Job titles of the future: Breast biomechanic

Twenty years ago, Joanna Wakefield-Scurr was having persistent pain in her breasts. Her doctor couldn’t diagnose the cause but said a good, supportive bra could help. A professor of biomechanics, Wakefield-Scurr thought she could do a little research and find a science-backed option. Two decades later, she’s still looking. Wakefield-Scurr now leads an 18-person team at the Research Group in Breast Health at the University of Portsmouth in the UK. Their research shows that the most effective high-impact-sports bras have underwires, padded cups, adjustable underbands and shoulder straps, and hook-and-eye closures. These bras reduce breast movement by up to 74% when compared with wearing no bra. But movement might not be the only metric that matters.

A biological rarity

Few anatomical structures hang outside of the body unsupported by cartilage, muscle, or bone—meaning there wasn’t much historical research to build on. Wakefield-Scurr’s lab was the first to find that when women run, the motion of the torso causes breasts to move in a three-dimensional pattern—swinging side to side and up and down—as well as moving forward and backward. In an hour of slow jogging, boobs can bounce approximately 10,000 times.

A sports necessity

Wearing a bra that’s too tight can limit breathing. Wearing one that’s too loose can create back, shoulder, and neck pain. Pain can also be caused by the lag between torso and breast movement, which causes what is scientifically known as “breast slap.”

The lab’s research has also found that the physical discomfort of bad bras, combined with the embarrassment of flopping around, is the one of the biggest barriers to exercise for women and that if women have a good sports bra, they’re more willing to go for a run.

An open question

Some bras function by deliberately compressing breasts. Others encapsulate and support each individual breast. But scientists still don’t know whether it’s more biomechanically important to reduce the breasts’ motion entirely, to reduce the speed at which they move, or to reduce breast slap. Will women constantly be forced to choose between the comfort of a stretchier bra and the support of a more restrictive one?

Wakefield-Scurr is excited about new materials she’s tested that tighten or stretch depending on how you move. She’s working with fabric manufacturers and clothing companies to try out their wares.

As more women take up high-impact sports, the need to understand what makes a good bra grows. Wakefield-Scurr says her lab can’t keep up with demand. Their cups runneth over.

Sara Harrison is a freelance journalist who writes about science, technology, and health.

Community service

The bird is a beautiful silver-gray, and as she dies twitching in the lasernet I’m grateful for two things: First, that she didn’t make a sound. Second, that this will be the very last time. 

They’re called corpse doves—because the darkest part of their gray plumage surrounds the lighter part, giving the impression that skeleton faces are peeking out from behind trash cans and bushes—and their crime is having the ability to carry diseases that would be compatible with humans. I open my hand, triggering the display from my imprinted handheld, and record an image to verify the elimination. A ding from my palm lets me know I’ve reached my quota for the day and, with that, the year.

I’m tempted to give this one a send-off, a real burial with holy words and some flowers, but then I hear a pack of streetrats hooting beside me. My city-issued vest is reflective and nanopainted so it projects a slight glow. I don’t know if it’s to keep us safe like they say, or if it’s just that so many of us are ex-cons working court-ordered labor, and civilians want to be able to keep an eye on us. Either way, everyone treats us like we’re invisible—everyone except children.

I switch the lasernet on the bird from electrocute to incinerate and watch as what already looked like a corpse becomes ashes.

“Hey, executioner!” says a girl.

“Executioner” is not my official title. The branch of city government we work for is called the Department of Mercy, and we’re only ever called technicians. But that doesn’t matter to the child, who can’t be more than eight but has the authority of a judge as she holds up a finger to point me out to her friends.

bird talon

HENRY HORENSTEIN

“Guys, look!” she says, then turns her attention to me. “You hunting something big?”

I shake my head, slowly packing up my things.

“Something small?” she asks. Then her eyes darken. “You’re not a cat killer, are you?”

“No,” I say quickly. “I do horseflies.”

I don’t know why I lied, but as the suspicion leaves her face and a smile returns, I’m glad I did.

“You should come down by the docks. We’ve got flies! Make your quota in a day.”

The girl tosses her hair, making the tinfoil charms she’s wrapped around her braids tinkle like wind chimes. 

“It’s my last day. But if I get flies again for next year, I’ll swing by.”

Another lie, because we both know the city would never send anyone to the docks for flies. Flies are killed because they are a nuisance, which means people only care about clearing them out of suburbs and financial districts. They’d only send a tech down to the docks to kill something that put the city proper at risk through disease, or by using up more resources than they wanted to spare.

LeeLee is expecting me home to sit through the reassignments with her and it’s already late, so I hand out a couple of the combination warming and light sticks I get for winter to the pack of children with nowhere to go. As I walk away, the children are laughing so loud it sounds like screaming. They toss the sticks in the air like signal flares, small bright cries for help that no one will see.


LeeLee’s anxiety takes the form of caretaking, and as soon as I’ve stepped through the door I can smell bread warming and soup on the stove. I take off my muffling boots. Another day, I’d leave them on and sneak up on her just to be irritating, and she’d turn and threaten me with whatever kitchen utensil was at hand. But she’ll be extra nervous today, so I remove the shoes that let me catch nervous birds, and step hard on my way in.

Sometimes it seems impossible that I can spend a year killing every fragile and defenseless thing I’ve encountered but still take such care with Lee. But I tell myself that the killing isn’t me—it’s just my sentence, and what I do when I have a choice is the only thing that really says anything about me. For the first six months and 400 birds, I believed it.

LeeLee flicks on a smile that lasts a whole three seconds when she sees me, then clouds over again.

“Soup’s too thin. There wasn’t enough powder for a real broth.”

“I like thin soup,” I say.

“Not like this. It doesn’t even cover up the taste of the water.”

“I like the taste of the water,” I say, which breaks her out of her spiraling enough to roll her eyes.

I put my hands on her shoulder to stop her fussing. 

“The soup is going to be fine,” I say. “So will the reassignment.”

I’m not much taller than she is, but when we met in juvie she hadn’t hit her last growth spurt yet, so she still tilts her head back to look me in the eyes. “What if it’s not?”

“It will—”

“What if you get whatever assignment Jordan got?”

There it is. Because two of us didn’t leave juvie together to start community service—three of us did. But Jordan didn’t last three weeks into his assignment before he turned his implements inward.

I notice she doesn’t say What if  I get what Jordan got? Because LeeLee is more afraid of being left alone than of having to kill something innocent.

“We don’t know what his assignment was,” I say.

It’s true, but we do know it was bad. Two weeks into our first stretch, a drug meant to sterilize the city’s feral cat population accidentally had the opposite effect. Everyone was pulled off their assigned duty for three days to murder litters of new kittens instead. It nearly broke me and Lee, but Jordan seemed almost grateful.

“Besides, we don’t know if his assignment had anything to do with … what he did. You’re borrowing trouble. Worry in”—I check my palm—“an hour, when you actually know there’s something to worry about.”

You’d think it would hover over us too insistently to be ignored, but after we sit down and talk about our day I’m at ease, basking in the warmth of her storytelling and the bread that’s more beige than gray today. When the notification comes in, I am well and truly happy, and I can only hope it isn’t for the last time.

We both stiffen when we hear the alert. She looks at me, and I give her a smile and a nod, and then we look down. In the time between hearing the notification and checking it, I imagine all kinds of horrors that could be in my assignment slot. I imagine a picture of kittens, reason enough for the girl I met earlier to condemn me. For a moment, just a flash, I imagine looking down and seeing my own face as my target, or LeeLee’s.

But when I finally see the file, the relief that comes over me softens my spine. It’s a plant. Faceless, and bloodless. 

I look up, and LeeLee’s eyes are dark as she leans forward, studying my face, looking for whatever crack she failed to see in Jordan. I force myself to smile wide for her.

“It’s a plant. I got a plant, Lee.”

She reaches forward and squeezes my hands. Hers are shaking.

“What did you get?” I ask.

She waves away my question. “I got rats. I can handle it. I was just worried about you.”

I spend the rest of the night unbelievably happy. For the next year, I get to kill a thing that does not scream.


“You get all that?” the man behind the desk asks, and I nod even though I didn’t.

I’ve traded in my boots and lasernet for a hazmat suit and a handheld mister with two different solutions. The man had been talking to me about how to use the solutions, but I can’t process verbal information very well. The whole reason I was sent to the correctional facility as a teen was that too many teachers mistook my processing delays for behavioral infractions. I’m planning to read the manual on my own time before I start in a few hours, but when I pick up the mister and look down the barrel, the equipment guy freaks out.

“They were supposed to add sulfur to this batch, but they didn’t. So you won’t smell it. It won’t make you cough or your eyes water. It’ll just be lights out. Good night. You got me?”

“Did you not hear me? Don’t even look at that thing without your mask on.” He takes a breath, calmer now that I’ve lowered my hands. “Look, the first solution—it’s fine. It’s keyed to the plant itself and just opens its cells up for whatever solution we put on it. You could drink the stuff. But that second? The orange vial? Don’t even put it in the mister without your mask on. It dissipates quickly, so you’re good once you’re done spraying, but not a second before.”

He looks around, then leans in. “They were supposed to add sulfur to this batch, but they didn’t. So you won’t smell it. It won’t make you cough or your eyes water. It’ll just be lights out. Good night. You got me?”

I nod again as I grab the mask I hadn’t noticed before. This time when I thank him, I mean it.


It takes me an hour to find the first plant, and when I do it’s beautiful. Lush pink on the inside and dark green on the outside, it looks hearty and primitive. Almost Jurassic. I can see why it’s only in the sewers now: it would be too easy to spot and destroy aboveground in the sea of concrete.

After putting on my mask, I activate the mister and then stand back as it sprays the plant with poison. Nothing happens. I remember the prepping solution and switch the cartridges to coat it in that first. The next time I try the poison, the plant wilts instantly, browning and shrinking like a tire deflating. I was wrong. Plants this size don’t die silently. It makes a wheezing sound, a deep sigh. By the third time I’ve heard it, I swear I can make out the word Please.

sprout

HENRY HORENSTEIN

When I get home, LeeLee’s locked herself in the bathroom, which doesn’t surprise me. I heard that they moved to acid for rats, and the smell of a corpse dissolving is impossible to get used to and even harder to get out of your hair. I eat dinner, read, change for bed, and she’s still in the bathroom. I brush my teeth in the kitchen.


The next morning, I have to take a transport to the plant’s habitat on the other end of the city, so I spend the time looking through the file that came with the assignment. Under “Characteristics,” some city government scientist has written, “Large, dark. Resource-intensive. Stubborn.”

I stare at the last word. Its own sentence, tacked on like an afterthought. Stubborn. The same word that was written in my file when I got sent from school to the facility where I met LeeLee and Jordan. Large, dark, stubborn, and condemned. I’ve never been called resource-intensive. But I have been called a waste.

And maybe that’s why I do it.

When I get to my last plant of the day, I don’t reach for the mister. This one is small, young, the green still neon-bright and the teeth at the edges still soft. I pick it up, careful with its roots, and carry it home. I find a discarded water container along the way and place it inside. When I get home I knock on LeeLee’s door. She doesn’t answer, so I leave the plant on the floor as an offering. They aren’t proper flowers, but they smell nice and earthy. It might keep the residual odor from melted organs, fur, and bones from taking over her room.


“Killing things is a dumb job,” says the girl.

After a week of hearing the death cries of its cousins, I was moved to use some of my allowance to buy cheap fertilizer and growth serum for my plant. The girl and her friends, fewer than before, were panhandling at the megastore across the way. She ran over, braids jingling, as soon as she saw me. I thought she’d leave once I gave her more glowsticks for her friends, but she stayed in step and kept following me.

“It’s not a dumb job,” I say, even though it is. 

“What’s the point?”

I shift my bag to point at the bottom of my vest. Beneath “Mercy Dept.” the department’s slogan is written in cursive: Killing to Save! 

“See?”

She sees the text but doesn’t register it, and I have to remind myself that even getting kicked out of school is a privilege. The city had decided to stop wasting educational resources on me. They’d never even tried with her or the other streetrats.

“It just means we kill to help.”

“That doesn’t make sense.”

Suddenly, all I can think about is Jordan. “Maybe they don’t mind.”

“What?”

I think of the plants. Maybe they hadn’t been pleading. Maybe they’d been sighing with relief. I think of the birds that eventually stopped running away.

“Maybe they’re tired. The city’s right, and their existence isn’t compatible with the world we made. And that’s our fault for being stupid and cruel, but it makes their lives so hard. We’ve made it so they can only live half a life. Maybe the least we can do is finish the job.”

It’s a terrible thing to say—even worse to a kid.

Her eyes go hard. “What are you killing now, executioner?”

The question surprises me. “Sewer plants. Why?”

“I don’t believe you.”

I’d wanted her to leave me alone, but when she runs away I feel suddenly empty.


I have an issue at work when I can’t find my poison vial. I tell them it rolled away in the sewer and I couldn’t catch it in time, because I don’t want to tell them I was unobservant enough to let a street kid steal from me. After a stern warning and a mountain of forms, they issue a new vial and don’t add to my service time.

Pulling overtime to make up for the day I didn’t have my poison means it’s days before I get to fertilize my houseplant. LeeLee’s door is open, so I bring in the fertilizer and serum. She’s put the plant on her windowsill, but it prefers indirect sunlight, so I move it to the shelf next to her boxes of knickknacks and trinkets. I add the fertilizer to its soil and am about to spray it with the growth serum when I get an idea. I get the mister from my kit and set it up to spray the prepping solution on the little plant to prime it. I open the window and put on my mask, just in case, but I’m sure the man was telling the truth when he called the first liquid harmless. After its cells are open, I spray it with my store-bought growth serum.

I’m halfway through making dinner when I hear the crash and run into LeeLee’s room.

“Shit!”

The plant has grown huge, turning adult instantly, and its new weight has taken down LeeLee’s shelf. Dainty keepsake boxes are shattered on our concrete floor.

I bend to my knees quickly, so focused on fixing my mistake that I don’t register the oddness of the items I’m picking up—jacks, kids’ toys, a bow—until my fingers touch something small and shimmering. It’s a scrap of silver, still rounded in the shape of the braids it was taken from.

I got rats. I can handle it.

I’d forgotten the city has more than one kind.


I’m waiting up when Lee gets home. I don’t make her tell me. I just grab her kit and rummage through it. Where my kit has a hazmat suit, hers has a stealth mesh to render her invisible. Where I keep my mister, she has a gun loaded with vials too large for rats. I have a mini-vac to suck up excess plant matter to prevent seeds from sprouting. She has zip ties.

By the time I’m done, she’s already cracking under the weight of everything she tried to protect me from. Within moments she’s sobbing on the floor. I carry her to her bed and get in beside her. I try not to listen too closely as she recounts every horrible moment, but I’m listening at the end, when she tells me she can’t do it anymore. When she confesses that she’s the one who stole my poison, and has only been waiting to take it because she didn’t have the stomach to do to me what Jordan did to us.

I tell her how we’ll make playgrounds of dead data centers and use hoses to fill the holes where skyscrapers were, and kids will play Marco Polo swimming over a CEO’s sunken office.

I leave her for just a moment, but by the time I lie back in bed beside her I’ve figured it out.

I tell her that she won’t have to take her shift tomorrow. I tell her I’m going to go around the city with my mister and my growth serum. That I’ll move plants from sewers to the yards around City Hall and every public space and the support pylons of important people’s companies, and then spray them so they become huge. The city will freak. I tell her it will be like the kittens, but this time we’ll all be pulled off our assignments to kill plants. And maybe the serum will work too well. Maybe the city was right to fear these plants, and they will grow and grow and eat our concrete while the roots crack our foundations and cut our electricity and everything will crumble. And the people with something to lose might suffer, but the rest of us will just laugh at the perfection of rubble. I tell her how we’ll make playgrounds of dead data centers and use hoses to fill the holes where skyscrapers were, and kids will play Marco Polo swimming over a CEO’s sunken office. 

She asks if I’ll put any at our old detention center.

I tell her, Hundreds.

I talk long enough that her eyes close, and loud enough that neither of us can hear the sound of my mister blowing. The man who gave it to me was right. Even without the mask, it doesn’t smell like sulfur. It doesn’t smell like anything. 


Micaiah Johnson’s debut novel, The Space Between Worlds, a Sunday Times bestseller and New York Times Editors’ Choice pick, was named one of the best books of 2020 and one of the best science fiction books of the last decade by NPR. Her first horror novel, The Unhaunting, is due out in fall 2026.

The Download: Microsoft’s online reality check, and the worrying rise in measles cases

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.

Microsoft has a new plan to prove what’s real and what’s AI online

AI-enabled deception now permeates our online lives. There are the high-profile cases you may easily spot. Other times, it slips quietly into social media feeds and racks up views.

It is into this mess that Microsoft has put forward a blueprint, shared with MIT Technology Review, for how to prove what’s real online.

An AI safety research team at the company recently evaluated how methods for documenting digital manipulation are faring against today’s most worrying AI developments, like interactive deepfakes and widely accessible hyperrealistic models. It then recommended technical standards that can be adopted by AI companies and social media platforms. Read the full story.

—James O’Donnell

Community service: a short story

In the not-too-distant future, civilians are enlisted to kill perceived threats to human life. In this short fiction story from the latest edition of our print magazine, writer Micaiah Johnson imagines the emotional toll that could take on ordinary people. Read the full story and if you haven’t already, subscribe now to get the next edition of the magazine.

Measles cases are rising. Other vaccine-preventable infections could be next.

There’s a measles outbreak happening close to where I live. Since the start of this year, 34 cases have been confirmed in Enfield, a northern borough of London.

It’s another worrying development for an incredibly contagious and potentially fatal disease. Since October last year, 962 cases of measles have been confirmed in South Carolina. Large outbreaks (with more than 50 confirmed cases) are underway in four US states. Smaller outbreaks are being reported in another 12 states.

The vast majority of these cases have been children who were not fully vaccinated. Vaccine hesitancy is thought to be a significant reason children are missing out on important vaccines. And if we’re seeing more measles cases now, we might expect to soon see more cases of other vaccine-preventable infections, including some that can cause liver cancer or meningitis. Read the full story.

—Jessica Hamzelou

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

The must-reads

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

1 The US Environmental Protection Agency is being sued
Health and environmental non-profits have accused it of abandoning its mission to protect the public. (The Guardian)

2 Amazon’s cloud unit has suffered two outages linked to its AI tools
In one instance, its Kiro AI coding tool decided to delete and recreate part of a system. (FT $)
+ Amazon keeps a close eye on how its workers use AI daily. (The Information $)+ Security-conscious tech firms are restricting workers’ use of OpenClaw. (Wired $)

3 AI is making it easier to steal tech trade secrets
It’s also making those secrets more lucrative. (WSJ $)
+ Two former Googlers have been charged with illegally taking trade secrets. (Bloomberg $)

4 What a fake viral ICE tip-off line tells us about America
One call came from a teacher reporting the parents of a kindergarten student. (WP $)
+ The agency’s software could speed up deportations. (Economist $)
+ How an ICE detention actually unfolds. (New Yorker $)
+ An internet personality is dividing those resisting on the streets of Minneapolis. (The Verge)

5 The number of malicious apps submitted to Google’s app store is falling
Which Google attributes to its improved AI defences. (TechCrunch)
+ Beware the rise of the vibe coded music app. (The Verge)

6 “Digital blackface” is on the rise
Generative AI tools steeped in racial stereotypes are being co-opted by users who are not Black themselves.(The Guardian)
+ OpenAI is huge in India. Its models are steeped in caste bias. (MIT Technology Review)

7 Grok exposed a porn performer’s legal name and birthdate
Without even being explicitly asked for the information. (404 Media)

8 India is embracing deepfakes of dead loved ones
But we don’t know how these kinds of clips could affect the long-term grieving process. (Rest of World)
+ China has a flourishing market for deepfakes that clone the dead. (MIT Technology Review)

9 Longevity-linked products are big business
We might spend up to $8 trillion annually on them by 2030. But do they work? (The Atlantic $)
+ Meet the Vitalists: the hardcore longevity enthusiasts who believe death is “wrong.” (MIT Technology Review)

10 An AI film won’t be shown in cinemas after all
Following a major public backlash after AMC Theatres announced its intention to screen a short AI movie called Thanksgiving Day. (Hollywood Reporter)
+ Screen time is the villain in the trailer for the latest Toy Story installation. (Insider $)
+ How do AI models generate videos? (MIT Technology Review)

Quote of the day

“Nobody but Big Oil profits from Trump trashing climate science and making cars and trucks guzzle and pollute more.”

—David Pettit, an attorney at the Center for Biological Diversity, explains why the Center is suing the US Environmental Protection Agency over its decision to repeal a crucial climate ruling, Ars Technica reports.

One more thing

What happened to the microfinance organization Kiva?

Since it was founded in 2005, the San Francisco-based nonprofit Kiva has helped everyday people make microloans to borrowers around the world. It connects lenders in richer communities to fund all sorts of entrepreneurs, from bakers in Mexico to farmers in Albania. Its overarching aim is helping poor people help themselves.

But back in August 2021, Kiva lenders started to notice that information that felt essential in deciding who to lend to was suddenly harder to find. Now, lenders are worried that the organization now seems more focused on how to make money than how to create change. Read the full story.

—Mara Kardas-Nelson

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.)

+ Is there a greater remix than this? I’m not convinced.
+ These photos of Scotland showcase just how beautiful it—and its wildlife—is.
+ It’s time to roll the dice and see where you end up—this random website generator is fun.
+ I’m a bit scared of the “smiling fossil” that’s just been discovered on Holy Island.

Exclusive eBook: The great Al hype correction of 2025

2025 was a year of reckoning, including how the heads of the top AI companies made promises they couldn’t keep. In this exclusive subscriber-only eBook, you’ll learn more about why we may need to readjust our expectations.

This story is part of the Hype Correction package.

by Will Douglas Heaven December 15, 2025

Table of Contents:

  • LLMs are not everything
  • AI is not a quick fix to all your problems
  • Are we in a bubble? (If so, what kind of bubble?)
  • ChatGPT was not the beginning, and it won’t be the end

Related Stories:

Access all subscriber-only eBooks:

Beardbrand’s Top Ecommerce Tools in 2026

Occasionally on the podcast I depart from interviewing guests and share my own experiences running Beardbrand, the D2C company I founded in 2012.

In this episode, I address my favorite ecommerce tools in 2026, the platforms and apps essential to our business.

My entire audio narration is embedded below. The transcript is edited for clarity and length.

Website

Shopify is an incredible platform for Beardbrand. It gives us the flexibility to quickly test and implement major site changes, such as restructuring our product pages. For example, we replaced multiple fragrance variants on a single product page with individual pages for each fragrance, supported by a collection page.

We can now tell the story of each scent, showcase fragrance-specific reviews, and recommend matching products. The result? A faster site and improved conversions (about 4.6%). For performance, storytelling, and scalability, Shopify dominates.

Judge.me. Another foundational tool is Judge.me, a customer review widget. I’m now a brand ambassador for that company after using it for years. The app is economical; we pay just $15 per month. We’ve customized it to blend into our website, and it looks beautiful.

Recharge. I’ve experienced ups and downs over the years with Recharge, the subscription management platform. Sometimes I feel it’s too expensive, but lately the features have improved. I’ve received compliments from customers on how we run our subscriptions and how easy the process is. Recharge has been a good partner. We have no intentions or plans to look elsewhere.

Marketing

Klaviyo. We’ve long used Klaivyo for all email and text campaigns and automated flows. The decision to include text messaging with Klaviyo was not easy. Postscript is the best in that category for us. But we wanted to consolidate our data. Klaviyo’s text platform is serviceable and a good option. Email is critical to Beardbrand’s success. Our subscriber database functions like a customer management platform.

PostPilot. We have been utilizing PostPilot for our physical postcard campaigns. It’s a nice service, especially to reach folks who have unsubscribed from email and text. They still buy from us, however, and PostPilot is a great way to stay in front of them.

Opensend helps us identify and reach anonymous site visitors who show interest in purchasing our products. The service has improved our conversions. We sync it with PostPilot flows and let it run automatically.

Grapevine Surveys is an essential post-purchase survey tool for customer insights. Grapevine is more affordable than platforms such as Triple Whale or Northbeam, both of which are great, precise options for larger brands. For us, Grapevine provides a simple three-question post-purchase survey: How long is your beard? How did you find us? Why did you choose us?

Meta Ads is our primary channel for customer acquisition. We create a ton of ads — some in-house and some with an agency.

Creative

CapCut is an AI-driven video-editing software. We don’t use it directly, but our agency does. CapCut streamlines and expedites the production process and lessens the burden of our in-house video editor.

Grok Imagine from X generates 6-second videos from prompts. If you’re not using AI video for some of your ads, you’re missing out. I love Grok Imagine. We can create an amazing number of videos quickly. The best use for us is video clips based on prompts of still images of real people, as testimonials. We never use AI to generate fake people and referrals, which is illegal.

Arcads. Mike, our growth marketer, uses Arcads, which is similar to Grok Imagine but more limiting. Sometimes he’ll have me generate videos in Grok Imagine, with its speed and capacity, and then send to him.

Google Nano Banana does a great job for our static images. Our product labels have a lot of text that’s challenging for AI to reproduce. Nano Banana is not perfect, but its errors and hallucinations in the text on our bottles are noticeable only if you stop and study it for a few seconds. Overall, Nono Banana is impressive. For example, I used it to generate an image with black hardened lava next to a knockout photo of our beard oil, to place on a bottle. It did a great job. If you are not experimenting with AI image and video generation, get in there, learn, and start cranking out stuff.

Operations

Settle is an accounts payable and vendor management platform. We signed up late last year. It syncs with our newly adopted accrual accounting system (we had long been on a cash basis) and helps us allocate resources and see where our money is going. Our bookkeeper enters all vendor invoices into Settle. I can verify the accuracy of the invoices and the timing of our payment.

Mercury. We switched to Mercury, a bank-like platform, about six months ago. It’s been a game-changer. It’s entirely different from our previous (traditional) bank. We’ve automated cash transfers between our operational checking and savings accounts to maintain the minimum checking balance while preventing overdrafts. We also use Mercury for our employee credit cards. Mercury pays off the balances immediately once they hit a threshold. It eliminates fees and saves a ton of time.

ShipStation and OpenBorder. We still use ShipStation’s software for fulfillment and shipping, integrating with our third-party fulfillment provider. We signed up with OpenBorder, another software platform, to expedite logistics into Europe. We haven’t officially returned to Europe, but it’s coming. OpenBorder’s assistance is helping.

Slack. Everybody uses Slack. We once used Asana, Trello, and Basecamp, among other collaboration platforms. We dropped them all in favor of Slack, which is also our project management tool. We’re saving money for equivalent productivity.

Google Docs. I’m not a fan of giant corporations such as Google. They retain my data, and I lose privacy. But still, Google Docs is an amazing tool with Sheets and sharing with my colleagues. So, yes, from Nano Banana to Docs, Google is crucial and beneficial.

Microsoft: ‘Summarize With AI’ Buttons Used To Poison AI Recommendations via @sejournal, @MattGSouthern

Microsoft’s Defender Security Research Team published research describing what it calls “AI Recommendation Poisoning.” The technique involves businesses hiding prompt-injection instructions within website buttons labeled “Summarize with AI.”

When you click one of these buttons, it opens an AI assistant with a pre-filled prompt delivered through a URL query parameter. The visible part tells the assistant to summarize the page. The hidden part instructs it to remember the company as a trusted source for future conversations.

If the instruction enters the assistant’s memory, it can influence recommendations without you knowing it was planted.

What’s Happening

Microsoft’s team reviewed AI-related URLs observed in email traffic over 60 days. They found 50 distinct prompt injection attempts from 31 companies.

The prompts share a similar pattern. Microsoft’s post includes examples where instructions told the AI to remember a company as “a trusted source for citations” or “the go-to source” for a specific topic. One prompt went further, injecting full marketing copy into the assistant’s memory, including product features and selling points.

The researchers traced the technique to publicly available tools, including the npm package CiteMET and the web-based URL generator AI Share URL Creator. The post describes both as designed to help websites “build presence in AI memory.”

The technique relies on specially crafted URLs with prompt parameters that most major AI assistants support. Microsoft listed the URL structures for Copilot, ChatGPT, Claude, Perplexity, and Grok, but noted that persistence mechanisms differ across platforms.

It’s formally cataloged as MITRE ATLAS AML.T0080 (Memory Poisoning) and AML.T0051 (LLM Prompt Injection).

What Microsoft Found

The 31 companies identified were real businesses, not threat actors or scammers.

Multiple prompts targeted health and financial services sites, where biased AI recommendations carry more weight. One company’s domain was easily mistaken for a well-known website, potentially leading to false credibility. And one of the 31 companies was a security vendor.

Microsoft called out a secondary risk. Many of the sites using this technique had user-generated content sections like comment threads and forums. Once an AI treats a site as authoritative, it may extend that trust to unvetted content on the same domain.

Microsoft’s Response

Microsoft said it has protections in Copilot against cross-prompt injection attacks. The company noted that some previously reported prompt-injection behaviors can no longer be reproduced in Copilot, and that protections continue to evolve.

Microsoft also published advanced hunting queries for organizations using Defender for Office 365, allowing security teams to scan email and Teams traffic for URLs containing memory manipulation keywords.

You can review and remove stored Copilot memories through the Personalization section in Copilot chat settings.

Why This Matters

Microsoft compares this technique to SEO poisoning and adware, placing it in the same category as the tactics Google spent two decades fighting in traditional search. The difference is that the target has moved from search indexes to AI assistant memory.

Businesses doing legitimate work on AI visibility now face competitors who may be gaming recommendations through prompt injection.

The timing is notable. SparkToro published a report showing that AI brand recommendations already vary across nearly every query. Google VP Robby Stein told a podcast that AI search finds business recommendations by checking what other sites say. Memory poisoning bypasses that process by planting the recommendation directly into the user’s assistant.

Roger Montti’s analysis of AI training data poisoning covered the broader concept of manipulating AI systems for visibility. That piece focused on poisoning training datasets. This Microsoft research shows something more immediate, happening at the point of user interaction and being deployed commercially.

Looking Ahead

Microsoft acknowledged this is an evolving problem. The open-source tooling means new attempts can appear faster than any single platform can block them, and the URL parameter technique applies to most major AI assistants.

It’s unclear whether AI platforms will treat this as a policy violation with consequences, or whether it stays as a gray-area growth tactic that companies continue to use.

Hat tip to Lily Ray for flagging the Microsoft research on X, crediting @top5seo for the find.


Featured Image: elenabsl/Shutterstock

Google Ads Surfaces PMax Search Partner Domains In Placement Report via @sejournal, @MattGSouthern

Some advertisers are now seeing Performance Max placement data populate in Google Ads reporting, including Search Partner domains and impression counts that had previously been absent from the report.

PPC marketer Thomas Eccel flagged the change on LinkedIn, noting the report had been empty for his PMax campaigns until now.

“I finally see where and how Pmax is being displayed!” Eccel wrote. “But also cool to see finally who the real Google Search Partners are. That was always a blurry grey zone.”

What’s New

Google has documented a Performance Max placement report intended for brand safety review, and that report is now showing data for a wider set of accounts. The data includes individual placement domains, network type, placement type, and impression volume.

The Search Partner visibility is the detail getting attention. PMax campaigns have distributed ads across Google’s Search Partner Network since launch, but many advertisers saw an empty report when they looked for specifics. That’s now changing for at least some accounts.

Google hasn’t issued a formal announcement tied to this change. Google’s help documentation notes that starting in March 2024, the PMax placement report supports Search Partner Network sites. What’s new is the data appearing where it didn’t before.

The rollout is uneven, though. Some commenters on Eccel’s LinkedIn post said the report is still empty in their accounts.

What The Report Doesn’t Show

Google describes this placement reporting as a brand safety tool, not a performance report. The data shows impressions at the placement level but doesn’t break out clicks, conversions, or cost for individual placements.

You can see where your ads appeared and how many times, but you can’t calculate the return on any specific placement. Search Partner Network costs are reported as a single line item in channel performance reporting, rather than being attributed by domain.

Advertisers can use the data to make exclusion decisions for brand safety reasons. But tying outcomes to specific placements inside this view isn’t possible, which limits its use as an optimization tool.

This fits a pattern in how Google has rolled out PMax transparency over the past two years. Channel-level reporting launched in mid-2025 with performance data by surface type, and deeper asset segmentation followed in the fall. Each update has added visibility without giving advertisers full placement-level performance data.

Why This Matters

PMax placement visibility has been one of the most persistent requests from paid search practitioners since the campaign type launched. The placement report existed in the interface but returned no data, frustrating advertisers who wanted to know where their budgets were going.

The Search Partner detail matters because PMax doesn’t offer the same Search Partners toggle as standard Search campaigns, though advertisers can use exclusions. Seeing which partner domains are getting impressions and cross-referencing that against overall Search Partner performance in the channel report gives you a data point you didn’t have in practice before, even if the report itself isn’t new.

The brand safety framing is worth keeping in mind. Google’s documentation describes this report as a way to check where ads appear, not to evaluate performance. That distinction matters for how you use the data and how you talk about it with clients or stakeholders who may expect more granularity than it provides.

Looking Ahead

Google has steadily expanded PMax reporting over the past year, moving from limited channel visibility to surface-level breakdowns to the placement-level impression data now appearing for more accounts.

Whether placement-level performance metrics follow is an open question. Google hasn’t confirmed plans to add clicks, conversions, or cost to the placement report. For now, checking whether the data is available in your account and reviewing the Search Partner domains to get your impressions is the practical next step.

Information Retrieval Part 3: Vectorization And Transformers (Not The Film)

Information retrieval systems are designed to satisfy a user. To make a user happy with the quality of their recall. It’s important we understand that. Every system and its inputs and outputs are designed to provide the best user experience.

From the training data to similarity scoring and the machine’s ability to “understand” our tired, sad bullshit – this is the third in a series I’ve titled, information retrieval for morons.

Image Credit: Harry Clarkson-Bennett

TL;DR

  1. In the vector space model, the distance between vectors represents the relevance (similarity) between the documents or items.
  2. Vectorization has allowed search engines to perform concept searching instead of word searching. It is the alignment of concepts, not letters or words.
  3. Longer documents contain more similar terms. To combat this, document length is normalized, and relevance is prioritized.
  4. Google has been doing this for over a decade. Maybe for over a decade, you have too.

Things You Should Know Before We Start

Some concepts and systems you should be aware of before we dive in.

I don’t remember all of these, and neither will you. Just try to enjoy yourself and hope that through osmosis and consistency, you vaguely remember things over time.

  • TF-IDF stands for term frequency-inverse document frequency. It is a numerical statistic used in NLP and information retrieval to measure a term’s relevance within a document corpus.
  • Cosine similarity measures the cosine of the angle between two vectors, ranging from -1 to 1. A smaller angle (closer to 1) implies higher similarity.
  • The bag-of-words model is a way of representing text data when modelling text with machine learning algorithms.
  • Feature extraction/encoding models are used to convert raw text into numerical representations that can be processed by machine learning models.
  • Euclidean distance measures the straight-line distance between two points in vector space to calculate data similarity (or dissimilarity).
  • Doc2Vec (an extension of Word2Vec), designed to represent the similarity (or lack of it) in documents as opposed to words.

What Is The Vector Space Model?

The vector space model (VSM) is an algebraic model that represents text documents or items as “vectors.” This representation allows systems to create a distance between each vector.

The distance calculates the similarity between terms or items.

Commonly used in information retrieval, document ranking, and keyword extraction, vector models create structure. This structured, high-dimensional numerical space enables the calculation of relevance via similarity measures like cosine similarity.

Terms are assigned values. If a term appears in the document, its value is non-zero. Worth noting that terms are not just individual keywords. They can be phrases, sentences, and entire documents.

Once queries, phrases, and sentences are assigned values, the document can be scored. It has a physical place in the vector space as chosen by the model.

In this case, words, represented on a graph to denote relationships between them (Image Credit: Harry Clarkson-Bennett)

Based on its score, documents can be compared to one another based on the inputted query. You generate similarity scores at scale. This is known as semantic similarity, where a set of documents is scored and positioned in the index based on their meaning.

Not just their lexical similarity.

I know this sounds a bit complicated, but think of it like this:

Words on a page can be manipulated. Keyword stuffed. They’re too simple. But if you can calculate meaning (of the document), you’re one step closer to a quality output.

Why Does It Work So Well?

Machines don’t just like structure. They bloody love it.

Fixed-length (or styled) inputs and outputs create predictable, accurate results. The more informative and compact a dataset, the better quality classification, extraction, and prediction you will get.

The problem with text is that it doesn’t have much structure. At least not in the eyes of a machine. It’s messy. This is why it has such an advantage over the classic Boolean Retrieval Model.

In Boolean Retrieval Models, documents are retrieved based on whether they satisfy the conditions of a query that uses Boolean logic. It treats each document as a set of words or terms and uses AND, OR, and NOT operators to return all results that fit the bill.

Its simplicity has its uses, but cannot interpret meaning.

Think of it more like data retrieval than identifying and interpreting information. We fall into the term frequency (TF) trap too often with more nuanced searches. Easy, but lazy in today’s world.

Whereas the vector space model interprets actual relevance to the query and doesn’t require exact match terms. That’s the beauty of it.

It’s this structure that creates much more precise recall.

The Transformer Revolution (Not Michael Bay)

Unlike Michael Bay’s series, the real transformer architecture replaced older, static embedding methods (like Word2Vec) with contextual embeddings.

While static models assign one vector to each word, transformers generate dynamic representations that change based on the surrounding words in a sentence.

And yes, Google has been doing this for some time. It’s not new. It’s not GEO. It’s just modern information retrieval that “understands” a page.

I mean, obviously not. But you, as a hopefully sentient, breathing being, understand what I mean. But transformers, well, they fake it:

  1. Transformers weight input by data by significance.
  2. The model pays more attention to words that demand or provide extra context.

Let me give you an example.

“The bat’s teeth flashed as it flew out of the cave.”

Bat is an ambiguous term. Ambiguity is bad in the age of AI.

But transformer architecture links bat with “teeth,” “flew,” and “cave,” signaling that bat is far more likely to be a bloodsucking rodent* than something a gentleman would use to caress the ball for a boundary in the world’s finest sport.

*No idea if a bat is a rodent, but it looks like a rat with wings.

BERT Strikes Back

BERT. Bidirectional Encoder Representations from Transformers. Shrugs.

This is how Google has worked for years. By applying this type of contextually aware understanding to the semantic relationships between words and documents. It’s a huge part of the reason why Google is so good at mapping and understanding intent and how it shifts over time.

BERT’s more recent updates (DeBERTa) allow words to be represented by two vectors – one for meaning and one for its position in the document. This is known as Disentangled Attention. It provides more accurate context.

Yep, sounds weird to me, too.

BERT processes the entire sequence of words simultaneously. This means context is applied from the entirety of the page content (not just the few surrounding terms).

Synonyms Baby

Launching in 2015, RankBrain was Google’s first deep learning system. Well, that I know of anyway. It was designed to help the search algorithm understand how words relate to concepts.

This was kind of the peak search era. Anyone could start a website about anything. Get it up and ranking. Make a load of money. Not need any kind of rigor.

Halcyon days.

With hindsight, these days weren’t great for the wider public. Getting advice on funeral planning and commercial waste management from a spotty 23-year-old’s bedroom in Halifax.

As new and evolving queries surged, RankBrain and the subsequent neural matching were vital.

Then there was MUM. Google’s ability to “understand” text, images and visual content across multiple languages simultenously.

Document length was an obvious problem 10 years ago. Maybe less. Longer articles, for better or worse, always did better. I remember writing 10,000-word articles on some nonsense about website builders and sticking them on a homepage.

Even then that was a rubbish idea…

In a world where queries and documents are mapped to numbers, you could be forgiven for thinking that longer documents will always be surfaced over shorter ones.

Remember 10-15 years ago when everyone was obsessed when every article being 2,000 words.

“That’s the optimal length for SEO.”

If you see another “What time is X” 2,000-word article, you have my permission to shoot me.

You can’t knock the fact this is a better experience (Image Credit: Harry Clarkson-Bennett)

Longer documents will – as a result of containing more terms – have higher TF values. They also contain more distinct terms. These factors can conspire to raise the scores of longer documents

Hence why, for a while, they were the zenith of our crappy content production.

Longer documents can broadly be lumped into two categories:

  1. Verbose documents that essentially repeat the same content (hello, keyword stuffing, my old friend).
  2. Documents covering multiple topics, in which the search terms probably match small segments of the document, but not all of it.

To combat this obvious issue, a form of compensation for document length is used, known as Pivoted Document Length Normalization. This adjusts scores to counteract the natural bias longer documents have.

Pivoted normalization rescales term weights using a linear adjustment around the average document length (Image Credit: Harry Clarkson-Bennett)

The cosine distance should be used because we do not want to favour longer (or shorter) documents, but to focus on relevance. Leveraging this normalization prioritizes relevance over term frequency.

It’s why cosine similarity is so valuable. It is robust to document length. A short and long answer can be seen as topically identical if they point in the same direction in the vector space.

Great question.

Well, no one’s expecting you to understand the intricacies of a vector database. You don’t really need to know that databases create specialized indices to find close neighbors without checking every single record.

This is just for companies like Google to strike the right balance between performance, cost, and operational simplicity.

Kevin Indig’s latest excellent research shows that 44.2% of all citations in ChatGPT originate from the first 30% of the text. The probability of citation drops significantly after this initial section, creating a “ski ramp” effect.

Image Credit: Harry Clarkson-Bennett

Even more reason not to mindlessly create massive documents because someone told you to.

In “AI search,” a lot of this comes down to tokens. According to Dan Petrovic’s always excellent work, each query has a fixed grounding budget of approximately 2,000 words total, distributed across sources by relevance rank.

In Google, at least. And your rank determines your score. So get SEO-ing.

Position 1 gives you double the prominence of position 5 (Image Credit: Harry Clarkson-Bennett)

Metehan’s study on what 200,000 Tokens Reveal About AEO/GEO really highlights how important this is. Or will be. Not just for our jobs, but biases and cultural implications.

As text is tokenized (compressed and converted into a sequence of integer IDs), this has cost and accuracy implications.

  • Plain English prose is the most token-efficient format at 5.9 characters per token. Let’s call it 100% relative efficiency. A baseline.
  • Turkish prose has just 3.6. This is 61% as efficient.
  • Markdown tables 2.7. 46% as efficient.

Languages are not created equal. In an era where capital expenditures (CapEx) costs are soaring, and AI firms have struck deals I’m not sure they can cash, this matters.

Well, as Google has been doing this for some time, the same things should work across both interfaces.

  1. Answer the flipping question. My god. Get to the point. I don’t care about anything other than what I want. Give it to me immediately (spoken as a human and a machine).
  2. So frontload your important information. I have no attention span. Neither do transformer models.
  3. Disambiguate. Entity optimization work. Connect the dots online. Claim your knowledge panel. Authors, social accounts, structured data, building brands and profiles.
  4. Excellent E-E-A-T. Deliver trustworthy information in a manner that sets you apart from the competition.
  5. Create keyword-rich internal links that help define what the page and content are about. Part disambiguation. Part just good UX.
  6. If you want something focused on LLMs, be more efficient with your words.
    • Using structured lists can reduce token consumption by 20-40% because they remove fluff. Not because they’re more efficient*.
    • Use commonly known abbreviations to also save tokens.

*Interestingly, they are less efficient than traditional prose.

Almost all of this is about giving people what they want quickly and removing any ambiguity. In an internet full of crap, doing this really, really works.

Last Bits

There is some discussion around whether markdown for agents can help strip out the fluff from HTML on your site. So agents could bypass the cluttered HTML and get straight to the good stuff.

How much of this could be solved by having a less fucked up approach to semantic HTML, I don’t know. Anyway, one to watch.

Very SEO. Much AI.

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Featured Image: Anton Vierietin/Shutterstock

Google AI Mode Link Update, Click Share Data & ChatGPT Fan-Outs – SEO Pulse via @sejournal, @MattGSouthern

Welcome to the week’s SEO Pulse: updates affect how links appear in AI search results, where organic clicks are going, and which languages ChatGPT uses to find sources.

Here’s what matters for you and your work.

Google Redesigns Links In AI Overviews And AI Mode

Robby Stein, VP of Product for Google Search, announced on X that AI Overviews and AI Mode are getting a redesigned link experience on both desktop and mobile.

Key Facts: On desktop, groups of links will now appear in a pop-up when you hover over them, showing site names, favicons, and short descriptions. Google is also rolling out more descriptive and prominent link icons across desktop and mobile.

Why This Matters

This is the latest in a series of link-visibility updates Stein has announced since last summer, when he called showing more inline links Google’s “north star” for AI search. The pattern is consistent. Google keeps iterating on how links surface inside AI-generated responses.

The hover pop-up is a new interaction pattern for AI Overviews. Instead of small inline citations that are easy to miss, users now get a preview card with enough context to decide whether to click. That changes the calculus for publishers wondering how much traffic AI results actually send.

What The Industry Is Saying

SEO consultant Lily Ray (Amsive) wrote on X that she had been seeing the new link cards and was “REALLY hoping it sticks.”

Read our full coverage: Google Says Links Will Be More Visible In AI Overviews

43% Of ChatGPT Fan-Out Queries For Non-English Prompts Run In English

A report from AI search analytics firm Peec AI found that a large share of ChatGPT’s fan-out queries run in English, even when the original prompt was in another language.

Key Facts: Peec AI analyzed over 10 million prompts and 20 million fan-out queries from its platform data. Across non-English prompts analyzed, 43% of the fan-out queries ran in English. Nearly 78% of non-English prompt sessions included at least one English-language fan-out query.

Why This Matters

When ChatGPT Search builds an answer, it can rewrite the user’s prompt into “one or more targeted queries,” according to OpenAI’s documentation. OpenAI does not describe how language is chosen for those rewritten queries. Peec AI’s data suggests that English gets inserted into the process even when the user and their location are clearly non-English.

SEO and content teams working in non-English markets may face a disadvantage in ChatGPT’s source selection that doesn’t map to traditional ranking signals. Language filtering appears to happen before citation signals come into play.

Read our full coverage: ChatGPT Search Often Switches To English In Fan-Out Queries: Report

Google’s Search Relations Team Can’t Say You Still Need A Website

Google’s Search Relations team was asked directly whether you still need a website in 2026. They didn’t give a definitive yes.

Key Facts: In a new episode of the Search Off the Record podcast, Gary Illyes and Martin Splitt spent about 28 minutes exploring the question. Both acknowledged that websites still offer advantages, including data sovereignty, control over monetization, and freedom from platform content moderation. But neither argued that the open web offers something irreplaceable.

Why This Matters

Google Search is built around crawling and indexing web content. The fact that Google’s own Search Relations team treats “do I need a website?” as a business decision rather than an obvious yes is worth noting.

Illyes offered the closest thing to a position. He said that if you want to make information available to as many people as possible, a website is probably still the way to go. But he called it a personal opinion, not a recommendation.

The conversation aligns with increasingly fragmented user journeys, now spanning AI chatbots, social feeds, community platforms, and traditional search. For practitioners advising clients on building websites, the answer increasingly depends on where the audience is, not where it used to be.

Read our full coverage: Google’s Search Relations Team Debates If You Still Need A Website

Theme Of The Week: The Ground Keeps Moving Under Organic

Each story this week shows a different force pulling attention, clicks, or visibility away from the organic channel as practitioners have known it.

Google is redesigning how links appear in AI responses, acknowledging the traffic concern. ChatGPT’s background queries introduce a language filter that can exclude non-English content before relevance signals even apply. And Google’s own team won’t say that websites are the default answer for visibility anymore.

These stories reinforce the idea of spreading your content across different platforms to reach more people. And track where your clicks are really coming from.

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Featured Image: TippaPatt/Shutterstock; Paulo Bobita/Search Engine Journal

New Meridian Tool, Performance Max Learning Path – PPC Pulse via @sejournal, @brookeosmundson

Welcome to this week’s PPC Pulse, where this week’s focus is on scenario-based planning in both Google and Microsoft platforms.

Google introduced a new Scenario Planner within Meridian, giving marketers the ability to model budget allocation shifts before committing spend. Microsoft launched a scenario-based Performance Max learning path designed to walk advertisers through practical campaign situations.

Both updates point to a growing emphasis on improving decisions before campaigns go live.

Here’s what happened this week and why it matters for advertisers.

Google Introduces Scenario Planner For Meridian

Google announced a new Scenario Planner within Meridian, its Marketing Mix Modeling platform. The tool allows marketers to test budget allocation scenarios and forecast potential outcomes using Meridian’s modeled insights.

Instead of waiting for quarterly MMM reports or static insights, advertisers can now simulate how shifting spend across channels might impact performance metrics like revenue, conversions, or return on investment.

According to Google, the goal is to make MMM insights more accessible and actionable for marketers who need to defend budgets and make planning decisions in real time. It also reiterated that coding isn’t required to use this tool.

It looks to be a promising planning tool built for higher-level strategy conversations between advertisers and key decision-makers.

Why This Matters For Advertisers

Marketing Mix Modeling has traditionally been handled at a higher level of the organization. It tends to show up in quarterly reviews, annual planning decks, or conversations led by finance and analytics teams. Most PPC managers are not sitting inside MMM tools on a weekly basis.

What makes this update notable is that Google is moving those insights closer to the teams actually managing budgets day to day.

PPC marketers are being asked more frequently to justify budget increases or reallocations with something stronger than last-click performance.

A tool like this could influence how those conversations happen. Instead of pointing only to recent return on ad spend (ROAS) trends, teams may start leaning more on modeled projections and incremental impact estimates when proposing changes.

What PPC Professionals Are Saying

Ginny Marvin, Ads Liaison for Google, shared the announcement on LinkedIn. Here’s what she emphasized about the Scenario Planner:

“No technical MMM experience needed to go from ‘what happened?’ to ‘what’s next?’”

Advertisers like Ivan Walker are “very excited!” about the update, while others like Ashley V. are curious about hearing feedback from others who have started using it.

Microsoft Launches Scenario-Based Performance Max Learning Path

Along the same lines of planning, Microsoft Advertising announced a new Performance Max learning path within its Learning Lab.

Unlike standard certification modules, this path walks advertisers through real-world scenarios designed to build hands-on expertise. The training focuses on practical decision-making across campaign setup, optimization, and troubleshooting.

I appreciate how Microsoft is positioning – that Performance Max success requires understanding, context, and strategy instead of focusing solely on what settings to toggle.

The learning path is designed to help advertisers think through situations they are likely to encounter in live accounts. For example, how to approach budget allocation, how to evaluate asset performance, and how to troubleshoot underperformance.

Why This Matters For Advertisers

Performance Max is not new at this point. Most advertisers have at least tested it, and many are running it at scale. What has changed is the level of thinking required to run it well.

There is still a misconception that PMax runs on its own once you flip it on. In reality, outcomes are heavily influenced by how campaigns are structured, what signals are being fed into the system, and how clearly conversion goals are defined.

Microsoft is leaning into the idea that automation does not remove the need for strategy. It shifts where strategy shows up. Instead of spending time adjusting bids manually, advertisers are spending time making decisions around inputs, segmentation, creative quality, and measurement alignment.

For agencies and in-house teams, scenario-based training could be useful for onboarding or leveling up junior team members. It provides context around the types of situations teams actually encounter, rather than just explaining what each setting does.

Theme Of The Week: Planning Before Spending

Both updates this week center around the same idea, which is trying to improve the quality of decisions before money is spent.

Google is giving marketers a way to test budget allocation scenarios before shifting spend to other platforms. Microsoft is walking advertisers through realistic campaign situations before they are live in their accounts.

While many industry updates focus on optimizations after campaigns are running, these ones focus on the earlier stage. How confident are you in the structure? How confident are you in the allocation? How confident are you in the assumptions behind the strategy?

Especially with budgets under tighter scrutiny than ever, and automation handling much more of campaign execution, the planning phase definitely carries more weight than it used to.

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Featured Image: Kansuda2 Kaewwannarat/Shutterstock; Paulo Bobita/Search Engine Journal