desk-taps-shopsense-ai-to-turn-more-of-the-open-web-into-shoppable-ad-inventory
https://www.ces.tech/schedule/content-to-commerce-the-world-of-social-shopping/
https://www.mediapost.com/publications/article/414950/amazon-brings-content-to-commerce-technology-into.html

Amazon is teaming up with Shopsense on a new content‑to‑commerce partnership, and the interesting bit isn’t just ‘AI ads.’ It’s that this setup turns a lot of regular content into a kind of stealth storefront plugged into Amazon’s pipes. Publishers can use Shopsense through Amazon’s ad stack so that articles, images, and clips get scanned, recognized, and instantly turned into product listing ads that match the look and feel of the page. And the agents under the hood decide what to show, sometimes even pushing higher‑ticket items when they see people actually engaging with them.

What makes this stand out is the broader pattern:

The Trade Desk already teamed with Shopsense to make more of the open web shoppable. YouTube’s already talking about this kind of AI‑driven shopping layer, but they keep dodging any real timeline, which kind of tells you they know it’s a sensitive move. While many social platforms are trying to sneak a mall into your feed. Meanwhile, users keep saying they’re exhausted by feeds that feel like nonstop shopping, and there’s growing skepticism about AI-heavy ad experiences.

So we’re kind of stress-testing a line here: at what point does “relevant commerce” just become background noise people tune out?

If you were running a mid-sized publisher, would you flip this on for the extra revenue or hold back to protect the reading experience?

Go down the rabbit hole:
https://stratechery.com/2025/an-interview-with-youtube-ceo-neal-mohan-about-building-a-stage-for-creators/#monetization
https://www.adweek.com/programmatic/the-trade-desk-shopsense-ai-shoppable-ad-inventory/
https://www.thetradedesk.com/press-room/the-trade-desk-taps-shopsense-ai-to-turn-more-of-the-open-web-into-shoppable-ad-inventory
https://www.ces.tech/schedule/content-to-commerce-the-world-of-social-shopping/
https://www.mediapost.com/publications/article/414950/amazon-brings-content-to-commerce-technology-into.html

Pre‑AI, user growth was the hard ceiling on revenue. You hit a point where the platform had basically saturated its audience, and the only real growth lever was “more users = more impressions.”

With AI‑driven ad systems, especially in walled gardens, the constraint looks different. The limit isn’t audience size, it’s decision quality: how well the system predicts, ranks, matches, and times the right creative for the right person.

If the model keeps getting better at prediction and learning from conversion feedback, the same user base can yield more and more value. Reach is still nice, but it’s not the primary bottleneck anymore.

So in an AI world, “user growth story” feels less important than “decisioning story.” The real question becomes: how efficiently can a platform turn existing attention into outcomes?

Which might be one reason walled gardens are thriving again: Google and Meta just posted their strongest quarterly ad numbers since the pandemic, with Google up 16% to $77B and Meta up 33% to $56.3B.

A lot of SEO and brand safety platforms are starting to add AI visibility features.
Until now, a lot of tools focused on if you show up (visibility) or how you’re portrayed (sentiment/safety). But increasingly, the real question is: is the AI actually right about your brand?

“Accuracy” — basically the gap between what the model says and your approved narrative — seems like it’s becoming the real differentiator. And it’s not something traditional SEO tools were built for, even if they’re now adding AI visibility layers.
What’s interesting is how this starts to overlap with brand governance. But it’s not the same thing:

• Brand safety = where your brand appears
• Accuracy = what the AI actually says

Different problem, different control layer.

We’re even seeing players from the brand safety/verification world move into this space, which makes sense… but also raises the question: who owns accuracy?

Curious how others see this:
If you’re a brand or agency, how important is this capability vs other features when evaluating vendors?