Snapchat has announced a new milestone for its Snapchat+ subscription offering, with more than 25 million people now signed up to the program, and Snap now generating over $1 billion per year from its non-advertising business.

Which also includes its Lens+ subscription, giving users exclusive access to new AR features, as well as its Memories storage plans. But primarily, this intake is driven by Snapchat+, underlining the value of this add-on offering for Snapchat’s dedicated audience.

As per Snapchat: “Since launching in late 2022, Snapchat+ has become one of the fastest-growing consumer subscription services globally, with subscriber growth every quarter. What started as an early-access program for our most engaged Snapchatters has quickly scaled into a meaningful business – one that now represents a strong and growing revenue stream alongside our ads business.”

Indeed, Snapchat+ has clearly been the most successful of the new wave of social subscription offerings, though Meta Verified has also seen strong take-up (Meta hasn’t provided any official data on this).

But Snapchat, with its package of user-aligned add-on tools, has clearly tapped into a valuable opportunity, using its audience nous to build add-on tools that users want to pay for, and enhance the Snapchat experience.

And Snapchat definitely needs to diversify its revenue intake.

Snapchat’s growth has stalled in both the U.S. and EU markets, which could suggest that it’s reached saturation point, and will have trouble expanding its user base beyond this core audience. Snap also still has a challenge in keeping older users coming back, with the app remaining a young user platform, that doesn’t see strong retention as people age up.

With that in mind, Snapchat needs to maximize the revenue opportunities that it has, as opposed to relying on expanded reach. And it has done this, with new ad options like Sponsored Snaps, which bring promotions into the Snap inbox.

But there’s only so much ads that Snap can push in front of its users, so it also needs to consider other opportunities.

Snapchat+ subscription are one consideration, while it also has its coming Specs AR glasses, which it’s hoping will maintain its relevance as a key platform in the evolving AR race.

Though Specs will have a significant challenger, in Meta’s AI glasses, with Meta also looking to launch its own AR version in 2027, and with Apple also looking to build another AR device, it will be difficult for Snap to make significant money out of Specs, at least in the short term.

Which leaves Snapchat+ as a critical opportunity, and with $1 billion per year in Snapchat+ revenue, it has become a valuable means to maximize the audience that it has, and complement its ad business.

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Marketing teams often struggle to connect what happens before a click with what happens after, especially when running Google Ads campaigns. Ads are built and managed in one place, while website performance and conversions live somewhere else. That gap can slow decision-making and make it harder to understand which campaigns are actually working.

That is the problem Webflow and Google are trying to narrow with a new integration that brings Google Ads campaign creation and performance tracking directly into the Webflow platform. According to a product update published on Webflow’s website, the integration allows teams to set up, run, and monitor Google Ads campaigns without leaving their site-building environment.

The move reflects a wider shift in marketing tools toward tighter links between content, advertising, and measurement, especially as automation plays a larger role in campaign management.

Bringing ads closer to the website

Based on details shared by Webflow, the integration is delivered through a native Google Ads for Webflow app, now available in the Webflow Marketplace. Once connected, a company’s Google Ads account can be linked directly to its Webflow sites, allowing teams to manage ads alongside their web content.

The update also supports Google’s Performance Max campaigns, which use automation to place ads across Google Search, YouTube, Display, Gmail, Discover, and Maps. Webflow’s announcement explains that marketers can create these campaigns using existing site assets, such as images, copy, and URLs already stored in Webflow.

Martech360 reports that this approach reduces the need to move between tools when launching or adjusting campaigns. Rather than building ads in Google Ads and then reviewing site behaviour elsewhere, marketers can view campaign metrics and on-site activity all in one place.

Why this matters for measurement

One of the recurring challenges in digital marketing is linking ad spend to real outcomes. While most teams track clicks and impressions, determining what users do after seeing a website frequently necessitates additional setup and manual analysis.

According to Webflow’s documentation, the integration handles much of the tagging and tracking required to connect Google Ads with site activity. This may help teams reduce the time spent on technical configuration, especially for standard conversion events such as form submissions or purchases.

An analysis published by TMCnet points out that closer ties between ads and site data can help teams react faster. If a campaign drives traffic to a page that performs poorly, marketers may be able to spot the issue earlier and adjust either the ad or the page itself.

This is particularly relevant as more campaigns rely on automated bidding and targeting. While automation can handle delivery at scale, teams still need clear signals to guide creative and structural changes.

How marketers are reacting

Some early users have shared their experiences publicly. In a LinkedIn post referenced by Webflow, Abby Liebenthal, Head of Marketing and Experiences at Fried Egg Golf, described how the integration affects campaign planning.

“Having Google Ads inside Webflow changes how we think about promotion,” Liebenthal said. “It allows our team to move from idea to live campaign faster.”

Another perspective comes from Ben Geller, Director of Product Marketing at You.com, who was quoted in a MartechVibe article covering the launch. Geller said the integration helps teams tailor messaging more closely to audience intent and ensure users land on pages built for specific campaigns.

These comments offer a glimpse into how some teams see value in reducing friction between content and advertising, though results will vary depending on how each organisation sets up and uses the tools.

Limits and trade-offs

While tighter integration can simplify workflows, it does not remove the need for careful planning. Webflow’s own integration guide notes that certain site behaviours, such as AJAX-based form submissions, may still require custom tracking steps to ensure conversions are recorded accurately.

There is also the broader question of control. Performance Max campaigns rely heavily on Google’s automation, which can limit visibility into where ads appear and why certain decisions are made. The Webflow integration makes these campaigns easier to manage, but it does not change how Google’s underlying systems work.

For teams that prefer hands-on control or advanced reporting, separate analytics and ad management tools may still play a role.

A sign of where marketing tools are heading

Viewed in context, the Webflow and Google Ads integration reflects a larger trend in marketing technology. Platforms that once focused on a single task — building websites or running ads — are now trying to reduce the gaps between creation, distribution, and measurement.

As Webflow explained in its product update, the goal is to help teams “connect creative work with performance data.” That language points to a growing demand for tools that support faster feedback and fewer manual handoffs.

For small and mid-size teams in particular, the appeal lies in simplicity. Managing fewer platforms can free up time, even if more specialised tools are still needed for deeper analysis.

The integration does not promise better results on its own. But by bringing ad creation and site performance closer together, it gives marketers another way to understand how their campaigns play out once users arrive.

(Photo by Myriam Jessier)

See also: Inside Google’s push to blend AI chat and online shopping

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Reddit has shared some new insights into effective marketing approaches in the app, as well as information on how brands are driving success with their Reddit promotions.

Based on key engagement trends, as identified by Reddit’s advertising team, the platform highlighted four key trends that are helping brands boost engagement and can help users create more compelling in-app activations.

Those four key trends are: nostalgia-driven storytelling, UGC social proof, niche-inspired campaigns and campaigns that unfold, not just launch.

For each of these trends, Reddit has shared an overview, including the main in-app communities, and notes on their development.

There are also more specific notes on how brands are incorporating these trends into their promotions, and driving response from the Reddit community.

And there are some valuable pointers, covering not only how these trends have emerged, and why they’re resonating with the Reddit community, but also how brands have adapted to such, and integrated these into their campaigns.

It could provide some key guidance on how to maximize your own Reddit ads approach, and appeal to Reddit’s community, which can be a little dismissive of ads and promotions. This is becoming a bigger consideration for more brands, as Reddit continues to see more usage, and plays a bigger role in the discovery process for many users.

Indeed, Reddit is now a key data source for AI chatbots, and more people are turning to the platform for genuine insight and human-centric notes, based on actual hands-on experience. As a result, Reddit is becoming a larger consideration for more brands, and these notes and pointers could be highly valuable in helping you develop a more responsive, engaging Reddit marketing strategy.

The complete notes are included in a 7-page guide from Reddit.

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Elon Musk has shared some insights into growth projections for the X app, as well as how X is currently performing, as part of a broader all-hands meeting of the new, combined xAI and SpaceX group.

Following the departures of several xAI executives, Musk sought to reassure staff of his future direction, by outlining his evolving plans to build a base on the moon, develop xAI, and more.

And within that, we also got some notes on X specifically, which point to how the platform is performing, and what kind of usage X is currently seeing.

First off, X’s head of product Nikita Bier said that X has now hit a $1 billion annual run rate for X subscriptions.

X Premium has been a major point of contention, with Musk hoping, initially, to get the majority of X’s users to pay for the app, as a means to both generate additional revenue (reducing reliance on advertisers) and deter spammers, by making account creation cost-prohibitive.

But thus far, X Premium hasn’t seemingly caught on in a major way, with reports suggesting that only around 1% of X’s user base are paying for the app, though earlier this year, external data did suggest that there had been an uptick in X subscriptions, driven by interest in its Grok AI tools.

And now, X claims that it’s now reached $1 billion in annual revenue from subscriptions.

I mean, that’s still a long way off what the platform generates from ads, but for comparison, Snapchat recently reported that it earned $750 million from Snapchat+ subscriptions in 2025. So X reaching $1 billion is within the realm of what could be expected, with expanding interest in Grok helping to drive more interest in its paid options.

Musk also gave a strange overview of how X actually has one billion users, or will have soon, or something.

As per Musk: “The reason we say 1 billion users is that while our monthly users are, on average, around 600 million, the number of people who have the X app installed is well over a billion, it’s just that most people only occasionally come to the X app when there’s some major world event. But as we give people more reasons to use the X app, whether it’s for communications, for Grok or for X money, whatever the case may be, we want it to be such that, if you wanted to, you could live your life on the X app, and as we make it more and more useful, we’ll obviously give people compelling reasons to use the app every day, and my expectation is well over a billion daily active users.”

So, X doesn’t have a billion users now, but it has a passive usage of over a billion, and it will have a billion actives sometime soon.

Though even the user count quoted here is in contention, because earlier in the week, Bier noted that he serves as customer support for 500 million people.

So, X has 500 million or 600 million active users, but actually, a billion people could use the app if it was better. So Musk is claiming a billion users, or saying that this is the aim.

Either way, there’s something about a billion users here.

X also claims that January saw a major spike in overall engagement, while February will beat that again, while first-time downloads are also up 50%.

And in terms of what people are doing in the app, X says that the amount of long-form articles published on X has jumped 10x, with reading of long-form posts in the app up 17x.

Though when you offer a $1 million prize for the most popular article in the app, that will tend to drive a lot more interest.

Finally, Musk also discussed the development of X Money, which he hopes will be the next big evolution for the app.

Musk’s dream is to create an all-in-one platform, in which users can engage, shop, pay bills, etc., all in a single app, as Chinese users do on WeChat, but thus far, Musk’s hopes for even simple money transfers in-stream have been dashed by regulatory approvals, which X has been struggling to obtain in some U.S. states.

Musk initially said that it would “blow my mind” if X didn’t have funds transfers up and running on X by the end of 2024, but more than a year on, we’re still yet to see anything on this front.

Though Musk says that it is coming.

Musk says that X Money is already operating internally, with expectations of a public launch within the next two months.

Yeah, I wouldn’t be betting too much on that happening, and Musk does have a long history of overly optimistic projections. But as it stands right now, X does not have a money transmitter license in New York, a key state for its plans, and without that, and approval in several other U.S. states, I’m not sure that we’re going to be seeing X Money in 2025.

But Musk says that it’s coming, probably by April. So we’ll see.

X didn’t offer insight into its ad business, so we don’t have full insight into the performance of the app, but these notes do provide some additional context as to how the X project is evolving, as Musk and Co. look for ways to make it a more valuable, engaging platform.

And it also forms a key part of X’s AI plans, with X data feeding into the xAI system. So it is a crucial puzzle piece for Musk’s broader plans, and it’ll be interesting to see if and how X can get more of its audience back in 2026.

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Have you jumped into the mini-drama trend on TikTok yet?

Popularized in China, the mini-drama format essentially sees full-length productions split into tiny episodes, that people can then tune into on TikTok. The plots of these shows tend to be soap opera-style, Hallmark movie-esque escapism (as explained by Business Insider: “”plots often revolve around tropes like badly behaving husbands or werewolf romance”), which generally end on cliffhanger confessions to keep people coming back for more.

And people are doing just that, with the format gaining significant traction on TikTok over the past year.

So, of course, as you would expect, Instagram is now also looking to latch onto the trend.

As highlighted by app researcher Alessandro Paluzzi, Instagram is currently experimenting with its own, dedicated Short Drama element within the app.

As this example shows, the new element, which is currently in early testing, and is not available to the public as yet, would seemingly enable Instagram users to follow their favorite short drama shows, and stay up to date on the latest episodes more easily.

Would that be beneficial?

Well, again, mini dramas are big on TikTok, with one estimate indicating that the format generated $1.3 billion in the US in 2025, mostly via direct payments from viewers.

And clearly TikTok sees potential. It launched a Minis section in the app last year, while it’s also launched a dedicated mini dramas app, called PineDrama, in the U.S. and Brazil.

The serialized approach to content replicates the daily soap opera style that has long been a staple on regular TV, and with shortened episodes, aligned with TikTok usage and engagement, it does seem to have potential as a means to drive more engagement, and keep people coming back to the app.

And Meta, which never wants to miss out on any trend, is also obviously aware of the potential here.

I haven’t watched a heap of these myself, so I can’t say much for the content, though as with soap operas, I doubt the artistic merit is the allure, with people more drawn into the exaggerated drama of these short episodes.

And maybe, that could also see it become a thing on IG.

We’ll keep you updated on any progress.

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YouTube is giving with one hand, and taking with the other, with recent strategy statements from the company giving contradictory messages about the use of AI on the platform.

In a communication to the YouTube community on Wednesday, YouTube’s chief executive, Neal Mohan, stated that a key part of the company’s strategy for 2026 is an initiative to lower the amount of low-quality, AI-derived content that appears in users’ feeds. “As an open platform, we allow for a broad range of free expression while ensuring YouTube remains a place where people feel good spending their time,” he said.

The company’s hands-off approach to content moderation is to change with regards to AI-generated content, in order to preserve the standard of videos posted, he said, thus safeguarding viewers’ experiences. Despite what YouTube describes as its ‘openness’, Mohan said the company will be “actively building on our established systems that have been very successful in combating spam and clickbait, and reducing the spread of low-quality, repetitive content.”

Yet simultaneously, YouTube is invested in AI-assisted content creation, with creator-focused features planned for 2026 including tools allowing creators to generate Shorts using AI models of themselves.

“AI will act as a bridge between curiosity and understanding. Ultimately, we’re focused on ensuring AI serves the people who make YouTube great: the creators, artists, partners, and billions of viewers looking to capture, experience, and share a deeper connection to the world around them.”

Creators will still be obliged to declare when they publish artificially generated or modified media, and YouTube will mark content produced using its own in-house AI tools.

Over a million channel owners used on-platform AI creation tools in December 2025 and there were over 20 million uses of YouTube Ask by viewers in the same month. In 2026, YouTube will roll out two new features in addition to the creator likenesses generated for Shorts, namely using text prompts to create games and experimental music production tools.

The company is also doubling down on protecting itself from criticisms over copyright infringing content on its platform. “We remain committed to protecting creative integrity by supporting critical legislation like the NO FAKES Act,” Mohan said.

“Ultimately, we’re focused on ensuring AI benefits those who make YouTube successful: the creators, artists, partners, and billions of viewers eager to capture, experience, and share a stronger connection to the world around them.”

The company’s strategy seems therefore to be to provide AI content creation tools to creators, and flag ensuing ‘creations’ as AI-generated so viewers are aware of their provenance. Creators are expected to declare whether or not they use AI in their work if using external software.

The company’s strategy, as embodied by the software that influences viewers’ choices, will become apparent over time. It’s not clear whether the YouTube algorithms will promote creators who use or don’t use AI, or choose on-platform AI tools over alternatives. YouTube seems to be hoping that viewers reject what Mohan termed “AI slop” but accept media created by YouTube-endorsed AI tools. The latter videos, presumably, won’t regarded as “AI slop,” despite the fact that they will be automatically flagged as AI-generated onscreen.

(Image source: “Rand Paul, Ai Generated Images, Public Domain, CC0” by MrScott+Ai Art is marked with CC0 1.0. )
 

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Tags: ai, content creation, google, shorts, video, youtube

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TikTok could be about to make a big leap into generative AI content, with parent company ByteDance announcing its latest artificial intelligence video tool, called Seedance 2.0, which is producing some amazingly realistic results.

Seedance 2.0, currently in Beta, is currently available to some users of Jimeng AI, which is another video platform owned by ByteDance. The tool utilizes the latest generative AI video capacity to produce more refined results than many other models, with more life-like, realistic movements, and less of that moving underwater feel.

And while it’s not on the cards for TikTok as yet, it seems likely that it’ll eventually make its way to the video platform, which already has a range of generative AI tools available to assist creators and marketers.

Over the past year, TikTok has added AI Group Shot, which enables users to add images of themselves and their friends to an image template, an AI meme generator, AI-powered translations and captions, video idea generators, image-to-video, AI selfies, custom avatar stickers, and more.   

So TikTok’s not exactly falling behind in the broader AI race, but Meta and X have put increased focus on their AI elements, and in particular, their evolving video generation models, as a means to drive more usage of their expensive AI features.

But Seedance 2.0 might just outperform them, which could see TikTok eventually steal their thunder. Which would be a big problem for the social media giants.

The biggest challenge for generative AI is making money, because it costs so much to build AI infrastructure, that just making your initial costs back is going to be a steep climb.

Meta, for example, is planning to spend $600 billion on AI data center projects over the next three years, while OpenAI is forecasting $1.4 trillion in data center investment over the next 8 years.

Which, really, is too much money to even comprehend, but for context, Meta’s full-year revenue for 2025 was just over $200 billion. The two biggest companies in the U.S., Walmart and Amazon, are bringing in just over $681 billion and $638 billion respectively, which shows just how much consumer demand will be required for these AI projects to actually make a profit.

That’s why X is trying to showcase its latest video tools, because it needs to start getting more people to sign-up to subscribe to its tools, so it can at least start heading towards funding its projects, while Meta will also be looking to sweeten its subscription packages with its newer AI models. Because the operating cost is going to necessitate huge revenue intake, across various elements.

And if TikTok comes in with a better, more realistic, more engaging video generation option, that won’t be good news for the respective ambitions of these AI projects.

What’s more, the evolution of Seedance suggests that Chinese developers may well be getting ahead of U.S. companies on AI development. That will not please the U.S. government, which recently outlined its plan to clear roadblocks to AI development, in order to ensure that it leads the way.

Also, now that TikTok U.S. is a separate entity from the main TikTok app, and technically separate from ByteDance, what will that mean for new features like this coming in? WIll the new TikTok USDS management team, which was hand-picked by the Trump administration, even want to add options like this if it means potentially harming or reflecting poorly on America’s own AI development?

As such, the development of Seedance 2.0, and other Chinese AI models, could have significant implications, though we don’t know the details of the codebase or process as yet.

But it’s another reminder that the AI race is global, and that developers in all regions are working with similar technology.

Will the best one win out, and if so, will the U.S. be the leader of the pack?

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AI tools are becoming a common sight in email marketing activities, but their effectiveness depends on how well the technology is integrated into existing systems and changing processes inside the marketing function. The approach that gets meaningful results focuses on governance, data quality, and measurement. Regardless of the details of any implementation of AI in this context, it’s fair to say up front that AI works best when treated as part of the marketing infrastructure rather than a media creation tool.

As is the case in any software integration, the success of AI in email campaigns relies on access to structured, reliable information, often housed in a CRM platform. But regardless of data’s source, initial work comprises of consolidating records, defining deal stages, and ensuring engagement history are all able to be mined from a single, or many systems. Without this, AI models find it difficult to distinguish between, for example, early-stage leads and firm prospects, or understand which messages support progression through sales funnel stages. Data quality, as ever, is a prerequisite for effective content generation.

Recipient consent is central with email, and AI-powered systems work fast: Their very speed and efficiency will expose an organisation to accusations of unsolicited mail unless care is taken. Marketing teams should review opt-ins and examine their existing compliance policies before ramping up the use of AI-generated workflows.

Once data and consent are in place – arguably the majority of the marketing team’s activity in any campaign – AI tools can be embedded in email workflows. Native assistants shipping with marketing platforms can be presented as more effective than disconnected tools, although companies may wish to diversify their software suppliers to avoid vendor lock-in and give themselves more options and testing possibilities (see below).

However, CRM-‘native’ AIs will be able to reference contact data, deal information, and past campaigns without integration. The work of establishing connections between a third party AI (perhaps running locally) is a task usually best performed by an IT specialist, and smaller organisations may not have the necessary staff or resources.

Getting going

Once given access to customer data, an AI can help marketers generate subject lines, body copy, rich media, and calls to action inside the email editor. Modular content – building messages with specific content blocks – helps retain visibility and provides the balance between impersonal, fully-automated messaging and manual content creation. The overriding ethos should be one of assisted content curation with oversight by a human marketer.

Building libraries of introductions, body text, product descriptions, and calls to action means the AI tools are given as much help as possible to assemble emails that are relevant to recipients. It also has the secondary benefit of tracking the effectiveness of individual content elements.

To help retain the human element and prevent breach of data policies (and ensure the brand’s messaging remains on point), approval processes are essential. In practical terms, pure AI-generated content is not necessarily ready for immediate deployment. Companies need to review their workflows and sample outputs, particularly for campaigns involving mention of price. In regulated industries or compliance-sensitive areas, this oversight is business-critical.

The art of the prompt

The quality of AI output depends on how clearly marketers can define the audience, set out a campaign’s objectives, and work out what constraints are necessary. Prompting an AI effectively is an acquired skill, and in the context of email campaigns, prompts should specify recipients’ lifecycle stages, segment membership, and the desired call-to-action, all translated into CRM-specific context (dictating raw field names, for example).

Welcome and activation emails should focus on introducing value and encouraging first actions. Nurture emails build understanding through examples and case studies, and also set the brand’s tone-of-voice and ethos. Sales acceleration messages target contacts who have already showed intent – here, repeated engagement with pricing information can be effective. Renewal and expansion emails focus on reinforcing value delivered and introducing relevant additions. It’s good practice to guide the AI to produce content aligned with a specific goal separately. Broad engagement, generated/aided by AI, or hand-crafted, can be too generic to be effective for sales.

Checks and balances

One phrase that often crops up with AI implementations in many contexts is guardrails. For the purposes of email campaigns, a two-stage QA process is often cited, with the first stage assessing clarity and accuracy of the message, and the second checking compliance, including data usage in terms of local (that’s local-to-the-recipient) regulation. This level of care helps prevent common AI-related issues such as invented statistics, exaggerated claims, inconsistent tone, or anodyne messaging. Artificial intelligence is still a new technology, and marketers, like most people, are still finding their way amid the myriad opportunities AI offers.

It’s important to consider privacy and consent early on. When prompting an AI, input should limit the levels of personalisation to that of given consent. When this is not known or denied, there should be responses and behaviours to fall back on. Erring on the side of caution is advisable.

Relevance or personalisation, it should be noted, does not necessarily need the exhaustive use of every available data point. A brand proving how much it knows about its prospects are more likely to create distrust than delight!

Testing

Like all marketing activities, measurement is central to evaluating AI’s contribution. A test-and-learn approach can be split along lifecycle stage lines, by demographic or desired outcome. AI is not a magic bullet to remove tasks like A/B testing, or response-tracking. With AI in particular – given its speed and efficiency, it’s advisable to change one variable at a time to maintain clarity around cause and effect.

With CRM-native AI, engagement, conversions, and movement along pipelines can be linked to specific content variants or prompts. This allows teams to compare AI-generated content with human-written alternatives and assess whether AI improves outcomes or simply reduces production time. The same considerations are possible with external AIs, of course (albeit coming with a technical overhead), and A/B testing of the language model itself can be highly effective. In short, if you have the resources and the CRM’s own AI is lacking, deploying a different model (one that’s more focused on a sector, or more capable generally) is an option worth exploring.

Finally, text produced by the optimum AI+human process can and should be repurposed for other channels. This save repeating work, and can help ensure a brand’s voice is maintained throughout all external messaging.

Conclusion

AI can help marketers work faster and cover more ground with email campaigns, but unless handled carefully, can multiply errors if not governed with care. Success comes from a sum of the parts: AI systems, prompt engineering, review processes, and the measurement of effectiveness. The sophistication of a model or marketing software rarely determines the outcome. AI adoption for email should be treated as an operational change, and as any change management specialist will inform you, change needs planning, control, and evaluation.

(Image source: “Mailbox” by jparise is licensed under CC BY-SA 2.0. )

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Tags: ai, compliance, content creation, email, strategy

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Of all of Elon Musk’s many changes to the platform formerly known as Twitter, Community Notes remains the most interesting, and the integration of AI-generated Community Notes adds another valuable element to a process that can significantly reduce the spread of misinformation in the app.

And this latest update to Community Notes is another interesting step, with X now testing out Collaborative Notes, which will make it easy for human Community Notes contributors to guide and improve AI-generated Notes.

As explained by X:

“When [a user] requests a note, AI drafts one, then the community refines it together through ratings and suggestions. You can watch it get better in real time.”

So rather than just relying on the AI response, and having a potentially flawed and/or misinterpreted AI answer as a fact-check, now, Community Notes contributors will be able to easily rate and respond to these AI notes, which could ensure that they’re updated with more relevant, accurate information.

That could help to address some key flaws with the notes system. A major problem with notes is the time that it takes for one to be displayed, because X is focused on real-time, in-the-moment discussion. That means that if it takes even 15 minutes for a note to be shown on a post, then at least some of its damage will be done, and it’s impossible to get notes up on these posts in real-time.

But AI helps to solve for this. AI-generated notes are only created when a user requests one, but they can be shown much faster, and the capacity to then refine these with similar speed, based on human ratings, could help to further reduce the impact of false claims.

It also assists in data gathering to support Notes, as the AI response will hopefully reference the relevant source material quickly, reducing the need for contributors to go through extensive work to provide relevant context.

Overall, it does seem like a helpful addition, but then again, the fact that the system is using Grok as its AI fact-check creator is also potentially problematic, because as Elon has repeatedly told us, he’s put in specific work to make Grok “less woke.” Which means that, on some of Musk’s pet issues, it’s likely to spread false information either way.

Indeed, Grok has been found to be promoting false information about past events, including a terrorist attack in France, while it’s also given incorrect information about the Holocaust, and about Musk himself.

So there may be some errors in the full AI process behind Community Notes, but as an isolated component within the Community Notes architecture, it seems like it could provide some benefit.

Really, you shouldn’t blindly trust any AI-generated response, as these are powered by the internet, and the internet has a lot of incorrect info polluting its sources. That’s especially true when X posts are the source data, but it’s another interesting element to consider in trying to create a better fact-checking process.

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Search and shopping are starting to collapse into the same place, as AI-driven interfaces encourage users to move from questions to purchases without leaving the conversation. Google is now applying that AI-driven shopping approach directly to its search and advertising systems. Instead of moving from a query to a product page to a checkout screen, users are being encouraged to stay inside a single conversational flow.

According to reporting by the Financial Times, Google is expanding its AI-powered shopping experience by embedding personalised ads directly into its AI chat interface. The effort is built around what the company calls a “Universal Commerce Protocol,” designed to let retailers support product discovery, comparisons, and purchases without sending users to external websites.

For marketers, the change signals more than a new ad format. It points to a future where search, commerce, and promotion happen in one place, shaped by AI responses rather than ranked links.

From search to AI-driven shopping at Google

The Universal Commerce Protocol is meant to sit underneath Google’s AI Mode, which presents users with conversational answers instead of a page of blue links. In that flow, shoppers can ask follow-up questions, compare options, and receive tailored product suggestions. Ads and offers appear as part of that exchange, rather than as separate units clearly set apart from organic results.

Retailers participating in the programme include large chains like Walmart and Target, as well as merchants that sell through Shopify. For these sellers, the promise is straightforward: fewer steps between interest and purchase, and more chances to influence decisions while the shopper is still asking questions.

The experience feels closer to chatting with a salesperson than browsing a store. The AI can narrow choices, explain differences, and surface promotions that match what it knows about the shopper’s preferences and past behaviour.

A new kind of ad environment

The setup blurs long-standing lines in digital advertising. Traditional search ads rely on keywords and clear intent signals. Display and shopping ads rely on placement and visuals. In an AI chat, intent unfolds over time, shaped by a back-and-forth conversation. Ads are inserted into that flow, often framed as helpful suggestions rather than interruptions.

That raises questions about how marketers plan and evaluate campaigns. If an offer appears after several prompts and follow-up questions, what triggered it? Which part of the conversation deserves credit for the sale? Existing attribution models were not built for this kind of environment.

Brand teams also face a tone challenge. Messages that sound like standard ad copy may feel out of place inside a conversational answer. Offers need to align with the context of the discussion, or they risk being ignored or mistrusted. This pushes advertisers to think less about slogans and more about relevance at each step of the buying process.

Control of the customer journey

The deeper issue is who owns the relationship with the customer. When discovery, comparison, and checkout happen inside an AI interface, retailers have less direct contact with shoppers. Email sign-ups, browsing data, and on-site behaviour are harder to capture if users never leave the chat.

At the same time, platforms gain more influence over what products are shown and which offers appear first. Even small changes in how the AI frames options can shift demand. For large retailers, this may be a manageable trade-off for reach and convenience. For smaller brands, it could increase dependence on platform rules they do not control.

The dynamic is not unique to Google. Other tech firms are also testing ways to fold commerce into AI-driven experiences. The difference here is scale. Search has long been one of the most important entry points to online shopping. Changing how it works changes the balance of power in the retail and advertising ecosystem.

Measurement remains an open question

One of the hardest problems is measurement. In a chat-based flow, impressions, clicks, and conversions do not always map cleanly to user actions. A shopper might read several AI-generated responses, consider a product, and complete a purchase later, all without a clear moment that looks like a click.

Marketers will need new ways to understand performance that go beyond last-touch models. They will also need clarity on how data is shared, what signals are available, and how privacy rules are applied inside conversational systems.

Without that transparency, some brands may hesitate to shift large budgets into AI chat commerce, even if the reach is attractive.

What AI-powered shopping means for marketing teams

For now, Google’s move offers a preview of where digital commerce is heading. The path from question to purchase is becoming shorter and more guided, with AI acting as both advisor and gatekeeper.

Marketing teams should see this as a signal to rethink how they show up during discovery, not just at checkout. Product data, pricing, availability, and tone all matter more when decisions are made inside a conversation rather than on a product page.

Advertising is about being useful at the right moment, inside systems that are designed to keep users from ever leaving.

(Photo by Solen Feyissa)

See also: Zeta brings generative AI deeper into marketing operations with OpenAI

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Tags: advertising, ai, customer experience, digital marketing, ecommerce, google, online shopping

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