u/u_of_digital

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?

OpenAI officially launched its self-serve ads manager this week, alongside CPC bidding and a conversion API that lets advertisers track post-click actions like cart adds and landing page views. The minimum spend requirement — which started at $200K and dropped to $50K in April — is now gone entirely. Fill rates have been low since launch; early advertisers reportedly struggled to spend their minimums due to limited inventory.

But the targeting mechanics are worth paying attention to. The ad served in a given conversation isn't determined by relevance alone. OpenAI factors in conversation content, the user's profile and preferences (for logged-in users), bid price, and how much of an advertiser's daily budget has already been spent. That's a fairly complex auction layer sitting inside what users mostly treat as a private thinking tool.

The user data angle hasn't gotten much mainstream attention yet. It probably should. OpenAI's February 2026 privacy policy set ad personalization to on by default for Free and Go users, most of whom haven't opted out. The targeting draws on conversation content, user profiles, and preferences (for logged-in users), plus buy-side factors such as bid price and how much of an advertiser's daily budget has already been spent. Advertisers only get aggregated metrics; no raw conversations are handed over. But the underlying signal is still your most unfiltered digital interaction, which puts it in a different category than a Google search or a Meta scroll.

Adthena released a free tool called AdBridge that lets advertisers move their existing Google Ads campaigns into ChatGPT Ads with minimal effort, including keywords, negative keywords, and some competitive insights in a CSV ready to upload. 

The key idea behind this launch is that much of ChatGPT's ad spend is expected to come directly from Google Search budgets, and tools like this are designed to make that transition as simple as possible. At the same time, OpenAI has lowered minimum spend levels.

The direction of the product is clearly influenced by the small but quickly expanding group of brands already experimenting with ads in the chatbot, and it’s only one part of a bigger stack Adthena is preparing to roll out. Sitting next to AdBridge will be Arlo, an AI helper aimed at letting marketers interrogate their own data and stack ChatGPT performance directly against traditional search.

Taboola rolled out the beta of Realize+, positioning it as an “agentic” system that continuously plans, executes, and optimizes campaigns in a closed loop. At the same time, they added Claude (Anthropic) as a built-in “skill,” so advertisers can manage campaigns through a conversational interface instead of traditional dashboards

AI element: This is basically pushing ad ops toward fully agent-driven workflows of budget allocation, creative iteration, and targeting adjustments happening in real time, similar to Google’s Performance Max or Meta’s Advantage+. The Claude layer shifts interaction from manual controls to something closer to “tell the system what you want and let it handle execution.”

What’s notable is how quickly this “agentic ops” model is becoming standard across ad platforms. In looking at where Taboola sits in that broader landscape, this kind of category map of AI tools came up in the research flow: https://uof.digital/ai/knowledgescape/. It situates Realize+ alongside a wider set of products approaching campaign automation in similar ways.

Meta released TRIBE v2, an AI model trained on 500+ hours of fMRI scans from 700+ people that predicts how the human brain responds to almost any image, video, or sound. It’s tri‑modal (vision, audio, language) and can do zero‑shot predictions for new people and tasks, effectively acting like a virtual brain that lights up in response to your content.

Marketers are already calling it an “MRI for ads”: upload a spot, get a second‑by‑second map of predicted attention and emotional response, and compare different creatives before you spend on media or run brand‑lift studies. You can already try browser‑based demos where you upload video, audio, or text, and TRIBE v2 returns predicted brain activity as interactive 3D heatmaps for different regions in seconds

Food for thought:
- Neural excitement is an intermediate signal, not a guarantee of sales
- Meta says this is for research/non‑commercial use (for now).

Read on:
https://aidemos.atmeta.com/tribev2
https://www.datacamp.com/tutorial/tribe-v2-tutorial

With AI Sponsored Snaps, brands can now drop full-on chatbots into your main Chat tab so you can “ask questions and get recommendations” without leaving the convo. Snap’s pitch: Sponsored Snaps already drive 22% more conversions and ~20% lower CPA vs other inventory, and now those same placements get an AI brain. They’re kicking this off with Experian, so yes, your credit advice might soon come from an ad agent sitting next to a friend group chat.

Food for thought:

- Nobody’s really saying when these bots appear in the flow. First reply? Twentieth? Tuned to “intent signals”? That’s the line between “useful assistant” and “unskippable upsell.”

- What’s the strategy when the assistant controls sequence and tone — are you optimizing for clicks, conversations, or outcomes inside the chat?

- How much risk are you willing to take on brand safety, hallucinations, and disclosure in exchange for first-mover advantage in conversational inventory?

Programmatic player MiQ acquired Rocket Lab, an AI-driven app marketing platform with strong reach across Latin America and Asia. The move brings in-app user acquisition, engagement tools, and a deeper pool of mobile data into MiQ’s ecosystem—likely feeding its Sigma platform for more predictive targeting. Rocket Lab will continue operating independently, with leadership staying in place. This follows closely on MiQ’s earlier pickup of Adsmovil, another LatAm-focused player, signaling a clear regional and mobile-first push. Financial terms remain under wraps.

Read on:
https://www.businesswire.com/news/home/20260407512278/en/MiQ-Acquires-Rocket-Lab-to-Accelerate-AI-Powered-App-Growth-Globally
https://blog.rocketlab.ai/en/why-choosing-media-channel-first-wrong-app-growth-latam
https://blog.rocketlab.ai/en/how-to-make-your-app-grow-in-latam