The shock of seeing your body used in deepfake porn 

When Jennifer got a job doing research for a nonprofit in 2023, she ran her new professional headshot through a facial recognition program. She wanted to see if the tech would pull up the porn videos she’d made more than 10 years before, when she was in her early 20s. It did in fact return some of that content, and also something alarming that she’d never seen before: one of her old videos, but with someone else’s face on her body.

“At first, I thought it was just a different person,” says Jennifer, who is being identified by a pseudonym to protect her privacy. 

But then she recognized a distinctly garish background from a video she’d shot around 2013, and she realized: “Somebody used me in a deepfake.”

Eerily, the facial recognition tech had identified her because the image still contained some of Jennifer’s features—her cheekbones, her brow, the shape of her chin. “It’s like I’m wearing somebody else’s face like a mask,” she says. 

“It’s like I’m wearing somebody else’s face like a mask.”

Conversations about sexualized deepfakes—which fall under the umbrella of nonconsensual intimate imagery, or NCII—most often center on the people whose faces are featured doing something they didn’t really do or on bodies that aren’t really theirs. These are often popular celebrities, though over the past few years more people (mostly women and sometimes youths) have been targeted, sparking alarm, fear, and even legislation. But these discussions and societal responses usually are not concerned with the bodies the faces are attached to in these images and videos.

As Jennifer, now 37 and a psychotherapist working in New York City, says: “There’s never any discussion about Whose body is this?” 

For years, the answer has generally been adult content creators. Deepfakes in fact earned their name back in November 2017, when someone with the Reddit username “deepfakes” uploaded videos showing faces of stars like Scarlett Johansson and Gal Gadot pasted onto porn actors’ bodies. The nonconsensual use of their bodies “happens all the time” in deepfakes, says Corey Silverstein, an attorney specializing in the adult industry. 

But more recently, as generative AI has improved, and as “nudify” apps have begun to proliferate, the issue has grown far more complicated—and, arguably, more dangerous for creators’ futures. 

Porn actors’ bodies aren’t necessarily being taken directly from sexual images and videos anymore, or at least not in an identifiable way. Instead, they are inevitably being used as training data to inform how new AI-generated bodies look, move, and perform. This threatens the livelihood and rights of porn actors as their work is used to train AI nudes that in turn could take away their business. And that’s not all: Advancements in AI have also made it possible for people to wholly re-create these performers’ likenesses without their consent, and the AI copycats may do things the performers wouldn’t do in real life. This could mean their digital doubles are participating in certain sex acts that they haven’t agreed to do, or even perpetrating scams against fans. 

Adult content creators are already marginalized by a society that largely fails to protect their safety and rights, and these developments put them in an even more vulnerable position. After Jennifer found the deepfake featuring her body, she posted on social media about the psychological effects: “I’ve never seen anyone ask whether that might be traumatic for the person whose body was used without consent too. IT IS!” Several other creators I spoke with shared the mental toll that comes with knowing their bodies have been used nonconsensually, as well as the fear that they’ll suffer financially as other people pirate their work. Silverstein says he hears from adult actors every day who “are concerned that their content is being exploited via AI, and they’re trying to figure out how to protect it.” 

One law professor and expert in violence against women calls these creators the “forgotten victims” of NCII deepfakes. And several of the people I spoke with worry that as the US develops a legal framework to combat nonconsensual sexual content online, adult actors are only at risk of further injury; instead of helping them, the crackdown on deepfakes may provide a loophole through which their content and careers could be stripped from the internet altogether.

How deepfakes cause “embodied harms”

During his preteen years in the 1970s, Spike Irons, now a porn actor and president of the adult content platform XChatFans, was “in love” with Farrah Fawcett. Though Fawcett did not pose nude, Jones managed to get his hands on what looked like pictures of her naked. “People were cutting out faces and pasting them on bodies,” Irons says. “Deepfakes, before AI, had been going around for quite a while. They just weren’t as prolific.”

The early public internet was rife with websites capitalizing on the idea that you could use technology to “see” celebrities naked. “People would just use Microsoft Paint,” says Silverstein, the attorney. It was a simple way to mash up celebrities’ faces with porn. 

People later used software like Adobe After Effects or FakeApp, which was designed to swap two individuals’ faces in images or videos. None of these programs required serious expertise to alter content, so there was a low barrier to entry. That, plus the wealth of porn performers’ videos online, helped make face-swap deepfakes that used real bodies prevalent by the 2010s. When, later in the decade, deepfakes of Gal Gadot and Emma Watson caused something of a broader panic, their faces were allegedly swapped onto the bodies of the porn actors Pepper XO and Mary Moody, respectively.

But it wasn’t just high-profile actors like them whose bodies were being used. Jennifer was “a very minor performer,” she says. “If it happened to me, I feel like it could happen to anybody who’s shot porn.” Since he started his practice in 2006, Silverstein says, “numerous clients” have reached out to report “This is my body on so-and-so.” 

Both people whose faces appear in NCII deepfakes and those whose bodies are used this way can feel serious distress. Experts call this type of damage “embodied harms,” says Anne Craanen, who researches gender-based violence at the UK’s Institute for Strategic Dialogue, an organization that analyzes extremist content, disinformation, and online threats. 

The term reflects the fact that even though the content exists in the virtual realm, it can cause physiological effects, including body dysmorphia. The face-swapped entity occupies the uncanny valley, distorting self-perception. After discovering their faces in sexual deepfakes, many people feel silenced, experts told me; they may “self-censor,” as Craanen puts it, and step back from public-facing life. Allison Mahoney, an attorney who works with abuse survivors, says that people whose faces appear in NCII can experience depression, anxiety, and suicidal ideation: “I’ve had multiple clients tell me that they don’t sleep at night, that they’re losing their hair.” 

Independent creators aren’t just “having sex on camera.” For someone to rip off their work “for their own entertainment or financial gain fucking sucks.”

Though the impact on people whose bodies are used hasn’t been discussed or studied as often, Jennifer says that “it’s just a really terrible feeling, knowing that you are part of somebody else’s abuse.” She sees it as akin to “a new form of sexual violence.”

The uncertainty that comes with not being aware of what your body is doing online can be highly unsettling. Like Jennifer, many adult actors don’t really know what’s out there. But some devoted followers know the actors’ bodies well—often recognizing tattoos, scars, or birthmarks—and “very quickly they bring [deepfakes] to the adult performer’s attention,” says Silverstein. Or performers will stumble upon the content by chance; some 20 years ago, for instance, the first such client to tell Silverstein her body was being used in a deepfake happened to be searching Nicole Kidman online when she found that one of the results showed Kidman’s face on her porn. “She was devastated, obviously, because they took her body,” he says, “and they were monetizing it.” 

Otherwise, this imagery may be found by an organization like Takedown Piracy, one of several copyright enforcement companies serving adult content creators. US copyright violations can be challenging to prove if someone’s body lacks distinguishing features, says Reba Rocket, Takedown Piracy’s chief operating and marketing officer. But Rocket says her team has added digital fingerprinting technology to clients’ material to help flag and remove problematic videos, often finding them before clients realize they’re online. 

By capturing “tens of thousands of tiny little visual data points” from videos, digital fingerprinting creates unique corresponding files that can be used to identify them, Rocket says—kind of like an invisible watermark. The prints remain even if pirates alter the videos or replace performers’ faces. Takedown Piracy has digitally fingerprinted more than half a billion videos and the organization has gotten 130 million copyrighted videos taken down from Google alone (though, of those videos, Rocket hasn’t tracked how many of these specifically include someone else’s face on a performer’s body). 

Besides copyright, a range of legal tools can be used to try to combat NCII, says Eric Goldman, a law professor at Santa Clara University. For example, victims can claim invasion of privacy. But using these tools isn’t particularly straightforward, and they may not even apply when it comes to someone’s body. If there aren’t, for instance, unique markers indicating that a body in a deepfake belongs to the person who says it does, US law “doesn’t really treat [this content] as invasion of privacy,” Goldman says, “because we don’t know who to attribute it to.”

In a 2018 study that reviewed “judicial resolution” of cases involving NCII, Goldman found that one successful way plaintiffs were able to win cases was to assert “intentional affliction of emotional distress.” But again, that hinges on the ability to clearly identify the person in the content. Relevant statutes, he adds, might also require “intent to harm the individual,” which may be hard to show for people whose bodies alone are featured.

“AI girls will do whatever you want”

In the last few years, Silverstein says, it’s become less and less common to see the bodies of real adult content creators in deepfakes, at least in a way that makes them clearly identifiable. 

Sometimes the bodies have been manipulated using AI or simpler editing tools. This can be as basic as erasing a birthmark or changing the size of a body part—minor edits that make it impossible to identify someone’s image beyond a reasonable doubt, so even porn actors who can tell that an altered image used their body as a base won’t get very far in the legal realm. “A lot of people are like, That looks like my body,” says Silverstein, but when he asks them how, they’ll reply, It just does

At the same time, other users are now creating NCII with wholly AI-generated bodies. In “nudify” apps, anyone with a minimal grasp of technology can upload a photo of someone’s clothed body and have it replaced with a fake naked one. “So [much] of this content being created is just someone’s face on an AI body,” Silverstein says.

Such apps have drawn a ton of attention recently, in incidents from Grok’s “nudifying” minors to Meta’s running ads for—and then suing—the nudify app Crushmate. But there’s been relatively little attention paid to the content being used to train them. They almost certainly draw on the more than 10,000 terabytes of online porn, and performers have virtually zero recourse. 

One reason is that creators aren’t able to demonstrate with any certainty that their content is being used to train AI models like those used by nudify apps. “These things are all a black box,” says Hany Farid, a professor at the University of California, Berkeley, who specializes in digital forensics. But “given the ubiquity” of adult content, he adds, it’s a “reasonable assumption” that online porn is being used in AI training. 

“It’s just not at all difficult to come up with pornographic data sets on the internet,” says Stephen Casper, a computer science PhD student at MIT who researches deepfakes. What’s more, he says, plenty of shadowy online communities provide “user guides” on how to use this data to train AI, and in particular programs that generate nudes. 

It’s not certain whether this activity falls within the US legal definition of “fair use”—an issue that’s currently being litigated in several lawsuits from other types of content creators—but Casper argues that even if it does, it’s ethically murky for porn created by consenting adults 10 years ago to wind up in those training data sets. When people “have their stuff used in a way that doesn’t respect or reflect reasonable expectations that they had at that time about what they were creating and how it would be used,” he says, there’s “a legitimate sense in which it’s kind of … nonconsensual.” 

Adult performers who started working years ago couldn’t possibly have consented to AI anything; Jennifer calls AI-related risks “retroactively placed.” Contracts that porn actors signed before AI, adds Silverstein, might provide that “the publisher could do anything with the content using technology that now exists or here and after will be discovered.” That felt more innocuous when producers were talking about the shift from VHS to DVD, because that didn’t change the content itself, just the way it was conveyed. It’s a far different prospect for someone to use your content to train a program to create new content … content that could replace your work altogether. 

Of course, this all affects creators’ bottom line—not unlike the way Google’s AI overviews affect revenue for online publishers who’ve stopped getting clicks when people are content with just reading AI-generated summaries. Performers’ “concern is … it’s another way to pirate [their] content,” says Rocket. 

After all, independent creators aren’t just “having sex on camera,” as the adult content creator Allie Eve Knox puts it. They’re paying for filming equipment and location rentals, and then spending hours editing and marketing. For someone to then rip off and distort that content “for their own entertainment or financial gain,” she says, “fucking sucks.” 

KIM HOECKELE

Tanya Tate, a longtime adult content creator, tells me about another highly unsettling AI-created situation: She was recently chatting with a fan on Mynx, a sexting app, when he asked her if she knew him. She told him no, and “his eyes just started watering,” Tate says. He was upset because he thought she did know him. Turns out he’d sent $20,000 to a scammer who’d used an AI-generated deepfake of Tate to seduce him. 

Several men, Tate subsequently learned, had been scammed by an AI version of her, and some of them began blaming her for their losses and posting false statements about her online. When she reported one particularly aggressive harasser to the police, they told her he was exercising his “freedom of speech,” she says. Rocket, too, is familiar with situations where AI is used to take advantage of fans. “The actual content creator will get nasty emails from these people who’ve been scammed,” she says.

Other porn actors say they fear that their likenesses have been used without consent to do other things they wouldn’t do. One, Octavia Red, tells me she doesn’t do anal scenes, “but I’m sure there’s tons of deepfake anal videos of me that I didn’t consent to.” That could cost her, she fears, if viewers choose to watch those videos instead of subscribing to her websites. And it could cause fans to develop false expectations about what kind of porn she’ll create.

“I saw one AI creator saying, ‘Well, AI girls will do whatever you want. They don’t say no,’” says Rocket. “That horrifies me … especially if they’re training those AI models on real people. I don’t think they understand the damage to mental health or reputation that that can create. And once it’s on the internet, it’s there forever.” 

Efforts to “scrub adult content from the internet”

As AI technology improves, it’s increasingly difficult for people to discern any type of real video from the best AI-generated ones on their own. In one 2025 study, UC Berkeley’s Farid found that participants correctly identified AI-generated voices about 60% of the time (not much better than random chance), while advances like false heartbeats make AI-generated humans tougher than ever to spot.

Nevertheless, most lawyers and legal experts I spoke with said copyright laws are still adult performers’ best bet in the US legal system, at least for getting their face-swapped content taken down. For his clients, Silverstein says, he tries to figure out the content’s origins and then issue takedown requests under the Digital Millennium Copyright Act, a 1998 law that adapted copyright law for the internet era. “Even recently, I had a performer who has an insanely well-known tattoo,” he says, and with a DMCA subpoena he managed to identify the poster of the content, who voluntarily removed it. 

But this way of working is becoming increasingly rare.

These days it’s nearly “impossible,” Silverstein says, to determine who produced a deepfake, because many platforms that host pirated content operate facelessly. They’re also often based in places that “don’t really care about US law when it comes to copyrights,” says Rocket—places like Russia, the Seychelles, and the Netherlands. 

While governments in the EU, the UK, and Australia have said they will ban or restrict access to nudify apps, it’s not an easily executed proposition. As Craanen notes, when app stores remove these services, they often simply reappear under different names, providing the same services. And social platforms where people share NCII deepfakes, argues Rocket, are slacking in getting them removed. “It’s endless, and it’s ridiculous, because places like Twitter and Facebook have the same technology we do,” Rocket says. “They can identify something as an infringement instantly, but they choose not to.”

(An Apple spokesperson, Adam Dema, said in an email that “’nudification’ apps are against our guidelines” in the app store, and it has “proactively rejected many of these apps and removed many others,” flagging a reporting portal for users. A Google spokesperson emailed, “Google Play does not allow apps that contain sexual content,” noting that the company takes “proactive steps to detect and remove apps with harmful content” and has suspended hundreds of apps for violating its policy. A Meta spokesperson shared a blog post about actions that company has taken against nudify apps but did not respond to follow-up questions about copyrighted material. X did not respond to a request for comment.)

As porn performers are forced to navigate AI-related threats, the only current federal law to address deepfakes may not help them much—and could even make matters worse. The Take It Down Act, which became US law last year, criminalizes publishing NCII and requires websites to remove it within 48 hours. But, as Farid notes, people could weaponize the measure by reporting porn that was made legally and with consent and claiming that it’s NCII. This could result in the content’s removal, which would hurt the performers who made it. Santa Clara’s Goldman points to Project 2025, the Heritage Foundation’s policy blueprint for the second Trump administration, which aims to wipe porn from the web. The Take It Down Act, he argues, “allows for the coordinated effort to scrub adult content from the internet.” 

US lawmakers have a history of hurting sex workers in their attempts to regulate explicit content online. State-level age verification laws are an example; visitors can pretty easily get around these measures, but they can still result in reduced revenue for adult performers (because of lower traffic to those sites and the high price of age-checking services they have to purchase). 

“They’re always doing something to fuck with the porn industry, but not in a way that actually helps sex workers,” says Jennifer. “If they do something, they’re taking away your income again—as opposed to something like giving you more rights to your image, [which] would be tremendously helpful.” 

But as generative AI plays an increasingly large role in NCII deepfakes, the types of images to which adult performers have rights moves deeper into a gray area. Can actors lay claim to AI images likely trained on their bodies? How about AI-generated videos that impersonate them, like the one that tricked Tanya Tate’s fan?

The biggest challenge will be creating “legitimate, effective laws that will absolutely protect content creators from abusing their likeness to train and create AI,” Rocket says. “Absent that, we’re just going to have to keep pulling content down from the internet that’s fake.”

In the meantime, a few porn actors tell me, they’re trying to take advantage of copyright laws that weren’t really made for them; they’ve signed with platforms that host their AI-generated duplicates, with whom fans pay to chat, in part so they’ll have contracts that protect ownership of their AI likenesses. When I spoke with the actor Kiki Daire in September 2025 for a story on adult creators’ “AI twins,” she said she “own[ed] her AI” because she’d signed a contract with Spicey AI, a site that hosted AI duplicates of adult performers. If another company or person created her AI-generated likeness, she added, “I have a leg to stand on, as far as being able to shut that down.”  

Even this, though, is not a sure thing; Spicey AI, for instance, shut down several months after I spoke with Daire, so it’s unlikely that her contract would hold. And when I spoke in October with Rachael Cavalli, another adult actor who had signed with an AI duplicate site in hopes it’d help protect her AI image, she admitted, “I don’t have time to sit around and look for companies that have used my image or turned something into a video that I didn’t actually do … it’s a lot of work.” In other words, having rights to your AI image on paper doesn’t make it easier to track down all the potentially infinite breaches of those rights online.

If she’d known what she knows about technology today, Jennifer says, she doesn’t think she would have done porn. The risks have increased too much, and too unpredictably. She now does in-person sex work; it’s “not necessarily safer,” she says, “but it’s a different risk profile that I feel more equipped to manage.” 

Plus, she figures AI is unlikely to replace in-person sex workers the way it could porn actors: “I don’t think there’s going to be stripper robots.” 

Jessica Klein is a Philadelphia-based freelance journalist covering intimate partner violence, cryptocurrency, and other topics.

The Tesla Semi could be a big deal for electric trucking

The Tesla Semi has officially arrived. The company recently released a photo of the first vehicle rolling off its new full-scale production line.

This moment has been nearly a decade in the making: The company first announced the truck in late 2017. And now we’ve got final battery specs, official prices, and big news about big orders.

The Semi is a relatively affordable electric semitruck with pretty impressive performance. It also comes at a moment when Tesla has lost its grip on the global electric-vehicle market. Let’s talk about what’s new with the Tesla Semi and why this could be a breakout moment for electric trucking.

Medium- and heavy-duty vehicles, like buses and semitrucks, make up a small fraction of vehicles on the road but contribute an outsize fraction of pollution, including both carbon dioxide emissions and other pollutants like nitrogen oxides (NOx) and small particles. Globally, trucks and buses represent about 8% of total vehicles on the road, but they create 35% of carbon dioxide emissions from road transport.

Tesla’s latest addition to its vehicle lineup, the Class 8 Semi, could be part of the solution to cleaning up this polluting sector. (I’ll note here that I briefly interned at Tesla in 2016. I don’t have any ties to or financial interest in the company today.) 

In November 2017, Elon Musk took to the stage at a lavish event in LA to announce the Semi. At that event, Musk promised a truck that could go from zero to 60 miles per hour in five seconds, could achieve a range of 500 miles, and would come with thermonuclear-explosion-proof glass. (Remember the era before the Twitter takeover and DOGE, when this was what Musk was known for? A simpler time.)

Soon after the unveiling, major corporations including Walmart put in early orders for Tesla Semis. Deliveries were expected in 2019.

That deadline obviously didn’t work out. The date was pushed back several times, and Tesla did start delivering a small number of pilot trucks, beginning in 2022. But this year, things got more serious, with the company releasing its final production specifications in February and rolling its first Semi off its high-volume production line in late April. 

And last week, WattEV announced an order of 370 Tesla Semis. WattEV offers electric freight operations, essentially providing trucks as a service to companies so they don’t have to purchase their own or supply their own charging infrastructure. The company will pay over $100 million for the new trucks, and the first 50 should be delivered this year, with the full fleet expected by the end of 2027. Those trucks will be supported by megawatt-charging systems located in Oakland, Fresno, Stockton, and Sacramento.

With the factory up and running and a huge order on the books, it feels as if the Tesla Semi has truly arrived. And some of Musk’s claims from 2017 ring true: The base model has a range of about 320 miles, and the long-range version about 480 miles (quite close to his 500-mile claim).

Delivering this much range for this big truck means a whopping battery. The base model Tesla Semi battery pack has a usable capacity of 548 kilowatt-hours, according to a document filed with the California Air Resources Board (CARB). But the battery is even more massive in the long-range version, which boasts a whopping 822 kilowatt-hour battery. Compare these to the Tesla Model 3, which typically comes with a 64 kilowatt-hour pack.

I reached out to Tesla to confirm the battery size and ask other questions for this article; the company didn’t respond.

These trucks cost quite a bit more than they were expected to in 2017. At that time, the expected price was $150,000 for the base model and $180,000 for the long-range. Today, Tesla is pricing the trucks at $260,000 and $300,000, respectively, according to documentation filed with CARB.

That’s considerably more expensive than the median diesel truck being sold today, which rang in at $172,500 for the 2025 model year, according to research from the International Council on Clean Transportation. But it’s much cheaper than similar battery-electric trucks available today, where the median is about $411,000.

And in California, where companies can get vouchers that cover $120,000 towards the purchase price of an electric truck, the Tesla Semi is competitive right away, especially since electric trucks tend to be much cheaper to run and maintain than diesel ones.

Over the years, it wasn’t always clear that the Tesla Semi would ever actually hit the roads. (At that same 2017 event, Musk announced a new Roadster sports car, and that’s nowhere to be seen.) So it’s encouraging to see the factory starting up, and a large order that looks like it could lend this project some commercial momentum.

Tesla had a massive impact on the electric vehicle market, and if it can scale production and support charging infrastructure, it could help do the same for trucking.

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

The Download: deepfake porn’s stolen bodies and AI sharing private numbers

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.

The shock of seeing your body used in deepfake porn

When Jennifer got a research job in 2023, she ran her new professional headshot through a facial recognition program. She wanted to see whether it would pull up the porn videos she’d made more than a decade earlier. It did, but it also surfaced something she’d never seen before: one of her old videos, now featuring someone else’s face on her body.

Conversations about sexualized deepfakes usually focus on the people whose faces are inserted into explicit content without consent. But another group often gets ignored: the people whose bodies those faces are attached to.

Adult content creators say AI systems are training on their work, cloning their likenesses, and generating explicit content they never agreed to make, all with little legal protection or control.  Read the full story on the threat to their rights, livelihoods, and ownership of their own bodies.

—Jessica Klein

This story is part of our The Big Story series, the home for MIT Technology Review’s most important, ambitious reporting. You can read the rest here

AI chatbots are giving out people’s real phone numbers

Generative AI is exposing people’s personal contact information—and there’s no easy way to stop it.

A software developer started receiving WhatsApp messages asking for help after Gemini surfaced his number. A university researcher got the chatbot to reveal a colleague’s private cell number. A Reddit user says Gemini sent a stream of callers looking for lawyers to his phone.

Experts believe these privacy lapses stem from personally identifiable information in AI training data. Chatbots may now be making that information dramatically easier to find.

Find out why these breaches are growing—and why there’s little that victims can do to stop them.

—Eileen Guo

The Tesla Semi could be a big deal for electric trucking

Nearly a decade after Elon Musk first unveiled the Tesla Semi, the electric truck is finally rolling off the production line. It could be a breakout moment for battery-powered freight.

Semitrucks produce an outsized share of road transport pollution, while electric alternatives have struggled with high prices, limited range, and charging challenges. Tesla is betting the Semi can overcome those problems. The truck reportedly travels up to 480 miles on a single charge and costs far less than many competing electric models.

Here’s how the Tesla Semi could give electric trucking a vital boost.

—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.

The must-reads

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

1 The US has approved Nvidia chip sales to 10 Chinese firms
Alibaba, Tencent, and ByteDance are among those cleared to buy H200 chips. (Reuters $)
+ The US will receive 25% of the revenue from the sales. (Engadget)
+ But Beijing wants domestic firms to prioritize homegrown chips. (Nikkei Asia)
+ Nvidia CEO Jensen Huang is in China with a White House delegation. (CNBC)

2 Beijing’s push for AI independence is weakening US leverage
It’s allowing China to resist pressure during the Beijing talks. (NYT $)
+ The country has made a big bet on open-source. (MIT Technology Review)
+ Here’s what’s at stake for tech at the Trump-Xi meeting. (Rest of World)

3 AI is “rotting the brains” of developers
They’re losing their previous abilities to do their jobs. (404 Media)
+ A populist backlash is building against AI. (MIT Technology Review)
+ It’s time to reset our expectations about AI. (MIT Technology Review)

4 Sam Altman has over $2 billion in companies that have dealt with OpenAI
The ties have triggered accusations of conflicts of interest. (The Times $)
+ The GOP is scrutinizing Altman’s business dealings. (WSJ $)

5 Andreessen Horowitz has become the top political donor in the US
A16z contributed $115.5 million to the midterm elections. (NYT)
+ AI lobbying has reached a fever pitch. (NYT $)

6 Microsoft feared being too dependent on OpenAI 
CEO Satya Nadella was worried about OpenAI supplanting his company. (CNBC)
+ Microsoft is eyeing startup deals for life after OpenAI. (Reuters $)

7 AI systems are forecasting wars and regime collapse
One estimates a 20% chance of regime change in Iran by 2026. (Economist $)
+ AI has turned the Iran conflict into theater. (MIT Technology Review)

8 Anthropic says a model behaved badly due to training on dystopian sci-fi
Training on more positive stories could help. (Ars Technica)

9 Data centers now consume 6% of the electricity in the US and UK
AI’s global energy consumption is up 15% globally in two years. (Guardian)

10 NASA has rescued Curiosity after its drill got stuck on Mars
The agency has just revealed how it freed the rover. (Wired $) 

Quote of the day

“Musk loves to be glazed, and this person is the doughnut factory.”

—Joan Donovan, assistant professor of journalism and emerging media studies at Boston University, tells the Washington Post how Elon Musk has consistently amplified one anonymous X account.

One More Thing

glitch aesthetic of a soldiers face

YOSHI SODEOKA


Inside the messy ethics of making war with machines

In a near-future war—one that might begin tomorrow—a sniper’s computer vision system flags a potential target. Just over the horizon, a chatbot advises a commander to order an artillery strike.

In both cases, an AI system recommends pulling the trigger while a human still has the final say. But how much of the decision is really theirs? When, if ever, is it ethical for that decision to kill? And who’s to blame when something goes wrong?

This is how AI is reshaping decision-making on the battlefield.

—Arthur Holland Michel

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.)

+ The secrets behind how Shazam works have been revealed.
+ For the first time in a decade, a rare “Cloud Jaguar” was caught on camera.
+ Explore our galaxy from your screen at this year’s Milky Way Photographer of the Year collection.
+ If you want a game over with style, a funeral company is offering Mario, Luigi, Peach, and even Yoshi-branded coffins.

Data readiness for agentic AI in financial services

Financial services companies have unique needs when it comes to business AI. They operate in one of the most highly regulated sectors while responding to external events that are updated by the second. As a result, the success of agentic AI in financial services depends less on the sophistication of the system and more on the quality, security, and accessibility of the data it relies on. 

“It all starts with the data,” says Steve Mayzak, global managing director of Search AI at Elastic.

Agentic AI—systems that can independently plan and take actions to complete tasks, rather than simply generate responses—holds enormous potential for financial services due to its ability to incorporate real-time data and optimize complex workflows. Gartner has found that more than half of financial services teams have already implemented or plan to implement agentic AI. 

However, introducing autonomous AI into any organization magnifies both the strengths and weaknesses of the underlying data it uses. To deploy agentic AI with speed, confidence, and control, financial services companies must first be able to search, secure, and contextualize their data at scale. “Agentic AI amplifies the weakest link in the chain: data availability and quality,” says Mayzak. “And your systems are only as good as their weakest link.”

Financial services companies, therefore, require a trusted and centralized data store that is easy to access, dependable, and can be managed at scale.

The high stakes of quality information

Regulation in the financial services sector requires a high degree of accountability for all data tools. As Mayzak says, “You can’t just stop at explaining where the data came from and what it was transformed into: ‘Here’s the data that went in, and this is what came out.’ You need an auditable and governable way to explain what information the model found and the logic of why that data was right for the next step.” That is, you need to be able to see, understand, and describe the underlying processes.

At the same time, financial services companies require speed and accuracy in order to meet customer expectations and stay ahead of competition. Markets are continually shifting, and risks and opportunities move along with them. If an AI model can parse natural language (unstructured data) from complex sources—in addition to structured data in spreadsheets that are easier to analyze—this gives users more relevant information. 

In this environment, there is no tolerance for error, including the hallucinations that plagued early AI efforts. Agentic AI systems depend on rapid access to high-quality, well-governed data that is secure and accessible. In financial services, that data spans transactions, customer interactions, risk signals, policies, and historical context. The task of preparing that data for AI should not be underestimated. “Natural language is way more messy than structured data, and that makes the process of organizing and cleaning it up that much more important and also that much harder,” says Mayzak.

The data must be well indexed and consolidated across different locations, not locked in the silos of separate systems across the organization. Otherwise, AI agents lag, provide inconsistent answers, and produce decisions that are harder to trace and explain, undermining confidence among regulators, customers, and internal stakeholders. 

As Mayzak says, “There are many different ways to describe how to execute a trade at a bank. In an agent-powered world, we need those descriptions to be deterministic—to give the same results every time. Yet we’re building on powerful but non-deterministic models. That’s incredibly tricky, but not impossible.”

For a financial services firm, managing this can be very challenging. A Forrester study found that 57% of financial organizations are still developing the necessary internal capabilities to fully leverage agentic AI. The data exists in many different formats, created over the course of a bank’s history,” says Mayzak. “Take any bank that’s been around for 50 years: They might have 60 different types of PDFs for the exact same thing. And at the same time, we want the output of these systems to be 100% accurate. In many cases, there is no ‘good enough’.” That is, companies need to do it right, and the first time.

Searching and securing results 

An effective search platform is key to solving the problem of fragmented, poorly indexed, inaccessible data. Financial services companies that can readily sift through both their structured and unstructured data, keep it secure, and apply it in the right context will get the most value from agentic AI. This often requires designing AI systems with data access and utility in mind so they can work faster and yield more accurate results, as well as reduce risk. “Search is the foundational technology that makes AI accurate and grounded in real data,” Mayzak says. “Search platforms have become the authoritative context and memory stores that will power this AI revolution.”

Once in place, these AI-enhanced searches and autonomous systems can serve financial services companies for a range of purposes. When monitoring client exposure, agentic AI can continuously scan transactions, market signals, and external data to detect emerging risks; platforms can then automatically flag or escalate issues in real time. In trade monitoring, AI agents can review trade workflows, identify discrepancies across different formats, and resolve exceptions step by step with minimal human intervention. In regulatory reporting, AI can gather data from across systems, generate required reports, and track how each output was produced. These applications of AI save time while supporting audit and compliance needs by being traceable and explainable.

Although such capabilities already exist, they are often manual, fragmented, and difficult to scale. Agentic AI allows financial organizations to move toward more automated, efficient, and scalable processes while maintaining the accuracy and transparency required in their highly regulated environment. As Mayzak says, “It’s not that different from how humans operate today, just done at a much faster pace and at scale.” 

Building an agentic AI ecosystem

Launching agentic AI can be daunting, especially if other AI ventures have stalled internally. Mayzak’s recommendation is to choose a manageable use case and allow it to grow over time. “Success can build on success,” he says. “While companies may aim to automate a 70-step business process, they are discovering that you have to start somewhere. What is working in the market is tackling the problem one step at a time. Once you get the first step working, then you can take the next step, and the next.” 

The financial services organizations that lead among their peers will be those that integrate agentic AI into a broader ecosystem that includes strong security controls, good data governance, and effective management of system performance. As Mayzak says, “Doing this well will create an AI feedback loop, where executives gain new signals from these systems to assess the effectiveness of their investments and generate reliable, actionable insights.” By iterating on pilots and continuously improving, companies will build agentic systems that can be measured, managed, and scaled. This will transform agentic AI into lasting competitive advantage.

Learn more about how Elastic supports financial services.

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. It 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.

Establishing AI and data sovereignty in the age of autonomous systems

When generative AI first moved from research labs into real-world business applications, enterprises made a tacit bargain: “Capability now, control later.” Feed your proprietary data into third-party AI models, and you will get powerful results. But your data passes through systems you do not own, under governance you do not set. The protections you rely on are only as durable as the provider’s next policy update.

Now, with generative AI established in everyday business operations and sophisticated new agentic AI systems advancing every day, companies are reevaluating the terms of that deal.

“Data is really a new currency; it’s the IP for many companies,” says Kevin Dallas, CEO of EDB, echoing a recurrent anxiety from customers. “The big concern is, if you’re deploying an AI-infused application with a cloud-based large language model, are you losing your IP? Are you losing your competitive position?”

That question is now fueling a movement toward reclaiming both the data and AI systems that have rapidly become part of core business infrastructure. AI and data sovereignty, which refers to breaking dependence on centralized providers and establishing genuine control over models and data estates, it is an urgent priority for many companies, says Dallas, citing internal EDB data: “70% of global executives believe they need a sovereign data and AI platform to be successful.”

The idea of AI sovereignty is becoming a global policy conversation. NVIDIA CEO Jensen Huang recently spoke about the need for such a shift at the World Economic Forum’s annual meeting at Davos in January 2026: “I really believe that every country should get involved to build AI infrastructure, build your own AI, take advantage of your fundamental natural resource—which is your language and culture—develop your AI, continue to refine it, and have your national intelligence be part of your ecosystem.”

This report explores how enterprises are pursuing sovereignty over their models and data estates in an era of rapid AI adoption. Drawing on a survey conducted by EDB of more than 2,050 senior executives and a series of interviews with industry experts, the research confirms that the sovereignty movement on the enterprise level is already well underway.

Download the report.

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. It 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.

AI Reshapes Print-on-Demand Ecommerce

Starting a print-on-demand ecommerce business has never been easier — or more difficult.

On the one hand, AI-generated artwork now rivals human-created versions. A merchant can construct dozens of print-on-demand-ready products in minutes and at almost no cost.

Unfortunately, the combination of that extremely low barrier to entry and AI-driven changes to ecommerce product discovery makes it difficult to start a POD business.

AI makes print-on-demand design easy and customer acquisition difficult.

It can seem almost impossible to get even the first sale. POD merchants now face at least three tough promotional challenges:

  • Zero-click search results,
  • AI-driven product discovery,
  • The “margin-to-CAC” challenge.

Zero Clicks

Google’s AI Overviews have crushed organic search traffic by an estimated 58% in 2025, according to data from Ahrefs,  SparkToro, Pew Research, and many others.

Google is apparently taking steps to restore at least some of the clicks its AI killed. The effect should be more links in AI experiences:

  • Deep dives that add outbound links beneath AI responses to encourage additional exploration.
  • Highlighting content that searchers already subscribe to.
  • Expert recommendations to surface reviews, forum posts, and social discussions.
  • Inline links that place clickable citations alongside AI-generated copy, rather than in a side panel.
  • Website previews to show linked headlines, descriptions, and site information when searchers hover over citations on desktop devices.

“Google is addressing the elephant in the room,” wrote Lisa Haiss in an Emarketer article. “Clickthrough rates are down, and publishers are losing traffic to zero-click searches.”

Yet even these new AI Overview features might not be enough to help a POD shop.

The traditional path of publishing optimized pages and gradually building organic search traffic may no longer produce enough visibility to grow a business. Or at least not now.

Product Discovery

Zero-click search results imperil product discovery.

Until very recently, most merchants understood how shoppers find products online. An entrepreneur launching a new POD business might focus on social posts, Pinterest images, marketplace listings, and, of course, traditional search engines. AI search, AI shopping, and agentic commerce complicate the mix.

Merchants recognize that AI is upending online shopping, but we’re unsure what form it will take or exactly how to optimize for it.

Structured product data, detailed descriptions, reviews, and merchandising context could be the solution. That appears to be what Shopify is betting on.

Margin-to-CAC

POD products often have thin margins. In a sense, the convenience of having someone else hold your inventory comes at a cost. A Bella+Canvas t-shirt printed with your design via Printful might cost $13. A $19 sales price leaves $6 to cover promotion and shipping.

This problem is not new. POD shops have always had to monitor customer acquisition costs to ensure decent profits.

What changed is the acquisition environment.

Meta’s Advantage+, Google’s Performance Max, and similar AI-bidding systems often optimize based on conversion data. These systems may produce better results for merchants with a sales history, trained pixels, broad product catalogs, and sufficient budget to allow the algorithms to learn.

For new stores, this is a cold start. A POD merchant must spend money to generate the conversion data the ad system needs, but each early sale will cost more than the product’s margin can absorb. Ads are initially more expensive.

POD Success

None of these challenges needs to end POD ecommerce. AI may ultimately create more opportunities for disciplined merchants.

The same tools making product discovery relatively more difficult are also reducing the cost of creating products, testing ideas, composing copy, generating images, and launching storefronts. A single entrepreneur can now operate with capabilities that once required a team.

Successful POD businesses will combine strong merchandising, clear positioning, structured product data, and patient customer acquisition strategies rather than simply relying on cheap traffic or viral designs.

Google Analytics Adds AI Assistant As Default Channel Group via @sejournal, @MattGSouthern

Google Analytics added an “AI Assistant” default channel group for traffic from recognized AI chatbot referrers, with Google naming ChatGPT, Gemini, and Claude as examples.

GA4 property owners no longer need to build custom channel groups with regex patterns to separate AI assistant visits from referrals. Until now, all AI chatbot traffic landed in the Referral bucket by default.

What’s New

The update touches three traffic source dimensions at once.

When Google Analytics detects a referrer matching a recognized AI assistant, it assigns “ai-assistant” as the medium value. Those sessions then get grouped under the “AI Assistant” channel in Default Channel Group reports. The campaign dimension receives a reserved “(ai-assistant)” label.

All three changes happen automatically. Property owners don’t need to configure anything.

Google described the update as a way to “monitor how generative AI impacts your business by tracking user clicks, trending AI sources, and how this traffic compares to traditional channels like organic search.”

Google hasn’t published the full list of recognized AI assistant referrers. The Help Center entry names ChatGPT, Gemini, and Claude as examples.

Context

Google has been working toward this for almost a year. In August, the Analytics team published guidance on building custom channel groups with regex patterns to capture AI assistant traffic. That guidance named ChatGPT, Gemini, Microsoft Copilot, Claude, and Perplexity as platforms to track. That marked the point when Google’s own documentation began treating AI assistant traffic as a category worth measuring separately.

The custom channel group workaround had limitations. Regex patterns required manual maintenance as AI platforms changed domains. Property owners needed editor-level access to set them up. And the two-custom-channel-group limit in GA4 meant dedicating one of only two available slots to AI tracking.

This follows a pattern Google set in 2022 when it added “cross-network” as a default channel group to capture Performance Max and Smart Shopping traffic. That update also moved traffic out of a generic bucket into its own dedicated channel without requiring manual configuration.

AI traffic attribution has been a recurring measurement challenge. Last year, Google fixed a bug that caused AI Mode search traffic to be reported as “direct” instead of “organic” in GA4 after a noreferrer code was stripping referrer headers. Google also added AI Mode data to Search Console performance reports, though that traffic gets blended into existing totals rather than appearing as a separate category.

Why This Matters

Anyone running a custom channel group to track AI assistant traffic may be able to simplify that setup as the native channel appears in reports. The native channel may reduce the need for the regex patterns and manual channel ordering that Google recommended last year.

Properties without custom AI tracking will start seeing this traffic broken out from referrals automatically. Sessions that previously appeared as generic referral traffic from chatgpt.com or claude.ai will have their own channel.

One gap worth watching is the referrer limitation. AI assistant traffic that arrives without a referrer header still lands in Direct. This can happen through in-app browsers and mobile apps, or when users copy and paste links. The new channel only captures what GA4 can identify through the referrer.

Looking Ahead

Google hasn’t published which AI assistants are on the recognized referrer list beyond the three named examples. It also hasn’t said how the list will be updated as new platforms launch. The August 2025 custom channel group guidance named five platforms, but the new automatic system doesn’t specify its full coverage.

The Default Channel Group definitions page hasn’t been updated to include “AI Assistant” in its channel table yet, so the full technical definition isn’t available to review. The custom channel group regex patterns Google published last year can still cover platforms that aren’t on the recognized referrer list.


Featured Image: Stocking/Shutterstock

Stop Treating AI Visibility As One Problem. It’s Actually Three, On Three Different Layers via @sejournal, @DuaneForrester

When a brand stops appearing in ChatGPT, or when its share of voice in Perplexity drops by half over a quarter, the typical response from the marketing org is to write more content. Sometimes a lot more. The thinking goes that if AI systems aren’t surfacing the brand, the fix is to feed them more material to work with. That instinct is a misdiagnosis. It’s a retrieval-layer fix being applied to what is increasingly a different kind of problem entirely, and the cost shows up as wasted budget, missed quarters, and a creeping sense that the work isn’t connecting to the outcomes anymore.

The mistake is treating AI visibility as a single problem when it isn’t. There are three structurally different layers between your brand and the answer a user receives, each with its own failure modes, its own fixes, and increasingly its own organizational owner. Diagnose the wrong layer, and the fix doesn’t land.

Where Most Of The Conversation Has Been Living

The first layer is retrieval. This is where the AI search optimization conversation has spent most of the last two years. The mechanics are familiar in shape if not in detail. When a model needs to answer a question grounded in real-world content, it pulls relevant material from external sources and uses that material to construct the response. The technical name is retrieval-augmented generation, or RAG, and the layer it operates on is the gateway between your content and the model’s output.

This is where crawlability, parseability, and chunk-friendliness do their work. If your content can’t be retrieved cleanly, nothing downstream matters. The visibility tracking platforms most marketing teams have evaluated this year measure outcomes that depend on this layer functioning, which is why they tend to reward the same disciplines that produced good results in classical search: structured content, schema markup, self-contained answers, clean technical implementation.

But retrieval has a structural limit, and Microsoft Research has been unusually direct about it. Plain RAG, in their words, struggles to connect the dots. It retrieves chunks of text that look relevant to the question, but it cannot reason about how those chunks relate to each other. When the answer requires synthesizing information across multiple sources, or when the question is broad enough that the right answer depends on understanding patterns across an entire dataset, retrieval alone breaks down. The model gets the chunks and has to guess at the relationships, and guessing is where hallucinations enter.

The discipline question this layer asks is straightforward. Can the model retrieve our content at all, and is it retrieving the right content for the right query? Most marketing teams have some version of this work in flight already, even if the specific tactics have shifted from classical SEO. But retrieval is only the gateway. Even when a model retrieves your content correctly, what it does with it depends on whether you exist as a recognized thing in the layer above.

The State of AEO/GEO Report Conductor 2026

Where Entity Recognition Does The Real Work

The second layer is the relationship layer, and the dominant structure on it is the knowledge graph. The major search infrastructures all maintain one. Google’s Knowledge Graph, Microsoft’s Satori, and the open knowledge graph built on Wikidata and schema.org collectively define how your brand is represented as an entity, what category you sit in, and which other entities you’re connected to.

This is the layer that decides whether AI Overviews and large language model responses treat you as a recognized member of your category, or as one fuzzy candidate string among many. Brands that exist as clean, well-defined entities get cited consistently. Brands that exist as undifferentiated tokens scattered across the open web get pattern-matched against fifty other candidates and lose more often than they win.

Knowledge graphs have been around long enough that the discipline is reasonably mature. Schema markup on owned properties, consistent naming and identifiers across the open web, structured presence on the high-trust nodes like Wikidata entries and review platforms, and the slow accumulation of brand mentions in contexts that the graph treats as authoritative. This is where the unlinked brand mentions conversation lives, because consistent contextual mentions strengthen the entity even without a hyperlink attached. The fix at this layer is structural rather than volume-based. Writing more content does almost nothing if the entity definition underneath it is fuzzy.

The discipline question here is harder than the retrieval-layer question. Are we a clean, defensible entity in our category, or are we still being pattern-matched against fifty other candidate strings? A brand that can’t answer that question affirmatively is going to lose ground in AI search, regardless of how much content it produces, because the second layer is where the model decides what your content is actually about.

The knowledge graph tells the model what your brand is. But increasingly, your brand has to function inside a third layer that most marketing teams haven’t met yet, where the model isn’t just understanding you, it’s being asked to reason about you on behalf of someone making a decision.

The Layer Enterprise Companies Are Quietly Building Right Now

The third layer is the context graph, and this one needs a careful introduction because most of the marketing conversation hasn’t reached it yet.

A context graph has the same structural shape as a knowledge graph, with entities, relationships, and typed connections, but it’s grounded differently. A knowledge graph models the world. It tells you what things are and how they relate in general. A context graph models a specific organization’s data, decisions, policies, and operational reality. The cleanest framing I’ve seen calls a knowledge graph the library and a context graph the operating manual written by the people who actually run the place. The library tells you what exists. The operating manual tells you what’s relevant, what’s authorized, and what to do about it right now. The library is read-only semantic infrastructure. The operating manual is a living operational layer that grows every time a business process executes.

What separates a context graph from anything that came before it is that governance lives inside the graph rather than alongside it. Policies, permissions, validity windows, and authorization rules are nodes the graph itself queries, not external documentation applied at the edges. When an agent retrieves something from a context graph, the result has already been filtered through what’s currently authorized, currently valid, and currently applicable. The graph is also continuously evolving, so what it knows about you this week is not necessarily what it knew last quarter. That’s where the word “governed” comes from when people in this space talk about governed retrieval. It isn’t a frame, but rather the architecture.

That architecture used to be invisible to anyone outside the organization that built it, which is why marketers haven’t had to think about it. That changed at Google Cloud Next ’26, when Google introduced the Knowledge Catalog inside its new Agentic Data Cloud. Google’s own description of the product, written in their own first-party blog content, says the Knowledge Catalog constructs a unified, dynamic context graph of your entire business, enabling you to ground agents in all of your business data and semantics. That sentence is the moment the term left the data-engineering blogs and entered enterprise procurement vocabulary.

The reason this matters for marketing is that context graphs are what’s going to power the next generation of agents inside your enterprise customers. Gartner projects that 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. Procurement agents, competitive intelligence agents, content strategy agents, vendor evaluation agents. These agents won’t be reasoning about your brand from the open web. They’ll be reasoning about your brand from inside their company’s context graph, and what that graph says about you depends on what got ingested into it.

That ingestion is where the work for marketing lives. The brand that arrives at the context graph fragmented arrives weak. If your category positioning is inconsistent across owned and earned media, the graph picks up the contradictions and represents you ambiguously. If your entity data is fuzzy on the second layer, it stays fuzzy when it gets pulled into the third. If your third-party signal is thin or contradictory, the graph has nothing solid to anchor to. The work is upstream of the graph, but the consequences land downstream of it, inside an agent’s reasoning process that you’ll never see directly.

I think of this discipline as governed visibility. The practice of making sure your brand arrives at the context graph in a state that holds up under governed retrieval. Clean entity definition, consistent third-party representation, reliable structured data, and a category position that doesn’t fall apart when an agent traverses the relationships around it. Governed visibility isn’t a new tactic stack. It’s the result of doing the second-layer work well enough that the third layer has something solid to ingest.

The discipline question at this layer is the one most marketing teams haven’t started asking yet. When an agent inside our customer’s company is reasoning about us, what does it find, and is the version of us it finds the version we’d want it to act on?

Three layers, three different problems, three different fixes. But also three different responsibility zones, and that’s where most teams are quietly losing ground.

The Reason Most Teams Will Lose This Even Though They’re Working Hard

Each layer maps to a different organizational responsibility, and most marketing teams only own one of the three cleanly.

  • The retrieval layer is shared with web, dev, and sometimes IT. Marketing influences what gets published, but the infrastructure that makes content retrievable sits in someone else’s domain.
  • The knowledge graph layer is genuinely marketing’s territory. Schema discipline, entity definition, third-party signal, brand consistency, the slow structural work that compounds over years.
  • The context graph layer is where IT owns the infrastructure inside the customer’s organization, but marketing has to influence what gets ingested. The work is upstream, and the consequences land downstream, often invisibly.

The teams that win in 2026 are the ones that figured out how to operate across all three responsibility zones rather than perfecting their work on just one. Most teams I see are still optimizing their owned content, which is the retrieval layer, while losing ground on entity definition, which is the knowledge graph layer, and remaining completely absent from the context graph conversation, which is the layer where some enterprise businesses are quietly standing up right now.

The work isn’t writing more content. The work is figuring out which layer the problem actually lives on, and building the disciplines to operate on all three. Governed visibility is the third-layer discipline that marketing is going to have to develop, whether or not the term sticks. The brands that build it now will look prepared in eighteen months. The brands that don’t will be wondering why their content investments stopped producing the visibility they used to.

If any of this lands or contradicts what you’re seeing inside your own teams, I want to hear about it. Drop a comment about which layer your work has been concentrated on, where you’re seeing the gaps, or where the responsibility zones break down inside your organization. The patterns are still forming, and the conversations in the comments tend to be fresher than anything else.

A lot of the measurement frameworks for this kind of work sit in The Machine Layer, which expands the original 12 KPIs for the GenAI era into something teams can actually run against.

The State of AEO/GEO Report Conductor 2026

More Resources:


This was originally published on Duane Forrester Decodes.


Featured Image: Master1305/Shutterstock; Paulo Bobita/Search Engine Journal

Direct Traffic & Popularity – Correlation, Not Causation via @sejournal, @TaylorDanRW

Last week, Cyrus Shepard published an AI citation ranking factors study, and it created a lot of noise on X, LinkedIn, and a number of private WhatsApp groups I’m in. Not only the distinction between what is a factor, and what is a correlation, especially given a lot of studies in SEO and AI are multifarious and have high levels of imponderable complexity. To be clear, this isn’t a criticism of Cyrus’s work; the study is excellent, and the correlation/causation caveat is one he makes himself explicitly.

This led me to think about the parallels with other ranking factor studies done previously, which have implied direct traffic is a considerable traditional SEO ranking factor. At the time, these studies received a lot of negative feedback, and this was again discussed by many online after the documentation in Google’s DOJ trial revealed a “popularity” signal.

It makes sense for direct traffic to be a component of how popularity is measured through Chrome. Google uses Chrome data to find new websites. It also judges a page’s “quality” based on how users interact with it after clicking, but the atomic levels of how this is done, and how much weight the variables here carry, are not public knowledge.

Direct Traffic x Popularity Correlation

Direct traffic is widely considered a symptom of good performance, not a primary driver of search rankings.

Treating direct traffic as a ranking factor leads to a misinformation loop, which encourages superficial, low-effort tactics, such as purchasing bot traffic, in a misguided attempt to boost popularity, as it’s very possible to have high levels of direct traffic and poor SEO performance.

A wider view suggests that high direct traffic is typically an indicator of a strong brand, correlating with genuine ranking factors like numerous brand searches, high-quality backlinks, and strong social engagement.

These elements are the true causes of high ranking; the direct traffic merely serves as a quantifiable measure of the brand’s overall health and success, an “all ships rise in high tides” effect.

Spikes in direct traffic, don’t correlate with Organic Search traffic. (Image from author, May 2026)

If Chrome data were a direct factor, a sudden spike in browser activity on a specific URL would immediately push it up the SERPs, and this would be a gameable exploit.

This would also be something Google would pick up as it looks to stamp out obvious manipulations of search ranking, and this would have happened many years ago.

Other Insights From The DOJ Files

NavBoost and Glue are specialized systems within Google’s infrastructure that focus on user interaction signals rather than the raw volume of direct traffic.

NavBoost looks at historical clickstream data and user behavior on search results to identify which pages are most relevant for specific queries, effectively acting as a memory of what users have found helpful.

While NavBoost focuses on traditional organic results, Glue extends those same user interaction principles to all other SERP features: knowledge panels, video carousels, image packs, and featured snippets.

They allow Google to gauge a site’s authority based on how users interact with it in the search ecosystem, independent of the user’s traffic source.

→ Read more: What The Google Antitrust Verdict Could Mean For The Future Of SEO

So, What Is Popularity?

Based on what we know from various official (and unofficial) sources, research, and the general SEO hive mind, we can define popularity as a sign of brand strength characterized by user behaviors such as autocompletes and bookmarks.

It functions as a correlation to high rankings because it naturally aligns with the various signals that make a page rank.

Google may avoid using Chrome data directly as a ranking factor, choosing instead to use it as a dataset to train or validate its AI models. This we don’t know, and we likely won’t be able to prove or disprove through research.

Thank you to Ryan Jones, Mark Williams-Cook, Chris Green, Gerry White, Kristine Schachinger, Charlie Whitworth, Emina Demiri Watson, (and anyone else I’ve missed) for the fun weekend discussions on this topic.

More Resources:


Featured Image: PerfectWave/Shutterstock

How To Measure AI Search: Current KPIs You Need To Know [Webinar] via @sejournal, @hethr_campbell

If your organic traffic is down but your pipeline looks fine, you’re not imagining it. AI-generated answers are intercepting the journey earlier, meaning users are getting what they need from a citation or a recommendation before they ever hit your site. The click never happens. But the influence did.

That’s the measurement problem most marketing teams haven’t solved yet, and the KPIs they’re reporting on weren’t designed to catch it.

Your Brand Can Appear In 1,000 AI Responses & GA4 Shows Nothing

Citations, brand mentions, and AI recommendations don’t pass through your tag manager. They don’t fire an event in GA4 or register a session in your CRM. They happen in the interface of the AI tool, and by the time a user reaches your site, or doesn’t, the influence has already occurred.

Tracking these signals requires monitoring AI outputs directly: which queries surface your brand, in which tools, and with what frequency and context.

That’s a different data collection layer entirely from what most teams have in place.

Learn more in our upcoming SEO webinar.

Ways To Connect AI Signals To Business Outcomes Across Every Channel

Once you’re capturing AI visibility signals, the next problem is connecting them to outcomes.

Last-click and even multi-touch attribution models weren’t designed for journeys where the most influential touchpoint leaves no clickstream trace.

Learn: Incrementality testing, which lets you isolate the lift that AI visibility is actually driving by comparing performance across exposed and unexposed segments.

Learn: Media mix modeling, which takes a broader view, quantifying AI’s contribution alongside paid, organic, and direct channels in a single revenue model.

Used together, they give you a defensible number to bring into a budget conversation.

The Three-Layer Stack That Makes AI Search Defensible in a Budget Review

The stack works in sequence.

At the top, you’re monitoring AI visibility: citation rate, share of voice in AI responses, and brand mention frequency across tools like ChatGPT, Gemini, and Perplexity.

In the middle, incrementality and MMM translate that visibility into estimated conversion impact.

At the bottom, you’re tying those estimates to pipeline and revenue data so the whole chain holds up under scrutiny. The teams getting this right aren’t using one new metric. They’re connecting three existing disciplines, SEO, media measurement, and analytics, around a shared data model.

DAC’s Felicia Delvecchio, VP of Media, Vincent DeLuca, Director of SEO, and Gavin Bowick, Lead Web Analytics are running through exactly how that model is built in a free live session.

What This AI Search & Revenue Webinar Covers

  • How to track AI visibility signals: citations, mentions, and recommendations, across the full funnel
  • Which incrementality and cross-channel models connect AI influence to actual revenue outcomes
  • Which KPIs to retire in 2026 and which metrics reflect real performance across SEO, paid, and AI channels
  • How to build a reporting structure that aligns across SEO, media, and analytics teams, and holds up when you’re presenting to leadership

This one is worth showing up live for.