Ecommerce MGMT, Author at Ecommerce Management Education

Nov 28 2025

The Impact AI Is Having On The Marketing Ecosystem

I’m not someone who’s drunk much of the AI Kool Aid. I have sipped it. Swilled it around my mouth like you would an 1869 Château Lafite Rothschild.

But I’ve seen enough cult documentaries to know you should spit it back into the glass.

Do I love the opportunities it’s provided me in a work sense? Absolutely. Do I think it’s fundamentally shifted the marketing ecosystem? No. I think it’s expedited what’s been happening for some time.

Reddit’s resurgence is search-dominated.
The booming creator economy shows people trust people.
Word of mouth still travels.
Content still goes viral.
People don’t click unless they have to.

When you take a step back, Reddit’s traffic surge is absurd (Image Credit: Harry Clarkson-Bennett)

LLMs provide a good proxy as to how you’re seen online. And they really lean into review platforms and strong brands. Associating your brand with your core topics, removing ambiguity, and strengthening your product positioning is never a bad thing.

It’s not just about search anymore. In reality, it never should have been. It’s about connecting. Generating value from the different types of media.

TL;DR

The search customer journey spans TikTok, YouTube, Instagram, and everything in between.
Last-click attribution is outdated: BOFU platforms get the credit, but creators, communities, and discovery platforms do the heavy lifting.
AI hasn’t broken anything, it’s just exposing how messy, multi-platform, and people-driven it’s always been.
Brands win by understanding their audience, investing in creators, and building experiences that cut through an enshittified internet.

The Customer Journey Has Changed

True. But it’s been changing for a long time. Paid channels are becoming more expensive, owned channels like search send fewer clicks (mainly a Google-driven mechanic), and earned channels are looking more like the golden ticket to corporate stooges.

The majority of brands use last click attribution (gross, get away from me). A method that overvalues search. For the last decade or more, there have been discovery platforms that are more valuable than search – TikTok, YouTube, Instagram. Pick one.

I like time decay or a position-based/first and last touch model in the “new” world (Image Credit: Harry Clarkson-Bennett)

We tend to use search for finding products, brands, or stories we know exist. And for comparison, related searches. But as AI Mode rolls ever closer and Google looks to greedily take on middle of the funnel queries, Google’s role as a discovery platform will change. Theoretically, at least.

Like every big tech company, enough is never enough, and they don’t want to send you clicks. Unless you pay for them, of course.

And it isn’t just Facebook. These companies are disastrously greedy (Image Credit: Harry Clarkson-Bennett)

Search Is No Longer A Single Platform Journey

The Rise at Seven SEO and Data teams analysed 1.5 billion searches across five channels of the most-searched keywords on the internet and found that:

A buying journey can take anywhere from two days to 10 weeks, with up to 97 interactions along the way.
Google only accounts for just 34.5% of total search share.
YouTube (24%), TikTok (16.7%), and Instagram (20.9%) make up more than 60%.
The average consumer now uses 3.6 platforms before making a purchase.

But Google isn’t really a discovery platform. Maybe a bit. Google Shopping. Some comparison searches. But it’s not what anyone is there for.

Someone sees a product on Instagram or TikTok. They read a review of it on Reddit (probably through Google, albeit with a branded search) and watch videos of it on TikTok or YouTube.

They might even buy direct or via Amazon. At best, they perform a branded search in Google.

Now, tell me, last click attribution makes sense.

I think it’s worth noting here that so many of these other platforms are driven by a clickless algorithm. Google requires a click. A fundamental search. The others have homepages that stare directly into your soul.

I don’t think any of this is new. And I suspect it’s been a while since search was a single platform journey. But it depends on what you define as search, I suppose.

Google’s Messy Middle is about right. We have been living through an era of marketing desperately tied to trying to track every penny. Something that has been a near-impossible job for some time. At some point, you just have to sit down, try to know your audience better than anyone else, and have at it.

We need to influence clicks via search before that happens. Brands have to focus their time on the right channels for their audience. Not just search. That’s why knowing your audience and using an attribution model that doesn’t just value the BOFU click matters.

But Has AI Been The Catalyst?

Probably a bit. Behavior has been changing long before LLMs hit the public arena. It’s changed because people have better options. More visually decisive. More authentic. The creator economy has boomed because people trust people.

When I’m doomscrolling on the bog or on the tube (praise be to the 5G gods), I might get served a new product.
If I want real opinions or reviews about said product, I might go to Reddit (albeit through Google) to see what someone thinks. Well, I wouldn’t because I’m an adult with a wife and a mortgage, but you see my point.
I might subscribe to specific Substacks or creators who use and speak about the product.
My favorite LLM might give me product ideas (which I would check very carefully).
Hell, I might even see something IRL on the tube.

A lot of this ends with a Google search. Maybe all of it. Google is a navigational engine. Hence, the last click attribution issue. I suspect the last click isn’t the most important session in the majority of cases.

Unless you’re young, lazy, or both, AI just won’t cut the mustard. Hell, Google’s kingpin tells you not to blindly trust AI. Even the guys fundamentally selling us this stuff are telling us it has some pretty serious flaws.

You’re a naysayer if you ask Sam about the company valuation, spiralling costs, or insane problems. (Image Credit: Harry Clarkson-Bennett)

It’s one of the reasons user journeys are so much more complex and elongated.

We have more effective platforms and opinions than ever.
We have more shitty platforms and opinions than ever.

Cutting through the noise is everything. For people and brands. So you have to learn how to build brands and products that are bold and get the right people talking and sharing.

90% of marketers say creator content yields stronger engagement and 83% link it to more conversions. And 61% of consumers trust recommendations from creators more than they trust brand advertising.

The algorithms trust people because people do.

Channel-By-Channel Breakdown

Things don’t happen in a silo. Call me old-fashioned, but I think we’d all do well to work together as a marketing department. AIOs don’t just affect search. They have a knock-on effect on the entire ecosystem, and it’s important we understand the what and the why.

SEO

Where do we start? I’ll try and be brief. The most obvious and direct threat is zero-click search, which has been on the rise for some time. While AI hasn’t been the key driver of this, it has and will continue to reduce referral traffic.

AIOs have significantly reduced CTR, particularly for informational, TOFU queries.
AI Mode is there to steal middle of the funnel clicks to “help users make the right decision.”
LLMs offer something of an alternative to search. Although based on what people really use them for, I think they are complementary, rather than a replacement.

I think AI has done some very interesting things in the SEO space. Vibe engineering platforms like Cursor and prototyping platforms like Lovable have opened up new worlds.

If you can wade through the shit, you can do some brilliant things.

Then you have Profound’s Zero Click conference, where one of the speakers said he felt sorry for anyone working in SEO. According to this turdy savant, there’s very little crossover between SEO and insert favorite acronym before proceeding to discuss lots of SEO ideas from 2012.

People who just do not understand marketing, SEO, the internet, or people. These are the guys driving the enshittification of our day-to-day lives. (More on this later).

PPC

PPC and SEO are ugly cousins, really. We operate in the same space, we target the same traffic. So it stands to reason that AIOs and AI Mode significantly impact paid search.

If you can believe it, it’s broadly a negative.

I know. I, too, am stunned.

Thanks to Seer Interactive, we have near-conclusive data that proves how serious this impact has been. When an AIO is present, and you are not cited, clicks are down over 78%.

Even when there’s no AIO present, paid clicks are down 20%. This is disastrous. Customer acquisition becomes more expensive, and the blended CPAs are significantly more expensive.

This may show a real and significant shift in user behavior. Users are becoming so used to getting what they want from a TOFU search, they don’t even follow up when an AIO isn’t present.

Attention is slipping everywhere.

Social

We’ve seen the rapid rise of disinformation in search. Google’s been promoting fake content to millions of people on Discover and has been struggling to block them for some time. Gaming the system isn’t new. PBNs, expired domain abuse, link schemes. You name it, it’s working.

Some very good expired domains and PBN abuse here, post the 2025 SPAM update (Image Credit: Harry Clarkson-Bennett)

Thankfully, the Vote Leave Take Control team have put their talents to good use and can now tell me what casino site should I choose.

The scale is unprecedented. Bullshit flies everywhere.

And that’s where social comes in. Globally, the average person spends 2 hours and 24 minutes on social media every day. That’s a lot of time to be hit by fake news. Personalized fake news, too. So maybe it’s not a surprise that social use has been on the decline for the last couple of years.

According to this study by the FT, social media use has decreased by 10% and has been driven by (*shakes fist*) the youth. I think these platforms are a shell of what they once were. The connections they provided have been replaced by absolute bullshit.

They will do literally anything to get and hold your attention. Except help you stay in touch with people or watch something that isn’t AI-generated. The content quality bell curve we see in search is mirrored by the enshittification of social channels.

First, the platform attracts users with some bait, such as free access.
Then the activity is monetized, bringing in the business customers with no thought for the user experience.
Once everyone is “trapped,” the value is transferred to their executives and shareholders.

People with no understanding of marketing or people thinking that auto-generated comments will boost their profile on LinkedIn. Businesses using AI to cut corners to generate meaningless bullshit and throwing it at me. See Coca Cola advert for reference.

Nothing says happy holidays like being fired for an incompetent robot.

The lights are on, the wheels are turning, but nobody is home. Or cares. The Mark Zuckerburgs of the world are, hopefully, turning people off hyper addictive brain rot.

Impressive, I know. Thank god for Ryan Air.

Best social media strategy on the planet (Image Credit: Harry Clarkson-Bennett)

As email is an owned channel, there’s not an obvious issue with generative AI. However, the Litmus State of Email Report shows the top roadblocks and operational challenges encountered by teams.

AI makes all of these roadblocks worse. Crummy, personality-devoid content churned out at scale will lower engagement. And it doesn’t take a genius to figure out that execs would love to save on personnel.

Operationally, you’d think AI will help. But if producing high-quality content at scale and improving your core benchmarks are fundamental issues, I’m not sure AI is the answer.

Personalization, research, and distribution. Absolutely. Creating content that draws real people in and engages with them. Color me sceptical.

Paid Vs. Earned Vs. Owned

This is all about the funnel. If it becomes more expensive to acquire customers in their unaware/aware phase with paid campaigns, your owned and earned channels need to work harder. They need to work harder to increase your conversion rate.

Paid campaigns or projects are designed to do two things: reach a newer potential audience and retarget an existing, highly qualified one. But they’re becoming more expensive. Especially in a PPC sense.
Most sensible companies are trying to build their email databases off the back of search and organic social. Owned media is simultaneously under threat and incredibly valuable.
Earned media – public exposure through word of mouth and shared content – is arguably more important than ever. People really trust people’s opinions.

Never a truer word spoken (Image Credit: Harry Clarkson-Bennett)

What Should You Do?

As an SEO and a marketer, you should focus on creating real connections with people. Understanding your audience. Leveraging people that have influence over your audience. Build, work with, and promote brilliant creators and own your audience data internally.

Squeeze every last drop out of your content. Cut and share it in appropriate formats across multiple channels.

Email is almost certainly the most applicable channel for most brands. You actually own it. Then figure out the role your brand plays in that journey. Create a great user experience on and off-site. Make sure it’s well documented, and you own everything in your control. Speak to your PPC and social teams to understand the challenges they’re having.

Help Center.
FAQ and product pages.
ToV consistency and brand guidelines.
Reviews and complaints (On and off-site).
Technical site quality.
Content quality.
Large-scale, TOFU campaigns.

This isn’t just about marketing. Or LLMs. They are just a good proxy for how you are seen on the internet.

It’s about working together as a marketing department with a shared goal of creating and amplifying brilliant experiences to the right people. Maximising the value of your owned channels, to reduce the reliance on paid, and doing things that create brand advocates and cause your earned media to soar.

There’s an opportunity here to do great things!

But whatever you do, don’t forget about good quality SEO. It’s the primary purpose of our job and it still works.

More Resources:

This post was originally published on Leadership in SEO.

Featured Image: MR.DEEN/Shutterstock

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Nov 28 2025

Paid Ad Scheduling Across Time Zones That Actually Works via @sejournal, @brookeosmundson

Scheduling ads in Google or Microsoft Ads sounds simple until you realize how many hours you’re wasting showing them at the wrong time.

A campaign that performs well in one market might fall flat in another, not because your targeting or creative is off, but because of when your ads appear.

Managing time zones is one of the easiest ways to improve efficiency and stop unnecessary spend. Yet, many PPC managers still rely on default settings or assume their ad platform will “figure it out.”

In reality, effective ad scheduling requires strategy, testing, and an understanding of how local behavior differs across regions.

This guide breaks down how to identify true peak hours, segment campaigns by region, and use automation tools to make scheduling work in your favor, no matter where your audience is.

Understanding Time Zone Challenges In PPC

When advertising across multiple regions, time zone discrepancies can create challenges that impact ad delivery, engagement, and conversions.

A common pitfall is assuming that a single campaign schedule will work universally. In reality, what works in one location might be completely ineffective in another.

For example, if your Google Ads account is set to Eastern Time but your target audience is primarily on the West Coast, your ads might be running during off-hours, leading to suboptimal performance.

International campaigns require even more diligence to consider local business hours and consumer behavior patterns.

Another factor is peak engagement hours. While lunchtime or evening hours may be prime time in one country, those same hours could be completely irrelevant in another.

Understanding these nuances is essential for optimizing your ad scheduling strategy.

Advanced Strategies For Scheduling Ads Across Time Zones

Successfully managing ad scheduling across time zones requires a thoughtful approach that goes beyond the basics.

While many advertisers set simple schedules and hope for the best, the real wins come from leveraging automation, data-driven insights, and strategic segmentation.

Whether you’re running campaigns domestically across U.S. time zones or managing international PPC efforts, applying advanced techniques can help ensure your ads are served at the right time for the right audience.

Segmenting Campaigns By Time Zone For Better Control

If you’re running campaigns across multiple time zones, one of the best ways to stay in control is by creating separate campaigns for different regions.

This lets you adjust ad schedules, budgets, and bidding strategies based on local peak performance times rather than forcing a single schedule to work for every location.

For example, an ecommerce brand serving customers in the U.S. and Europe might run separate campaigns for each region.

The U.S. campaign can focus on morning and evening hours when engagement peaks, while the European campaign targets prime shopping hours in local time zones.

While this approach adds complexity, the benefits far outweigh the extra management effort. Automating adjustments with rules and scripts can help streamline this process, ensuring each campaign is optimized without constant manual oversight.

Leveraging Automated Bidding Over Fixed Schedules

Manual ad scheduling has its place, but automated bid strategies like Target ROAS or Maximize Conversions allow you to optimize bids dynamically rather than setting fixed hours.

These AI-driven approaches adjust bids in real time, ensuring ads appear when conversion probability is highest, regardless of time zone differences.

For instance, if data shows that users in one region convert at a higher rate between 9 a.m. and 11 a.m. but another region performs better in the evening, automated bidding will allocate more budget when it matters most.

Instead of manually adjusting bids every few weeks, let machine learning do the heavy lifting.

Optimizing Scheduling Based On Market-Specific Peak Hours

Different markets have different user behaviors, so it’s crucial to base your scheduling decisions on actual performance data rather than assumptions.

Google Ads’ ad schedule reports and Microsoft Ads’ time-of-day insights can help you identify when users in each region are most active.

For example, if analytics reveal that North American users are most engaged in the evening while European users peak in the morning, your campaigns should reflect that.

Instead of blanketing all markets with a generic ad schedule, tailor your approach based on real-time engagement trends.

Using Labels To Manage And Adjust Scheduling

One often overlooked yet powerful feature in Google and Microsoft Ads is the use of labels.

Labels let you group campaigns, ad groups, or keywords into easily manageable categories, making it simpler to track and adjust schedules.

For example:

Tagging campaigns by region allows for easy bulk adjustments when shifting schedules due to seasonal changes or promotional events.
Labeling time-sensitive ads ensures that you can quickly pause or resume campaigns as needed without sifting through dozens of settings.
Using automation scripts with labels enables automatic bid adjustments or scheduling changes based on real-time performance.

By applying labels effectively, you can streamline scheduling changes without manually editing each campaign, saving time and reducing errors.

Automating Scheduling Adjustments With Scripts

If you’re managing multiple time zones, Google Ads scripts can be a game-changer.

Rather than manually adjusting schedules, scripts can dynamically modify bids based on real-time performance data.

For example, a script could be set up to boost bids by 20% during high-converting hours and reduce them by 10% when conversions drop. This keeps campaigns optimized while freeing up time to focus on strategy rather than daily bid adjustments.

Scripts also work well with labels. You can program scripts to modify bid strategies for campaigns tagged with specific labels, ensuring changes are applied only to relevant ads.

Adjusting For Daylight Saving Time Changes

Another scheduling headache is Daylight Saving Time (DST), which varies by country and can cause misalignment in ad schedules.

A campaign that ran perfectly last month might suddenly be off by an hour if a region switches to DST.

To avoid this, maintain a calendar of DST changes in key markets and adjust schedules proactively.

Another option is using automated rules or machine learning-based bid adjustments to handle these shifts without manual intervention.

Budget Allocation Based On Regional Performance Trends

Rather than splitting your budget evenly across all time zones, consider allocating more spend to the highest-performing regions based on historical data.

By analyzing performance reports, you can determine which locations deliver the best ROI and adjust budgets accordingly.

For instance, if your data shows that conversions peak in the late evening for Pacific time zone users but decline in the early morning for Eastern time users, shift more budget toward the stronger-performing time periods.

This approach ensures ad spend is being used effectively rather than wasted on time slots that don’t generate conversions.

Turning Time Zones Into An Advantage

Ad scheduling is just one of many levers that can make or break your campaign performance. When your ads align with local customer behavior, your budget works harder, and engagement improves.

Use data to pinpoint when conversions actually happen, then adjust delivery windows to match those trends.

Lean on automation to keep schedules consistent, especially across multiple markets, and review reports often enough to spot shifting patterns.

Treat time zone planning as part of your optimization routine, not a one-time setup. The more precisely your ads reflect when people are active, the stronger your results will be.

More Resources:

Featured Image: Roman Samborskyi/Shutterstock

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Nov 28 2025

Moving toward LessOps with VMware-to-cloud migrations

Today’s IT leaders face competing mandates to do more (“make us an ‘AI-first’ enterprise—yesterday”) with less (“no new hires for at least the next six months”).

VMware has become a focal point of these dueling directives. It remains central to enterprise IT, with 80% of organizations using VMware infrastructure products. But shifting licensing models are prompting teams to reconsider how they manage and scale these workloads, often on tighter budgets.

DOWNLOAD THE ARTICLE

For many organizations, the path forward involves adopting a LessOps model, an operational strategy that makes hybrid environments manageable without increasing headcount. This operational philosophy minimizes human intervention through extensive automation and selfservice capabilities while maintaining governance and compliance.

In practice, VMware-to-cloud migrations create a “two birds, one stone” opportunity. They present a practical moment to codify the automation and governance practices LessOps depends on—laying the groundwork for a leaner, more resilient IT operating model.

Download the full article.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

This content was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

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App Climate change and energy The Spark

Nov 28 2025

This year’s UN climate talks avoided fossil fuels, again

If we didn’t have pictures and videos, I almost wouldn’t believe the imagery that came out of this year’s UN climate talks.

Over the past few weeks in Belem, Brazil, attendees dealt with oppressive heat and flooding, and at one point a literal fire broke out, delaying negotiations. The symbolism was almost too much to bear.

While many, including the president of Brazil, framed this year’s conference as one of action, the talks ended with a watered-down agreement. The final draft doesn’t even include the phrase “fossil fuels.”

As emissions and global temperatures reach record highs again this year, I’m left wondering: Why is it so hard to formally acknowledge what’s causing the problem?

This is the 30th time that leaders have gathered for the Conference of the Parties, or COP, an annual UN conference focused on climate change. COP30 also marks 10 years since the gathering that produced the Paris Agreement, in which world powers committed to limiting global warming to “well below” 2.0 °C above preindustrial levels, with a goal of staying below the 1.5 °C mark. (That’s 3.6 °F and 2.7 °F, respectively, for my fellow Americans.)

Before the conference kicked off this year, host country Brazil’s president, Luiz Inácio Lula da Silva, cast this as the “implementation COP” and called for negotiators to focus on action, and specifically to deliver a road map for a global transition away from fossil fuels.

The science is clear—burning fossil fuels emits greenhouse gases and drives climate change. Reports have shown that meeting the goal of limiting warming to 1.5 °C would require stopping new fossil-fuel exploration and development.

The problem is, “fossil fuels” might as well be a curse word at global climate negotiations. Two years ago, fights over how to address fossil fuels brought talks at COP28 to a standstill. (It’s worth noting that the conference was hosted in Dubai in the UAE, and the leader was literally the head of the country’s national oil company.)

The agreement in Dubai ended up including a line that called on countries to transition away from fossil fuels in energy systems. It was short of what many advocates wanted, which was a more explicit call to phase out fossil fuels entirely. But it was still hailed as a win. As I wrote at the time: “The bar is truly on the floor.”

And yet this year, it seems we’ve dug into the basement.

At one point about 80 countries, a little under half of those present, demanded a concrete plan to move away from fossil fuels.

But oil producers like Saudi Arabia were insistent that fossil fuels not be singled out. Other countries, including some in Africa and Asia, also made a very fair point: Western nations like the US have burned the most fossil fuels and benefited from it economically. This contingent maintains that legacy polluters have a unique responsibility to finance the transition for less wealthy and developing nations rather than simply barring them from taking the same development route.

The US, by the way, didn’t send a formal delegation to the talks, for the first time in 30 years. But the absence spoke volumes. In a statement to the New York Times that sidestepped the COP talks, White House spokesperson Taylor Rogers said that president Trump had “set a strong example for the rest of the world” by pursuing new fossil-fuel development.

To sum up: Some countries are economically dependent on fossil fuels, some don’t want to stop depending on fossil fuels without incentives from other countries, and the current US administration would rather keep using fossil fuels than switch to other energy sources.

All those factors combined help explain why, in its final form, COP30’s agreement doesn’t name fossil fuels at all. Instead, there’s a vague line that leaders should take into account the decisions made in Dubai, and an acknowledgement that the “global transition towards low greenhouse-gas emissions and climate-resilient development is irreversible and the trend of the future.”

Hopefully, that’s true. But it’s concerning that even on the world’s biggest stage, naming what we’re supposed to be transitioning away from and putting together any sort of plan to actually do it seems to be impossible.

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

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Nov 28 2025

The Download: the fossil fuel elephant in the room, and better tests for endometriosis

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

This year’s UN climate talks avoided fossil fuels, again

Over the past few weeks in Belem, Brazil, attendees of this year’s UN climate talks dealt with oppressive heat and flooding, and at one point a literal fire broke out, delaying negotiations. The symbolism was almost too much to bear.

As emissions and global temperatures reach record highs again this year, I’m left wondering: Why is it so hard to formally acknowledge what’s causing the problem?

—Casey Crownhart

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

New noninvasive endometriosis tests are on the rise

Endometriosis inflicts debilitating pain and heavy bleeding on more than 11% of reproductive-age women in the United States. Diagnosis takes nearly 10 years on average, partly because half the cases don’t show up on scans, and surgery is required to obtain tissue samples.

But a new generation of noninvasive tests are emerging that could help accelerate diagnosis and improve management of this poorly understood condition. Read the full story.

—Colleen de Bellefonds

This story is from the last print issue of MIT Technology Review magazine, which is full of fascinating stories about the body. If you haven’t already, subscribe now to receive future issues once they land.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 OpenAI claims a teenager circumvented its safety features before ending his life
It says ChatGPT directed Adam Raine to seek help more than 100 times. (TechCrunch)
+ OpenAI is strongly refuting the idea it’s liable for the 16-year old’s death. (NBC News)
+ The looming crackdown on AI companionship. (MIT Technology Review)

2 The CDC’s new deputy director prefers natural immunity to vaccines
And he wasn’t even the worst choice among those considered for the role. (Ars Technica)
+ Meet Jim O’Neill, the longevity enthusiast who is now RFK Jr.’s right-hand man. (MIT Technology Review)

3 An MIT study says AI could already replace 12% of the US workforce
Researchers drew that conclusion after simulating a digital twin of the US labor market. (CNBC)
+ Separate research suggests it could replace 3 million jobs in the UK, too. (The Guardian)
+ AI usage looks unlikely to keep climbing. (Economist $)

4 An Italian defense group has created an AI-powered air shield system
It claims the system allows defenders to generate dome-style missile shields. (FT $)
+ Why Trump’s “golden dome” missile defense idea is another ripped straight from the movies. (MIT Technology Review)

5 The EU is considering a ban on social media for under-16s
Following in Australia’s footsteps, whose own ban comes into power next month. (Politico)
+ The European Parliament wants parents to decide on access. (The Guardian)

6 Why do so many astronauts keep getting stuck in space?
America, Russia and now China have had to contend with this situation. (WP $)
+ A rescue craft for three stranded Chinese astronauts has successfully reached them. (The Register)

7 Uploading pictures of your hotel room could help trafficking victims
A new app uses computer vision to determine where pictures of generic-looking rooms were taken. (IEEE Spectrum)

8 This browser tool turns back the clock to a pre-AI slop web
Back to the golden age of pre-November 30 2022. (404 Media)
+ The White House’s slop posts are shockingly bad. (NY Mag $)
+ Animated neo-Nazi propaganda is freely available on X. (The Atlantic $)

9 Grok’s “epic roasts” are as tragic as you’d expect
Test it out at parties at your own peril. (Wired $)

10 Startup founders dread explaining their jobs at Thanksgiving
Yes Grandma, I work with computers. (Insider $)

Quote of the day

“AI cannot ever replace the unique gift that you are to the world.”

—Pope Leo XIV warns students about the dangers of over-relying on AI, New York Magazine reports.

One more thing

Why we should thank pigeons for our AI breakthroughs

People looking for precursors to artificial intelligence often point to science fiction or thought experiments like the Turing test. But an equally important, if surprising and less appreciated, forerunner is American psychologist B.F. Skinner’s research with pigeons in the middle of the 20th century.

Skinner believed that association—learning, through trial and error, to link an action with a punishment or reward—was the building block of every behavior, not just in pigeons but in all living organisms, including human beings.

His “behaviorist” theories fell out of favor in the 1960s but were taken up by computer scientists who eventually provided the foundation for many of the leading AI tools. Read the full story.

—Ben Crair

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)

+ I hope you had a happy, err, Green Wednesday if you partook this year.
+ Here how to help an endangered species from the comfort of your own home.
+ Polly wants to FaceTime—now! (thanks Alice!)
+ I need Macaulay Culkin’s idea for another Home Alone sequel to get greenlit, stat.

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Nov 27 2025

The Alpha Is Not LLM Monitoring via @sejournal, @Kevin_Indig

Adobe just paid $1.9 billion for Semrush. Not for the LLM tracking dashboards. For the platform, the customer relationships, and the distribution.

Contrast: Investors poured $227 million into AI visibility tracking. Most of that went to tracking dashboards. The companies shipping outputs from agentic SEO raised a third of that. Adobe’s acquisition proves dashboards were never the point.

Investors chased LLM monitoring because it looked like easy SaaS, but the durable value sits in agentic SEO tools that actually ship work. Why? Because agentic SEO goes beyond the traditional SEO tooling setup, and offers SEO professionals and agencies a completely new operational capability that can augment (or doom) their business.

Together with Wordlift, Growth Capital, Niccolo Sanarico, Primo Capital, and G2, I analyzed the funding data and the companies behind it. The pattern is clear: Capital chased what sounded innovative. The real opportunity hid in what actually works.

1. AI Visibility Monitoring Looked Like The Future

We looked at 80 companies and their collective $1.5 billion in venture funding:

Established platforms (five companies) captured $550 million.
LLM Monitoring (18 companies) split $227 million.
Agentic SEO companies got $86 million.

AI visibility tracking seemed like the obvious problem in 2024 because every CMO asked the same question: “How does my brand show up in ChatGPT?” It’s still not a solved problem: We don’t have real user prompts, and responses vary significantly. But measuring is not defensible. The vast number of startups providing the same product proves it.

Monitoring tools have negative switching costs. Agentic tools have high switching costs.

Low pain: If a brand turns off a monitoring dashboard, they lose historical charts.
High pain: If a brand turns off an agentic SEO platform, their marketing stops publishing.

Venture capital collectively invested +$200 million because companies care about how and where they show up on the first new channel since Alphabet, Meta, and TikTok. The AI visibility industry has the potential to be bigger than the SEO industry (~$75 billion) because Brand and Product Marketing departments care about AI visibility as well.

What they missed is how fast that trend becomes infrastructure. Amplitude proved it was commoditizable by offering monitoring for free. When Semrush added it as a checkbox, the category collapsed.

2. The Alpha Is In Outcomes, Not Insights

Outcomes trump insights. In 2025, the value of AI is getting things done. Monitoring is table stakes.

73% of AI visibility tracking companies were founded in 2024 and raised $12 million on average. That check size is typically reserved for scale-stage companies with proven market-fit.

Our analysis reveals a massive maturity gap between where capital flowed and where value lives.

Monitoring companies (average age: 1.3 years) raised seed capital at growth valuations.
Agentic SEO companies (average age: 5.5 years) have been building infrastructure for nearly a decade.

Despite being more mature, the agentic layer raised one-third as much capital as the monitoring layer. Why? Because investors missed the moat.

Investors dislike “shipping” tools at the seed stage because they require integration, approval workflows, and “human-in-the-loop” setup. To a VC, this looks like low-margin consulting. Monitoring tools look like perfect SaaS: 90% gross margins, instant onboarding, and zero friction.

Money optimized for ease of adoption and missed ease of cancellation.

The Monitoring Trap: You can turn off a dashboard with a click to save budget.
The Execution Moat: The “messy” friction of agentic SEO is actually the defensibility. Once an operational workflow is installed, it becomes infrastructure. You cannot turn off an execution engine without halting your revenue.

Capital flowed to the “clean” financials of monitoring, leaving the “messy” but durable execution layer underfunded. That is where the opportunity sits.

Three capabilities separate the winners from the features:

Execution Velocity: Brands need content shipped across Reddit, TikTok, Quora, and traditional search simultaneously. Winners automate the entire workflow from insight to publication.
Grounding in Context: Generic optimization loses to systems that understand your specific business logic and brand voice. (Ontology is the new moat).
Operations at Scale: Content generation without pipeline management is a toy. You need systems enforcing governance across dozens of channels. Point solutions lose; platform plays win.

The difference is simple: one group solves “how do I know?” and the other solves “how do I ship?”

3. The Next 18 Months Will Wipe Out The Weakest Part Of The AI Stack

The market sorts into three tiers based on defensibility:

1. Established platforms win by commoditizing. Semrush and Ahrefs have customer relationships spanning two decades. They’ve already added LLM monitoring as a feature. They now need to move faster on the action layer – the workflow automation that helps marketers create and distribute assets at scale. Their risk isn’t losing relevance. It’s moving too slowly while specialized startups prove out what’s possible.

The challenge: Established platforms are read-optimized; agentic operations require write-access. Semrush and Ahrefs built 20-year moats on indexing the web (Read-Only). Moving to agentic SEO requires them to write back to the customer’s CMS (Write-Access).

2. Agentic SEO platforms scale into the gap. They’re solving real operational constraints with sticky products. AirOps is proving the thesis: $40 million Series B, $225 million valuation. Their product lives in the action layer – content generation, maintenance, rich media automation. Underfunded today, they capture follow-on capital tomorrow.

3. Monitoring tools consolidate or disappear. Standalone AI visibility vendors have 18 months to either build execution layers on top of their dashboards or find an acquirer. The market doesn’t support single-function tracking at venture scale.

Q3/Q4 2026 could be an “Extinction Event.” This is when the 18-month runway from the early 2024 hype cycle runs out. Companies will go to market to raise more money, fail to show the revenue growth required to support their 2024 valuations, and be forced to:

Accept a “down-round” (raising money at a lower valuation, crushing employee equity).
Sell for parts (acqui-hire).
Fold.

Let’s do some basic “Runway Math”:

Assumption: The dataset shows the average “Last Funding Date” for this cluster is March 2025. This means the bulk of this €227 million hit bank accounts in Q1 2025.
Data Point: The average company raised ~€21 million.
The Calculation: A typical Series A/Seed round is calculated to provide 18 to 24 months of runway. With the last funding in Q1 2025 and 18 months of runway, we arrive at Q3 2026.

To raise their next round (Series B) and extend their life, AI visibility companies must justify the high valuation of their previous round. But to justify a Series A valuation (likely $50-$100 million post-money given the AI hype), they need to show roughly 3x-5x ARR growth year-over-year. Because the product is commoditized by free tools like Amplitude and bundled features from Semrush, they might miss that 5x revenue growth target.

Andrea Volpini, Founder and CEO of Wordlift:

After 25 years, the Semantic Web has finally arrived. The idea that agents can reach a shared understanding by exchanging ontologies and even bootstrap new reasoning capabilities is no longer theoretical. It is how the human-centered web is turning into an agentic, reasoning web while most of the industry is caught off guard. When Sir Tim Berners-Lee warns that LLMs may end up consuming the web instead of humans, he is signaling a seismic shift. It is bigger than AI Search. It is reshaping the business model that has powered the web for three decades. This AI Map is meant to show who is laying the foundations of the reasoning web and who is about to be left behind.

4. The Market Thesis: When $166 Billion Meets Behavioral Disruption

From Niccolo Sanarico, writer of The Week in Italian Startups and Partner at Primo Capital:

Let’s leave the funding data for a moment, and shift to the demand side of the market: on the one hand, Google integrating AI search results on its SERP, ChatGPT or Perplexity becoming the entry point for search and discovery, are phenomena that are creating a change in user behavior – and when users change behavior, new giants emerge. On the other hand, SEO has historically been a consulting-like, human-driven, tool-enabled effort, but its components (data monitoring & analysis, content ideation & creation, process automation) are the bread and butter of the current generation of AI, and we believe there is a huge space for emerging AI platforms to chip away at the consulting side of this business. Unsurprisingly, 42% of the companies in our dataset were founded on or after 2020, despite the oldest and greatest players dating back more than 20 years, and the key message they are passing is “let us do the work.”

The numbers validate this thesis at scale. Even though it is not always easy to size it, recent research finds that the SEO market represents a $166 billion opportunity split between tools ($84.94 billion) and services ($81.46 billion), growing at 13%+ annually. But the distribution reveals the disruption opportunity: agencies dominate with 55% market share in services, while 60% of enterprise spend flows to large consulting relationships. This $50+ billion consulting layer – built on manual processes, relationship-dependent expertise, and human-intensive workflows – sits directly in AI’s disruption path.

The workforce data tells the automation story. With >200,000 SEO professionals globally and median salaries in the US of $82,000 (15% above U.S. national average), we’re looking at a knowledge worker category ripe for productivity transformation. The job market shifts already signal this transition: content-focused SEO roles declined 28% in 2024 as AI automation eliminated routine work, while leadership positions grew 50-58% as the focus shifted to strategy and execution oversight. When 90% of new SEO positions come from companies with 250+ employees, and these organizations are simultaneously increasing AI tool budgets from 5% to 15% of total SEO spend, the path forward is clear: AI platforms that can deliver execution velocity will capture the value gap between high-cost consulting and lower-margin monitoring tools.

5. What This Means For You

For Tool Buyers

Stop asking “Is it AI-powered?” Ask instead:

Does this solve an operational constraint or just give me information? (If it’s information, Semrush will have it free in 18 months.)
Does this automate a workflow or create new manual work? (Sticky products are deeply integrated. Point solutions require babysitting.)
Can I get this from my existing platform eventually, or is this defensible? (If an established player can bundle it, they will.)

For Investors

You’re at an inflection point:

The narrative layer (monitoring) is collapsing in real-time.
The substance layer (execution) is still underfunded.
This gap closes fast.

When evaluating opportunities, ask: “What would need to happen for Semrush or Ahrefs to provide this?” If the answer is “not much,” it’s not defensible at venture scale. If they had to rebuild core infrastructure or cannibalize part of their product, you have a moat.

The best signal isn’t which companies are raising capital, but which categories are raising capital despite low defensibility. That’s where you find the upside.

For Builders

Your strategic question isn’t “Which category should I enter?” It’s “How deeply integrated will I be in my customers’ workflows?” If you’re building monitoring tools, you have 18 months. Either build an execution layer on top of your dashboard or optimize for acquisition.

If you’re building execution platforms, defensibility comes from three things:

Depth of integration in daily workflows
Required domain expertise
Operational leverage you provide relative to building in-house

The winning companies are those that solve problems needing continuous domain expertise and cannot be easily copied. Automated workflows that understand brand guidelines, customer segments, and channel-specific best practices aren’t.

Ask yourself: What operational constraint am I solving that requires judgment calls, not just better AI? If the answer is “I’m just generating better content faster,” you’re building a feature. If the answer is “I’m managing complexity across dozens of channels while enforcing consistency,” you’re building a platform.

Full infographic of our analysis:

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Featured Image: Paulo Bobita/Search Engine Journal

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Nov 27 2025

From Organic Search To AI Answers: How To Redesign SEO Content Workflows via @sejournal, @rio_seo

It’s officially the end of organic search as we know it. A recent survey reveals that 83% of consumers believe AI-powered search tools are more efficient than traditional search engines.

The days of simple search are long gone, and a profound transformation continues to sweep the search engine results pages (SERPs). The rise of AI-powered answer engines, from ChatGPT to Perplexity to Google’s AI Overviews, is rewriting the rules of online visibility.

Instead of returning traditional blue links or images, AI systems are returning immediate results. For marketing leaders, the question is no longer “How do we rank number one?” but rather “How do we become the top answer?”

This shift has eliminated the distance between the search and the solution. No longer do customers need to click through to find the information they’re seeking. And while zero-click searches are more prevalent and old metrics like keyword rankings are fading fast, it also creates a massive opportunity for chief marketing officers to redefine SEO as a strategic growth function.

Yes, content remains king, but it must be rooted in a foundation that fuels authority, brand trust, and authenticity to serve the systems that are shaping what appears when a search is conducted. This isn’t just a new channel; it’s a new way of creating, structuring, and validating content

In this post, we’ll dissect how to redesign content workflows for generative engines to ensure your content reigns supreme in an AI-first era.

What Generative Engines Changed And Why “Traditional SEO” Won’t Recover

When users ask generative search engines a question, they aren’t presented with a list of websites to click through to learn more; instead, they’re given a quick, synthesized answer. The source of the answer is cited, allowing users to click to learn more if they so choose to. These citations are the new “rankings” and most likely to be clicked on.

In fact, research shows 60% of consumers click through at least sometimes after seeing an AI-generated overview in Google Search. A separate study found that 91% of frequent AI users turn to popular large language models (LLMs) such as ChatGPT for their searching needs.

While keyword optimization still holds importance in content marketing, generative engines are favoring expertise, brand authority, and structured data. For CMOs, the old metrics no longer necessarily equate to success. Visibility and impressions are no longer tied to website traffic, and success is now contingent upon citations, mentions, and verifiable authority signals.

The AI era signals a serious identity shift, one in which traditional SEO collides with AI-driven search. SEO can no longer be a mechanical, straightforward checklist that sits under demand generation. It must integrate with a broader strategy to manage brand knowledge, ensuring that when AI pulls data to form an answer, your content is what they trust most out of all the options out there.

In this new search era, improving visibility can be measured in three diverse ways:

Appearing in results or answers.
Being seen as a thought leader in your space by being cited or trusted as a credible source.
Driving influence, affinity, or conversions from your digital presence.

Traditional SEO is now only one piece of the content visibility puzzle. Generative SEO demands fluency across all three.

The CMO’s New Dilemma: AI As Both Channel And Competitor

Consumers have questions. Generative engines have the answers. With over half (56%) of consumers trusting the use of Gen AI as an education resource, generative engines are now mediators between your brand and your customers. They can influence purchases or sway customers toward your competition, depending on whether your content earns their hard-earned trust.

For example, if a customer asks, “What’s the best CRM for enterprise brands?” and an AI engine suggests HubSpot’s content over your brand, the damage isn’t just a lost click but a missed opportunity to garner interest and trust with that motivated searcher. The hard truth is the Gen AI model didn’t see your content as relevant or reliable enough to deliver in its answer.

Generative engines are trained on content that already exists, meaning your competitors’ content, user reviews, forum discussions, and your own material are all fair game. That means AI is both a discovery channel and competitor for audience attention. This duality must be recognized by CMOs to invest in structuring, amplifying, and revamping content workflows to match Gen AI’s expectations. The goal isn’t to chase algorithms; it’s to shape the content in a meaningful way to ensure those algorithms trust and view your content as the single source of truth.

Think of it this way: Traditional SEO practices taught you to optimize content for crawlers. With Generative SEO, you’re optimizing for the model’s memory.

How To Redesign SEO Content Workflows For The Generative Era

To win citations and influence AI-generated answers, it’s time to throw out your old playbooks and overhaul previous workflows. It may be time to ditch how you used to plan content and how performance was measured. Out with the old and in with the new (and more successful).

From Keyword Targeting To Knowledge Modeling

Generative models go beyond understanding just keywords. They understand entities and relationships, too. To show up in coveted AI answers and to be the top choice, your content must reflect structured, interconnected knowledge.

Start by building a brand knowledge graph that maps people, products, and topics that define your expertise. Schema markup is also a must to show how these entities connect. Additionally, every piece of content you produce should reinforce your position within that network.

Long-tail keywords may be easier to target and rank for in traditional SEO; however, optimizing for AI search requires a shift in content workflows, one that targets “entity clusters” instead. Here’s what this might look like in practice: A software company wouldn’t only optimize content around the focus keyword phrase “best CRM integrations.” The writer should also define its relationship to the concept of “CRM,” “workflow automation,” “customer data,” and other related phrases.

From Content Volume To Verifiable Authority

It was once thought that the more content, the better. This is not the case with SEO today as AI systems prefer and prioritize content that’s well-sourced, attributable, and authoritative. Content velocity is no longer the end game, but rather producing stronger, more evidence-backed pieces.

Marketing leaders should create an AI-readiness checklist for their content marketing team to ensure every piece of content is optimized for generative engines. Every article should include author credentials (job title, advanced degrees, and certifications), clear citations (where the statistics or research came from), and verifiable claims.

Create an AI-readiness checklist for your team. Every article should include author credentials, clear citations, and verifiable claims. Reference independent studies and owned research where possible. AI models cross-validate multiple sources to determine what’s credible and reliable.

In short: Don’t publish faster. Publish smarter.

From Static Publishing To Dynamic Feedback

If one thing is certain, it’s that generative engines are continuing to evolve, similar to traditional search. What ranks well today may change entirely tomorrow. That’s why successful SEO teams are adopting an agile publishing cycle to continue to stay on top of what’s working best. SEO teams are actively and consistently:

Testing which questions their audience asks in generative engines.
Tracking whether their content appears in those answers.
Refreshing content based on what’s being cited, summarized, or ignored.

Several tools are emerging to help you track your brand’s presence across, ChatGPT, Perplexity, AI Overviews, and more, including SE Ranking, Peec AI, Profound, and Conductor. If you choose to forego tools, you can also run regular AI audits on your own to see how your brand is represented across engines by following the aforementioned framework. Treat that data like search console metrics and think of it as your new visibility report.

How To Measure SEO Success In An Answer-Driven World

Measuring SEO success across generative engines looks different than how we used to measure traditional SEO. Traffic will always matter, but it’s no longer the sole proof of impact. For CMOs, understanding how to measure marketing’s impact is essential to demonstrate the value your team delivers to the organization’s mission.

Here’s how progressive CMOs are redefining SEO success:

AI Citations: How often your content is referenced within AI-generated responses.
Answer Visibility Share: The percentage of relevant queries where your content appears in an AI answer.
Zero-Click Exposure: Instances where your brand is visible in AI responses, even if users don’t visit your site.
Answer Referral Traffic: The new “clicks”; visits that originate directly from AI-generated links.
Semantic Coverage: The breadth of related entities and subtopics your brand consistently appears for.

These metrics move SEO reporting from vanity numbers to visibility intelligence and are a more accurate representation of brand authority in the machine age.

Future-Proof Your SEO For Generative Search

Generative search is just as volatile as traditional search, but volatility is fertile ground for innovation. Instead of resisting it, CMOs should continue to treat SEO as an experimental function; a sandbox for continuously testing new ways to be discovered and trusted. SEO continues to remain a function that isn’t a set it and forget it, but one that must change with time and testing.

CMOs should encourage their team to A/B test content formats, schema implementations, and even phrasing to see what appears in AI generated responses. Cross-pollinate SEO insights with PR, product, and customer experience. When your organization learns how AI represents your brand, it becomes a feedback loop that strengthens everything from messaging to market positioning.

In the near future, the term “organic search” will become something broader to encompass the fast-growing ecosystem of machine-mediated discovery. The brands that succeed won’t just optimize for keywords. They’ll build long-lasting trust.

The Next Evolution Of Search

The notion that AI is killing SEO is false. AI isn’t eliminating SEO but rather redefining what it means today. What used to be a tactical discipline is shifting to become a more strategic approach that requires understanding how your brand exists within digital knowledge systems. It’s straying from what’s comfortable and moving into largely uncharted territory.

The opportunity for marketing leaders is clear: It’s time to move past the known and venture into the somewhat elusive realm of generative answer engines. After all, Forrester predicts AI-powered search will drive 20% of all organic traffic by the end of 2025. At the end of the day, many of the traditional SEO best practices still apply: create content that’s verifiable, well-structured, and context-rich. The main mindset shift lies in how to measure generative engine success, not by rankings but by relevance in conversation.

In the age of AI answers, your brand doesn’t need to just be searchable; it needs to be knowable.

More Resources:

Featured Image: Roman Samborskyi/Shutterstock

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Ai AI Hype Index App Artificial intelligence

Nov 27 2025

The AI Hype Index: The people can’t get enough of AI slop

Last year, the fantasy author Joanna Maciejewska went viral (if such a thing is still possible on X) with a post saying “I want AI to do my laundry and dishes so that I can do art and writing, not for AI to do my art and writing so that I can do my laundry and dishes.” Clearly, it struck a chord with the disaffected masses.

Regrettably, 18 months after Maciejewska’s post, the entertainment industry insists that machines should make art and artists should do laundry. The streaming platform Disney+ has plans to let its users generate their own content from its intellectual property instead of, y’know, paying humans to make some new Star Wars or Marvel movies.

Elsewhere, it seems AI-generated music is resonating with a depressingly large audience, given that the AI band Breaking Rust has topped Billboard’s Country Digital Song Sales chart. If the people demand AI slop, who are we to deny them?

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Nov 27 2025

The Download: AI and the economy, and slop for the masses

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

How AI is changing the economy

There’s a lot at stake when it comes to understanding how AI is changing the economy right now. Should we be pessimistic? Optimistic? Or is the situation too nuanced for that?

Hopefully, we can point you towards some answers. Mat Honan, our editor in chief, will hold a special subscriber-only Roundtables conversation with our editor at large David Rotman, and Richard Waters, Financial Times columnist, exploring what’s happening across different markets. Register here to join us at 1pm ET on Tuesday December 9.

The event is part of the Financial Times and MIT Technology Review “The State of AI” partnership, exploring the global impact of artificial intelligence. Over the past month, we’ve been running discussions between our journalists—sign up here to receive future editions every Monday.

If you’re interested in how AI is affecting the economy, take a look at:

+ People are worried that AI will take everyone’s jobs. We’ve been here before.

+ What will AI mean for economic inequality? If we’re not careful, we could see widening gaps within countries and between them. Read the full story.

+ Artificial intelligence could put us on the path to a booming economic future, but getting there will take some serious course corrections. Here’s how to fine-tune AI for prosperity.

The AI Hype Index: The people can’t get enough of AI slop

Separating AI reality from hyped-up fiction isn’t always easy. That’s why we’ve created the AI Hype Index—a simple, at-a-glance summary of everything you need to know about the state of the industry. Take a look at this month’s edition of the index here, featuring everything from replacing animal testing with AI to our story on why AGI should be viewed as a conspiracy theory.

MIT Technology Review Narrated: How to fix the internet

We all know the internet (well, social media) is broken. But it has also provided a haven for marginalized groups and a place for support. It offers information at times of crisis. It can connect you with long-lost friends. It can make you laugh.

That makes it worth fighting for. And yet, fixing online discourse is the definition of a hard problem.

This is our latest story to be turned into a MIT Technology Review Narrated podcast, which we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 How much AI investment is too much AI investment?
Tech companies hope to learn from beleaguered Intel. (WSJ $)
+ HP is pivoting to AI in the hopes of saving $1 billion a year. (The Guardian)
+ The European Central bank has accused tech investors of FOMO. (FT $)

2 ICE is outsourcing immigrant surveillance to private firms
It’s incentivizing contractors with multi-million dollar rewards. (Wired $)
+ Californian residents have been traumatized by recent raids. (The Guardian)
+ Another effort to track ICE raids was just taken offline. (MIT Technology Review)

3 Poland plans to use drones to defend its rail network from attack
It’s blaming Russia for a recent line explosion. (FT $)
+ This giant microwave may change the future of war. (MIT Technology Review)

4 ChatGPT could eventually have as many subscribers as Spotify
According to erm, OpenAI. (The Information $)

5 Here’s how your phone-checking habits could shape your daily life
You’re probably underestimating just how often you pick it up. (WP $)
+ How to log off. (MIT Technology Review)

6 Chinese drugs are coming
Its drugmakers are on the verge of making more money overseas than at home. (Economist $)

7 Uber is deploying fully driverless robotaxis in an Abu Dhabi island
Roaming 12 square miles of the popular tourist destination. (The Verge)
+ Tesla is hoping to double its robotaxi fleet in Austin next month. (Reuters)

8 Apple is set to become the world’s largest smartphone maker
After more than a decade in Samsung’s shadow. (Bloomberg $)

9 An AI teddy bear that discussed sexual topics is back on sale
But the Teddy Kumma toy is now powered by a different chatbot. (Bloomberg $)
+ AI toys are all the rage in China—and now they’re appearing on shelves in the US too. (MIT Technology Review)

10 How Stranger Things became the ultimate algorithmic TV show
Its creators mashed a load of pop culture references together and created a streaming phenomenon. (NYT $)

Quote of the day

“AI is a very powerful tool—it’s a hammer and that doesn’t mean everything is a nail.”

—Marketing consultant Ryan Bearden explains to the Wall Street Journal why it pays to be discerning when using AI.

One more thing

Are we ready to hand AI agents the keys?

In recent months, a new class of agents has arrived on the scene: ones built using large language models. Any action that can be captured by text—from playing a video game using written commands to running a social media account—is potentially within the purview of this type of system.

LLM agents don’t have much of a track record yet, but to hear CEOs tell it, they will transform the economy—and soon. Despite that, like chatbot LLMs, agents can be chaotic and unpredictable. Here’s what could happen as we try to integrate them into everything.

—Grace Huckins

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)

+ The entries for this year’s Nature inFocus Photography Awards are fantastic.
+ There’s nothing like a good karaoke sesh.
+ Happy heavenly birthday Tina Turner, who would have turned 86 years old today.
+ Stop the presses—the hotly-contested list of the world’s top 50 vineyards has officially been announced

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Nov 27 2025

New Ecommerce Tools: November 26, 2025

Every week we publish a handpicked list of new products and services for ecommerce merchants. This installment includes updates on product experience management, agentic commerce, AI-powered payment integration, fulfillment, alternative payments, customer support, website builders, and cross-platform ad campaigns.

Got an ecommerce product release? Email updates@practicalecommerce.com.

New Tools for Merchants

Brandfuel launches AI-native Product Experience Management platform. Brandfuel has announced the availability (out of beta) of its AI-native Product Experience Management platform for ecommerce brands and agencies. According to Brandfuel, the platform can capture a brand’s personas, competitors, and keywords — to guide personalized content creation — as well as automate image analysis, alt tags, and per-product competitor tracking. The platform features product content scoring, multi-language and multichannel support, automated A/B content testing, Klaviyo and Meta integrations, and more.

Brandfuel

OpenAI introduces shopping research in ChatGPT. OpenAI‘s new shopping research feature in ChatGPT helps consumers find the right products. Per OpenAI, the tool asks clarifying questions, reviews quality sources, and builds on ChatGPT’s understanding of a user from past conversations to deliver a personalized buyer’s guide. Shopping research is currently rolling out on mobile and web for logged-in ChatGPT users on Free, Go, Plus, and Pro plans.

Worldpay accelerates agentic commerce with Model Context Protocol. Worldpay, a financial technology and payment processing company, has launched Worldpay Model Context Protocol, a set of server specifications and tools designed to accelerate AI-powered payment integration and agentic commerce. Developers and merchants can download, modify, and deploy the protocol immediately to enable the rapid creation of AI agents and direct payment integrations with Worldpay’s API. Worldpay MCP is available on its Developer Hub and on GitHub.

Perplexity announces free tool to streamline online shopping. Perplexity, in partnership with PayPal, is rolling out a free agentic shopping product for U.S. users, who can purchase items from more than 5,000 merchants through the search engine. Perplexity says the new free product will be better than its paid shopping subscription at detecting shopping intent, resulting in more personalized results.

NIQ and Amazon Marketing Cloud partner on cross-platform ad campaigns in Italy. NIQ, a consumer intelligence company, and Amazon Marketing Cloud have announced a collaboration to study the effectiveness in Italy of cross-platform advertising across linear television and Amazon Ads inventory. Advertisers and agencies will gain actionable insights into the relative performance of ad placements across digital, linear TV, and streaming environments, including how each contributes to incremental reach and influences product purchases on Amazon’s ecommerce platforms. The project is part of Amazon Marketing Cloud’s Global Strategic Initiative.

NIQ

Ecommerce accelerator Pattern expands fulfillment solutions. Pattern Group, which accelerates brands on global ecommerce marketplaces, has expanded its portfolio of fulfillment and logistics services. Pattern now offers inbound transportation services, leveraging the company’s carrier relationships and transportation infrastructure. Pattern has expanded its reverse logistics capabilities to help businesses recover more value from returns. Pattern has also launched Reimbursements, an automated service that handles filing and tracking marketplace reimbursement claims, particularly on Amazon.

Integrated E.U. payment solution Unzer enables Wero for merchants. Unzer, a payments and software provider serving small and mid-sized businesses across Germany, Austria, Luxembourg, and the Nordics, has gone live with Wero, a new alternative payment solution for Europe-based consumers and merchants. Unzer and the European Payments Initiative, a service backed by 16 European banks and providers, are now inviting merchants to be among the first to adopt the digital payment method through Unzer’s integrated platform, UnzerOne.

Ordoro partners with ShipBob on ecommerce fulfillment. Ordoro, a provider of multichannel ecommerce operations software, has teamed up with ShipBob, a supply chain and fulfillment platform, to help small and mid-market omnichannel merchants find the proper fulfillment setup for their growth stage. According to the companies, merchants using Ordoro benefit from advanced inventory and shipping automation, while brands ready to scale can either outsource to ShipBob’s global fulfillment network or run their own U.S. warehouse using ShipBob’s warehouse management software.

Website builder Jimdo releases AI-powered Companion for small businesses. Jimdo, a Germany-based website builder specializing in solopreneurs, microbusinesses, and small ecommerce ventures, has launched Companion, an AI agent. Built into the Jimdo architecture, Companion provides personalized recommendations that drive visibility and transactions by analyzing each business’s performance history, industry benchmarks, and competitive landscape. Companion is available for Jimdo’s website customers at no extra cost across the U.S., U.K., Ireland, as well as Germany, Austria, and Switzerland.

Jimdo

Fermàt launches AI Search Commerce Engine. Fermàt Commerce, an AI-powered commerce platform for personalized shopping experiences, has launched AI Search Commerce Engine to help measure visibility, generate shoppable content, and drive transactions from answer engines, including ChatGPT, Claude, and Gemini. “Monitor Prompts” identifies high-value AI prompts using search engine data, marketing signals, product catalogs, and customer reviews. “Generate First-Party Content” automatically creates shoppable content optimized for large-language-model indexing. “Measure Visibility” tracks results with citation-level attribution, competitor benchmarking, and prompt expansion.

Znode announces enhanced Commerce Connector for B2B ecommerce. Znode, a B2B ecommerce platform, has announced an update to its Commerce Connector. The new release introduces Data Exchanges, expanding Znode’s native integration capabilities for connecting to enterprise systems. Data Exchanges handles real-time or scheduled data flows for products, pricing, inventory, customers, and orders. The update allows manufacturers and distributors to integrate Znode with ERP, CRM, PIM, and other business systems. Administrators gain visibility through configurable mapping and monitoring tools to reduce integration risk, according to Znode.

OpenAI and Target partner to bring AI-powered experiences across retail. Through its partnership with OpenAI, omnichannel retailer Target has announced that consumers can discover and shop Target products inside ChatGPT as a curated, conversational experience. Target is offering its shopping experience through an app in ChatGPT, allowing users to purchase multiple items in a single transaction, shop for fresh food products, and select drive-up, pickup, or shipping fulfillment options.

HappyFox launches Autopilot agentic AI platform for customer support teams. HappyFox, a customer service software provider, has launched Autopilot, an agentic AI platform that delivers pre-built agents for quick deployment. “Shopify Delivery Dispute Analyzer” investigates ecommerce delivery discrepancies between fulfillment status and customer claims. “Ticket Triage Agent” automatically categorizes and tags tickets. “Churn Risk Detector” analyzes SaaS customer conversations for signals of dissatisfaction. “Duplicate Ticket Notifier” identifies and flags potential duplicate tickets. Users can access outcome-based pricing and pay only when agents complete tasks, per HappyFox.

HappyFox

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