Meta Doesn’t Know What Business It’s In & The Traffic Data Shows It via @sejournal, @gregjarboe

On Friday, May 8, 2026, The New York Times published a guest essay by investigative journalist Julia Angwin with a headline that demands attention: “Meta Is Dying.” She highlights that Meta lost daily active users in Q1 2026, falling from 3.58 billion in Q4 2025 to 3.56 billion.

Angwin sees this as the beginning of a long, slow decline, comparing the company’s trajectory to AOL in 2003 and Yahoo in 2015: technically alive, still profitable, but entering what she bluntly calls the “zombie era.”

She may be right. And if she is, Theodore Levitt told us exactly why this would happen, 66 years ago.

The Lesson Meta Never Learned

In 1960, Harvard Business School professor Theodore Levitt published “Marketing Myopia” in the Harvard Business Review. His central argument was that companies fail not because demand disappears, but because they define their business too narrowly. Railroads collapsed because they thought they were in the railroad business rather than the transportation business. Trolley car companies were replaced by automobiles they could have pioneered. “People don’t want a quarter-inch drill,” Levitt wrote. “They want a quarter-inch hole.”

Now look at Meta’s six major pivots over 22 years and ask: What business did Mark Zuckerberg actually think he was in?

In 2021, he declared the answer was “the metaverse business” – a bet whose Reality Labs division has since accumulated roughly $80 billion in operating losses. Users didn’t agree. In 2023, he pivoted to generative AI and has since committed over $100 billion to building models that, as Angwin notes, currently perform worse than the competition. Q1 2026 results show record revenue of $56.3 billion, up 33% year over year, but also $33.44 billion in total costs, a 35% increase, and an AI spending outlook that has rattled investors.

The revenue looks strong. The trajectory looks like a company that keeps pivoting to new product definitions while its core users quietly disengage.

What The Traffic Data Actually Shows

This is where opinion meets evidence, and the Similarweb traffic for March 2026 is instructive.

Google leads the world with 86.9 billion monthly visits. YouTube follows with 29.3 billion. Facebook comes in third at 11.9 billion, and Instagram comes in fourth at 7.1 billion. That gap between Google and Facebook, is the data equivalent of what Levitt was describing. Google defined itself as being in the information access business. Facebook defined itself as being in the social network business. One of those definitions scales indefinitely. The other runs out of room.

The AI category data is even more pointed. ChatGPT records 5.7 billion monthly visits globally, with year-over-year growth of 28.5%. Gemini is growing sharply at 283.8% YoY. Claude.ai jumped 423.7% to 613.7 million visits YoY.

Meta.ai does not appear in the top 100 most-visited websites.

Meta spent $100 billion entering the AI race. It is not winning it.

The Squeeze Play Angwin Describes

When an aging platform’s user base starts to shrink, the immediate response is almost always the same: monetize harder. Angwin documents this clearly. Meta’s Q1 ad impressions increased 19% year over year while average ad prices rose 12%. Revenue per user jumped 27%. The company is cramming more ads onto its platforms and charging advertisers more for each one.

This is the move that maximizes short-term revenue while accelerating long-term decline. More ads mean a worse user experience. A worse experience means slower growth. Slower growth means the ad inventory eventually stops expanding. Levitt described this as the trap companies fall into when they focus on selling their current product harder rather than understanding what customers actually need.

For digital marketers and SEO professionals, this creates a near-term concern. Meta’s Advantage+ advertising suite delivers genuinely strong performance data – a $4.52 return per dollar spent, 22% higher than comparable manual campaigns, according to Meta’s own earnings reports. But those returns depend on a healthy, engaged user base generating meaningful behavioral signals. If the user base contracts and ad load increases simultaneously, signal quality degrades, and performance follows.

The Counterargument Worth Taking Seriously

Angwin’s essay is persuasive, but she is writing opinion, not analysis, and the full Q1 picture is more complicated than “dying” suggests. Year-over-year, Meta’s daily active user base still grew 4%. The quarter-over-quarter decline has a partially verifiable explanation in internet disruptions in Iran and Russia’s WhatsApp ban. Revenue growth of 33% is not the profile of a company in terminal decline.

What it is, is the profile of a company spending at a scale that requires the growth to continue, while its AI investments have not yet produced meaningful new revenue streams. As the Wall Street Journal‘s Asa Fitch observed this week, “the spending growth looks increasingly unsustainable.”

Levitt’s lesson wasn’t that myopic companies always die quickly. AOL and Yahoo lingered for years. The lesson was that once a company loses the plot on what business it’s actually in, recovery becomes structurally difficult. Every dollar spent defending the wrong definition is a dollar not spent understanding the customer.

The question Levitt would ask isn’t whether Meta is dying. It’s whether Meta has ever clearly understood what business it was actually in. Across six pivots in 22 years, the answer appears to be: not consistently.

That uncertainty is now visible in the traffic data. And traffic data doesn’t lie.

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Featured Image: Roman Samborskyi/Shutterstock

SERP FAQ Removal & New Data Challenge Schema’s AI Search Value via @sejournal, @MattGSouthern

Schema markup had a rough week. Google ended FAQ rich results. Four days later, Ahrefs published a report, finding that adding JSON-LD didn’t produce a clear citation lift across Google AI Overviews, AI Mode, or ChatGPT.

These developments weaken two common pitches for schema markup: increased SERP visibility and potential AI citation gains. This article examines their implications and what the data indicates about schema’s future.

Google’s Visible Schema Rewards Have Been Narrowing For Years

Google has been pulling back visible Search rewards tied to specific structured data types since 2023. Google restricted FAQ rich results to authoritative government and health sites, and HowTo rich results were limited to desktop and later deprecated.

In 2025, Google announced the retirement of several structured data features, including Course Info, Claim Review, and Estimated Salary. Book Actions was initially included but later carved out after Google removed its deprecation banner. Google called the remaining retirements “not commonly used in Search” and no longer providing value to users.

In 2026, Practice Problem structured data was deprecated. John Mueller noted on Reddit that “markup types come and go, but a precious few you should hold on to.”

The pattern is that visible structured data rewards have disappeared after becoming familiar SEO tactics. The markup itself stays valid, but the rich result doesn’t. Google doesn’t always describe these removals as responses to overuse, but the pattern offers less reason to treat any single markup type as a durable strategy.

These recent updates differ because the evidence for one proposed replacement value also weakened. The “GEO” advisory space claims schema boosts AI citations, and Ahrefs data tested part of that.

What The Ahrefs Report Found

Ahrefs tracked 1,885 web pages that added JSON-LD schema. Each page was matched against control pages that never added schema. Citation changes were measured across Google AI Overviews, AI Mode, and ChatGPT.

The results were flat. Google AI Mode showed +2.4%, ChatGPT showed +2.2%, and Google AI Overviews showed -4.6%.

The first two were too small to tell apart from random variation. The AI Overviews decline was statistically significant, but Ahrefs said it can’t confidently attribute that to schema.

Every page in the dataset already had more than 100 AI Overview citations before any schema was added. These pages were already being crawled and cited.

Ahrefs acknowledged that for pages not yet visible to AI, schema might still help with crawling, parsing, or indexing. But their data can’t confirm that.

Gianluca Fiorelli, a strategic SEO consultant, called the study “one of the more honest pieces of research to come out of the AI Search space in 2026.” But he argued the scope was narrower than the headline suggested. He compared it to “testing whether adding a label to a bottle already on the supermarket shelf makes customers pick it up more often.”

Ahrefs also cited a searchVIU experiment that found five AI systems relied on visible HTML during direct page retrieval and did not use hidden JSON-LD, Microdata, or RDFa. That finding covers one stage of the pipeline. It does not rule out schema playing a role earlier in indexing or entity understanding.

Ryan Law, Ahrefs’ director of content marketing, summarized the finding on LinkedIn, saying:

“Does adding schema markup help your pages get cited in AI search? Probably not,” he wrote. He added that schema is “probably not some magic fix for improving your AI citations.”

The Practitioner Debate

Both updates land in the middle of an active argument about schema and GEO.

Roughly 168,000 pages use the phrase “FAQ schema is critical for GEO,” according to search results that Lily Ray, VP of SEO and AI Search at Amsive, flagged on LinkedIn. She called the trend familiar.

“Anything that can be spammed in SEO, will be spammed,” Ray wrote. She’d warned about this in a 2019 Moz article when FAQ schema first launched, and described Google’s FAQ removal as the same cycle repeating.

Ray hedged throughout her post, calling it “putting on my tin foil hat” and “just an idea.” But the pattern she described is the same one visible in the timeline above. A useful markup type gets scaled as a tactic, Google pulls the reward, and the industry moves on to the next one.

Joost de Valk, founder of Yoast, made the connection explicit in a blog post. “The GEO industry is replaying early SEO, just faster,” de Valk said. “And the FAQ schema deprecation is the first concrete proof point that the cycle is back on.”

He also filed a Schema.org proposal for a new FAQSection type to address what he sees as the structural problem, separating “this page has an FAQ section” from “this page IS an FAQ.”

The frustration was sharpest from practitioners who’d been watching the GEO playbook harden around schema as its most concrete recommendation. Mark Williams-Cook, director at Candour and founder of AlsoAsked, shared the Ahrefs report on LinkedIn.

“GEO bros are selling snake oil with schema to boost citations, but people like Gianluca Fiorelli are talking sense,” he posted.

Marie Haynes, founder of Marie Haynes Consulting, commented on Ray’s post with a different theory altogether.

“My theory is that Google needed our FAQs to train AI so they gave us incentive to add them (aka rich results.) And now they don’t need them anymore,” she wrote. The theory is unconfirmed by any primary source, but it shows how far the speculation has traveled.

Some practitioners pushed back on the gloomier readings. Google’s broader guidance still presents structured data as a way to make page information machine-readable, and at a 2025 Search Central Live event in Madrid, the Search Relations team told practitioners that supported structured data types are still worth using.

What The Data Can’t Answer Yet

Whether schema helps pages that aren’t yet being cited is a separate question that the data can’t answer, because every page already had more than 100 AI Overview citations before schema was added.

The test also pooled all schema types together. Article, FAQ, Product, HowTo, and Organization were all treated as one category. Type-specific effects haven’t been isolated, and they could look different.

The 30-day measurement window may miss slower effects, and on live websites, schema changes can overlap with other page changes, making it hard to separate what schema did from what changed around it. The report only examined schema in the page’s HTML, not schema injected via JavaScript, which AI crawlers treat differently.

Ahrefs measured Google AI Overviews, AI Mode, and ChatGPT. Whether Bing, Copilot, Perplexity, Claude, or other answer systems treat schema differently from the systems Ahrefs measured is an open question.

Google’s FAQ deprecation notice says the company will continue using FAQ structured data to “better understand” pages. What that produces in measurable terms is unclear. The same uncertainty applies to whether schema affects citations indirectly, through eligibility, entity understanding, or source selection, rather than during the direct retrieval that searchVIU tested.

Nobody has published data that isolates that path.

Why This Matters

The Ahrefs data gives no measured reason to add JSON-LD, expecting short-term AI citation gains for pages already visible in AI Overviews. The trickier question is what to do with schema strategies more broadly.

Product, Review, Event, Video, and some other structured data types still support active rich result features. Organization, Person, and Article markup can still help describe entities and content, even when the payoff is less visible.

A blanket “schema doesn’t work” reading overstates what the data showed, because the test pooled all types and measured only one outcome. What the data does challenge is a specific sales pitch.

“Add schema to boost AI citations” has been one of the more concrete recommendations in GEO guides. For example, Frase.io called schema markup “critically important for AI search, GEO, and AEO.”

Without data support for that claim, it’s harder to justify the investment. AI systems in searchVIU’s test relied on visible HTML during retrieval, not JSON-LD. That suggests content structure, clear headings, and direct answers in prose may matter more for AI citation than markup structure.

Looking Ahead

The question hanging over the SEO industry is where schema creates measurable value. Adding JSON-LD didn’t measurably increase AI citations for pages already visible in AI Overviews.

For those pages, schema looks more like plumbing that serves other systems than a lever that moves citation counts. That’s still real value, but it’s a different pitch.


Featured Image: BEST-BACKGROUNDS/Shutterstock

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The world is on track to miss its health targets

Every year the World Health Organization publishes a global health statistics report. It features the numbers behind world health trends and, importantly, assesses whether we’re on track to reach ambitious goals set in 2015. It’s a bit like a health grade.

The 2026 report was published on Wednesday. And the results aren’t looking brilliant. While we are seeing some improvements, they are uneven, and they’re far too slow.

The targets themselves are part of the United Nations’ Sustainable Development Goals, a sprawling and ambitious plan focused on improving life around the world. The 17 goals were set to tackle poverty and climate change and to boost education, gender equality, health, and well-being, among many other quality of life issues. Those targets were meant to be met by 2030.

Perhaps they were a little too ambitious. Here are the numbers and statistics that stood out to me on this year’s world health report card.

1.3 million new cases of HIV in 2024

Before the SDGs, there were the Millennium Development Goals. One MDG target was to halt and reverse the spread of HIV—and that target was exceeded by 2015. Back then, we were considered on track to “end the AIDS epidemic by 2030.”

How depressing, then, to see that in 2024 there were an estimated 1.3 million new cases of HIV. That’s 40% lower than the figure from 2010. But it’s still 1.3 million additional people with HIV. The SDG target is to reduce HIV incidence by 90% by 2030—we’re not likely to meet it.

10.7 million new cases of TB

The picture is even bleaker for tuberculosis, which ranks 10th on the WHO’s list of top global causes of death. The goal was to reduce cases by 80% between 2015 and 2030. So far, cases have only fallen by a measly 12%. And when you break the change down by region, the Americas saw an increase of 13%

An 8.5% rise in malaria cases

And then there’s malaria, the mosquito-borne disease with a 7% fatality rate. The European region has been free of malaria since 2015, but the disease is a significant concern in many countries in the Global South, particularly in Africa. The goal was to lower rates by 90% between 2015 and 2030. In 2024, there were an estimated 282 million cases of malaria globally—representing an 8.5% increase in incidence rates.

Antimalarial drug resistance is a major challenge here—forms of the malaria virus that are resistant to drugs have been confirmed or suspected in eight countries in Africa, according to a separate WHO report. Mosquitoes that are resistant to commonly used insecticides are present in nine African countries. And climate change, which can alter mosquito habitats, may be making things worse.

42.8 million children are wasting

We’re not meeting child health targets, either. Take malnutrition, for example. As of 2024, the global prevalence of wasting in children was 6.6%—that’s a staggering 42.8 million children who are literally wasting away because of a lack of adequate food. On the other end of the spectrum, 5.5% of children are now considered overweight. Both figures were meant to be below 5% by 2030, which now seems unlikely.

Vaccination rates are dropping in the Americas

Progress in improving childhood vaccination coverage has stalled. Globally, an estimated 76% of children are getting their second dose of a measles vaccine—a figure far below the the approximately 95% needed to prevent outbreaks. The Americas currently has lower rates of vaccine coverage for three of the four “core” vaccines than it did in 2015.

This is partly due to a lack of investment, says Goodarz Danaei, an epidemiologist at the Harvard T.H. Chan School of Public Health. “But now we have a misinformation campaign going around vaccines that makes it worse,” he adds.

The covid-19 pandemic didn’t exactly help, either. The impact on health services led to millions of children missing out on routine vaccinations.

22.1 million pandemic-related deaths

And of course the pandemic affected progress toward health goals in more direct ways: 7 million people died of covid-19. The WHO report estimates that, for each of these, there were an additional two “excess” deaths related to the pandemic, due to disruptions in health care, for example. That puts the total figure at 22.1 million pandemic-related deaths.

A woman dies every two minutes from “maternal causes”

Maternal mortality rates fell by about 40% between 2020 and 2023. But today’s rate equates to 712 maternal deaths every single day. That’s one every two minutes. The WHO report notes that we’d have to reduce the mortality rate by almost 15% per year in order to meet the 2030 target. This seems incredibly unlikely, particularly given the recent decimation of US funding for global aid programs, which is expected to result in thousands of additional maternal deaths.

Progress has also slowed in reducing the risk of death from noninfectious diseases like cancer, diabetes and cardiovascular disease. “Overall, neither the world nor any WHO region is currently on track to meet the 2030 SDG target,” the report states.

2.1 billion people struggle to afford health care

Despite plans to make health care more affordable, a significant chunk of the population is being pushed into poverty by health-care costs. In 2022, 2.1 billion people faced financial hardship due to health spending—and 1.6 billion of them were living in or had been pushed into poverty.

Across the board, there have been some important improvements in global health. But the achievements have not gone far enough. “The good news is that there is progress,” says Danaei. “But as always, the glass is half empty.”

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.

How Chinese short dramas became AI content machines

In a dimly lit bedroom, a frightened young woman is thrown onto a bed by a tall, muscular man. He grabs her hand, and flame-like vines crawl across her body, fusing with her flesh. She levitates, then drops. A dragon-shaped tattoo appears across her chest.

“Two months,” the man says. “Give me an heir, or I will eat you.”

The scene is from Carrying the Dragon King’s Baby, one of the many hundreds of short dramas that appear on apps like DramaWave and ReelShort. There’s just something about this one that isn’t quite right. The lighting may be glossy and cinematic, but the show has an odd visual texture like something between a movie and a video game cutscene. 

That’s because Carrying the Dragon King’s Baby is part of a new trend for making these shows entirely with AI: no actors, camera operators, cinematographers, or CGI specialists required.

China’s short drama industry has boomed since its launch, in 2018. These ultrashort, melodramatic, and often smutty shows are designed for smartphone viewing, with episodes often running just one or two minutes long: Viewers can finish an entire series in as little as 30 minutes to an hour. The films are made for endless scrolling, packed with emotional confrontations and melodramatic plot twists. The trend’s growth is driven by apps that bombard TikTok, Instagram, and Facebook with cliffhanger-heavy ads designed to lure viewers into buying subscriptions. In 2024, China’s short drama market reached roughly $6.9 billion in revenue, surpassing the country’s annual box office earnings for the first time. 

Since 2022, Chinese short drama companies have aggressively expanded overseas, translating existing hits and producing localized series featuring local actors. Globally, short drama apps have approached a billion cumulative downloads. The United States is the biggest market outside of China, providing around 50% of the revenue, according to research firm DataEye.

Now the industry is reinventing itself. Chinese short drama companies—already masters of low-budget, algorithmically optimized entertainment—are embracing generative AI to produce content faster and cheaper than ever. An average of 470 AI-generated short dramas were released every day in January, according to DataEye. Short-drama companies like Kunlun Tech are ramping up AI productions, shrinking film crews, and reorganizing the labor pipeline from the ground up. For some studios, AI has moved from being a supporting tool to providing the backbone of production itself.

Infinite stories, infinite tropes

Short dramas are already famously low-budget. But AI has made them dramatically cheaper to mass-produce, helping to accelerate the entire process—and save money. Production timelines have collapsed. Conceptualization, script writing, casting, shooting, and editing used to take three to four months. With AI, the process can now take less than a month, says Tang Tang, vice president at short-drama platform FlexTV. Producing a short drama in North America once cost roughly $200,000, but AI can cut that cost by 80% to 90%, according to Tang.

After expanding into the US market, Chinese short drama companies largely followed the same playbook they used in China: Buy traffic aggressively on TikTok, Facebook, and YouTube; offer a handful of free episodes; then charge viewers to unlock the rest inside the companies’ apps. Decisions about what to produce next are often driven less by creative instinct than by performance data. “We look at what themes, plotlines, and writers resonate with audiences, then quickly adjust,” says Tang.

The industry operates at a relentless pace. “Everyone expects quick returns,” Tang says. “In China, if a series doesn’t break even within a month, the industry considers it a failure.” 

As a result, screenwriters who spoke with MIT Technology Review said platforms often categorize projects using highly specific keywords that encompass everything from genre and setting to plot structure, such as “campus romance,” “gang rivalry,” “enemies to lovers,” or “rags to riches.” Recently, one of the most popular genres has been “reborn revenge,” a fantasy trope in which a wronged protagonist is miraculously reborn and given a chance to change their fate.

“You kind of have to keep the emotional intensity extremely high throughout the show, using the same plot devices over and over again: sudden deaths, betrayals, physical violence, huge confrontations,” says Phoenix Zhu, a freelance short drama screenwriter based in Suzhou. “It’s common to sacrifice narrative logic for shock value, because otherwise people are more likely to scroll away.”

Those simple tropes have made the format particularly compatible with AI-generated production. Earlier this year, FlexTV halted all traditionally shot productions and shifted entirely to AI-generated dramas. Kunlun Tech, the parent company of drama apps DramaWave and FreeReels, began producing AI-generated short dramas in 2025 and now offers more than 1,000 AI titles on its platforms. StoReels, another popular short drama company targeting a global audience, has said it aims to produce 100 AI-generated dramas per month.

“People’s attention spans are getting shorter, and serialized drama naturally has to get shorter,” says Han “Daniel” Fang, the CEO of Kunlun Tech. Fang told MIT Technology Review that the company is not going to stop investing in traditionally shot short dramas with real actors. But the company is expanding AI-generated productions and gradually increasing their share on its platforms as a low-cost way to experiment with new genres, themes, and ideas. “We want to bring the amount of AI work to 20% of the platform,” Fang says.

The format is also rapidly growing overseas. Research firm Omdia estimates that the global microdrama market reached $11 billion in 2025 and will grow to $14 billion by the end of 2026. The United States is expected to generate $1.5 billion in revenue in that market this year.

“No one comes to short dramas expecting high art,” says investor Shangguan Hong, former partner of Legend Capital. “The short-drama industry already stands out from traditional TV and filmmaking by being real-time and data-driven. AI only furthers that logic. In a sense, short drama is perfectly compatible with AI.”

Inside the content machine

The industry’s AI revolution is already changing the type of roles required to make short dramas. 

Phoenix Zhu graduated from college in 2024 with a degree in philosophy. After months of rejections from traditional media and film studios, she eventually found work writing scripts for short dramas. “It was a very difficult job market for young people,” Zhu says. “I couldn’t afford to be picky about what I wrote.”

To support herself, Zhu worked a string of part-time jobs, including as a barista, a flower seller, and an event coordinator, while taking freelance writing gigs online for advertising and education companies. In April 2025, she sold her first short-drama script for around 20,000 yuan (approximately $2,945). More commissions followed, and she thought her career was finally beginning to pick up.

Then AI arrived. Two projects already in the contract stage were abruptly canceled, Zhu says. Rates across the industry began falling. The raises she expected as she gained more experience never materialized. 

Still, writers like Zhu have been among the less disrupted workers in the industry. Many production roles on traditional filming sets have disappeared almost entirely from AI-generated productions.

“We could shrink the production team down to around 10 people,” says Tang, vice president at FlexTV. Like many companies in the industry, FlexTV relies primarily on Chinese writers and production teams, even for shows featuring non-Chinese characters and targeting overseas audiences. The reason is not just lower costs, Tang says, but also that Chinese writers better understand the pacing and narrative rhythm of short dramas.

Instead of camera crews, lighting technicians, makeup artists, and visual effects teams, AI productions now rely on smaller groups consisting largely of producers, writers, AI directors, and “AI asset curators.”

An AI asset curator translates scripts into prompts and generates reference images of characters, costumes, and scenes for AI video models to follow. MIT Technology Review found hundreds of job listings for the role on Chinese job sites, many requiring little prior industry experience beyond familiarity with AI tools.

“The technology has improved enormously just in the past few months,” says Hanzhong Bai, an AI short-drama producer based in Beijing. Bai says it is common for AI asset curators to use prompts like “combine the faces of these celebrities I like” when generating characters. Studios typically use a mix of tools, including Google’s image-generation model Nano Banana, ByteDance’s Seedance, and Kuaishou’s Kling.

For producers like Bai, AI also makes it economically viable to produce genres that were previously too expensive for short dramas, especially fantasy series requiring elaborate visual effects, costumes, or makeup. “We’ll see many more dragon and mermaid shows for exactly this reason,” Bai says.

The compressed production cycle has also changed the writing process itself. Writers once had two to three months to finish a script. Now, Zhu says, platforms often expect delivery within a month. Scripts can also be rougher and more flexible, since scenes, visuals, and even plot details can be changed later through prompts.

As a result, writers increasingly have to write for AI models as much as for human audiences. Zhu says she now has to describe scenes with far greater visual specificity, effectively taking on responsibilities once handled by cinematographers or visual effects teams.

“Before AI, writing ‘He gave her a cold stare’ might have been enough,” Zhu says. “Now I might need to write, ‘Cold beams of light shot out from his eyes.’”

Fang of Kunlun Tech believes the future quality of AI-generated short dramas is ultimately a numbers game. “Good ideas and good writing still stand out,” Fang says. “The quality [of AI short drama] will improve simply because more people with strong ideas will be able to make their shows.”

The Download: China’s AI drama factory and the WHO’s missing health targets

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

How Chinese short dramas became AI content machines

China’s short drama industry is fueled by bite-sized, melodramatic, and smutty shows built for smartphone scrolling. Now, many are being made entirely with AI: no actors, camera operators, cinematographers, or CGI specialists required.

An average of 470 AI-generated short dramas were released every day in January. Production timelines have shrunk from months to weeks, while costs have dropped by up to 90%. Storytelling is also increasingly driven by performance data.

The format is rapidly expanding overseas while reshaping the work of writers and production crews. Read the full story on AI’s dramatic impact on China’s short drama industry.

—Caiwei Chen

The world is on track to miss its health targets

The World Health Organization’s latest global statistics report reads less like a progress update than a warning sign. Progress on some of the world’s biggest health threats is stalling, and in some cases reversing altogether.

There were 1.3 million new HIV cases in 2024, malaria is resurging, vaccination rates are slipping in the Americas, and 42.8 million children are suffering from severe malnutrition. The world is now far off track from meeting many of the UN’s major health goals by 2030.

Here’s what the numbers reveal about the state of global health.

—Jessica Hamzelou

This story is from The Checkup, our weekly newsletter giving you the inside track on all things biotech. Sign up to receive it in your inbox every Thursday.

The must-reads

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

1 As their trial goes to the jury, Musk and Altman face lying accusations
Lawyers hammered the rivals’ credibility in their closing arguments. (WSJ $) 
+ Musk was accused of “selective amnesia.” (Reuters $) 
+ The pair are in court over OpenAI’s future. (MIT Technology Review)
+ And their trial has made everyone look bad. (Wired $) 
 
2 AI data centers are straining America’s power grid
Nevada is redirecting electricity from Lake Tahoe to AI. (Ars Technica)
+ Utah is getting a giant data center despite water shortage fears. (Guardian)
+ No one wants a data center in their backyard. (MIT Technology Review)
 
3 OpenAI is mulling legal action against Apple over its ChatGPT integration
It hasn’t got the expected benefits from its deal with Apple. (Bloomberg $)
+ OpenAI is frustrated by the promotion of the ChatGPT integration. (NYT $)

4 Anthropic has agreed terms for a $30 billion funding deal
At a $900 billion valuation, which leapfrogs OpenAI’s. (The Information $)
+ Dragoneer, Greenoaks, Sequoia, and Altimeter are leading the round. (FT $)

6 Washington and Beijing will hold formal talks on AI safety
They’ll discuss guardrails on AI. (CNBC)
+ And a protocol to stop nonstate actors getting powerful models. (NYT $)

5 Alphabet and Amazon are using “unprecedented” borrowing to fund AI
They’re tapping the foreign debt market at new levels. (FT $)
+ People can’t agree on what the AI bubble is. (MIT Technology Review)
 
7 Big Tech has turned to Sesame Street to deflect scrutiny of screen use
Sparking accusations of encouraging children’s tech dependence. (Reuters $)
 
8 Anthropic’s feud with the White House threatens other businesses
Figma and Tenable say it will harm their ability to sell software. (Bloomberg $)
 
9 Autonomous agents staged a digital crime spree during a safety test
The “AI Bonnie and Clyde” then deleted themselves. (Guardian)

10 A poop app analysis app offered to sell photos of users’ stools
The images were used for AI training. (404 Media)

Quote of the day

“It’s like we don’t exist.” 

—Danielle Hughes, North Lake Tahoe resident and CEO of Tahoe Spark, tells Fortune that residents are being sidelined as their energy supplier prioritizes data centers.

One More Thing

LIZ ISLES/ALL TECH IS HUMAN


The rise of the tech ethics congregation

Just before Christmas, a pastor preached a gospel of morals over money to several hundred members of his flock. But the preacher wasn’t religious, and his congregation wasn’t a church. It was All Tech Is Human, a nonprofit devoted to ethics and responsibility in tech.

Founded in 2018, the organization has built a fast-expanding community for people who believe technology should focus less on profits and more on the public interest. It’s also drawing people searching for meaning and connection in a digital world.

Find out why thousands of people are turning to tech ethics communities for guidance and connection.

—Greg M. Epstein

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.)

+ Go behind the scenes of the new Lucas Museum of Narrative Art.
+ Marvel at this robot folding and launching paper planes as quickly as possible.
+ Watch the moving moments rescued animals reunite with the humans who saved them.
+ Peer into the heart of a barred spiral galaxy in this stunning new capture from the James Webb Space Telescope.

Musk v. Altman week 3: Musk and Altman traded blows over each other’s credibility. Now the jury will pick a side.

In the final week of the Musk v. Altman trial, lawyers traded blows over Elon Musk’s and OpenAI CEO Sam Altman’s credibility. Altman was grilled on his alleged history of lying and self-dealing involving companies that do business with OpenAI. But he fired back, painting Musk as a power-seeker who wanted to control the development of artificial general intelligence (AGI)—powerful AI that can compete with humans on most cognitive tasks. 

As evidence of their commitment to AI safety, OpenAI brought out a golden trophy of a donkey’s ass that was gifted to an employee after he was called a “jackass” for standing up to Musk’s plans to race toward AGI. 

Lawyers for both sides also presented their closing arguments, floating unflattering mugshot-style photos of Musk and Altman next to each other on a giant screen. Musk’s lawyer Steven Molo argued that Altman and OpenAI president Greg Brockman broke their promise to use money Musk donated to maintain OpenAI as a nonprofit that develops AI for the benefit of humanity. Instead, they created a for-profit subsidiary that made them extraordinarily wealthy.

OpenAI’s lawyer Sarah Eddy argued that Altman and Brockman never promised to keep OpenAI a nonprofit. She added that even though it’s been restructured, OpenAI remains a nonprofit dedicated to developing AI safely.

She claimed that Musk sued too late—and that his real motive is to sabotage a competitor to his own AI company, xAI, which he launched in 2023. 

Musk is asking the court to unwind the 2025 restructuring that converted OpenAI’s for-profit subsidiary into a public benefit corporation and to remove Altman and Brockman from their roles. He is also seeking as much as $134 billion in damages from OpenAI and Microsoft, to be awarded to OpenAI’s nonprofit. 

The jury will begin deliberating on Monday and deliver an advisory verdict as soon as next week. The jury verdict is not binding on the judge, who will decide the case.

If the judge rules in Musk’s favor, it could upend OpenAI’s race toward an IPO at a valuation approaching $1 trillion. Meanwhile, xAI is expected to go public as a part of Musk’s rocket company SpaceX as early as June, at a target valuation of $1.75 trillion.

Musk the power-seeker, Altman the liar.

In the first week of the trial, Musk said he was suing to save OpenAI’s mission to build AI safely for the benefit of humanity. This week, Altman denied Musk was a paladin of AI safety and painted him as a power-seeker who wanted to control OpenAI. 

Altman told the jury that in 2017, when Musk and other cofounders were discussing creating a for-profit arm, they asked Musk what would happen to his control over such an entity if he died. “Maybe the control of OpenAI should pass to my children,” Musk said, according to Altman.

Musk’s lawyer shot back, grilling Altman on his alleged history of lying. He pointed out that OpenAI’s former executives Ilya Sutskever and Mira Murati, and former board members Helen Toner and Tasha McCauley, all testified that Altman had lied to them. In 2023, Altman was briefly fired as CEO over the alleged behavior.

Molo also pressed Altman about his personal investments in startups that do business with OpenAI. Altman testified that he tried to steer OpenAI to buying power from the nuclear energy company Helion Energy, a third of which he owns.

(Last Friday, the US House oversight committee launched an investigation into Altman’s potential conflicts of interest. Attorneys general from more than a half-dozen states called for the Securities and Exchange Commission to review them.)

During his closing statement, Molo put Altman’s credibility on the stand again. “Imagine that you’re on a hike, and you come upon one of those wooden bridges that you see on a trail, and it’s over a gorge,” he said. “A woman standing by the entry to the bridge says, ‘Don’t worry—the bridge is built on Sam Altman’s version of the truth.’ Would you walk across that bridge?”

Altman, who sat behind his lawyers, looked up uneasily every time his name was mentioned. 

During her closing argument, Eddy fired back. Musk “never cared about the nonprofit structure,” she said. “What he cared about was winning.” 

Musk, though, was absent. Despite the judge’s order that he remain available, he flew to China with President Trump.

Did Altman promise to keep OpenAI a nonprofit?

During her closing argument, Eddy argued that no testimony or evidence showed any conditions on Musk’s donations, or any promises made by Altman and Brockman to keep the company a nonprofit. “No commitments or promises were made. No restrictions were placed on Mr. Musk’s donations,” she said.

Eddy added that it was evident Musk wasn’t truly committed to keeping OpenAI a nonprofit. She noted that in 2017, he tried to create a for-profit subsidiary and fought a bitter battle with Altman and Brockman to have control over it.

“I was not opposed to there being a small for-profit that provides funding to the nonprofit,” Musk told the jury earlier in the trial, “as long as the tail didn’t wag the dog.” 

Eddy then argued that Musk sued too late, filing in 2024 after the statutes of limitations on his claims ran out. In 2019, OpenAI created a for-profit subsidiary, under which employees and investors received a capped return on their investment. 

But Musk testified that he discovered OpenAI had abandoned its nonprofit mission only in 2022, when Microsoft was preparing to invest $10 billion in OpenAI—a deal that closed in 2023. “I was disturbed to see OpenAI with a $20B valuation,” he texted Altman after reading the news. “This is a bait and switch.”

Musk told the jury that the $20 billion valuation made him realize “the for-profit is the tail wagging the dog.” 

“The 2023 deal was different,” Molo hammered home during his closing argument.

Is OpenAI still a nonprofit committed to its mission?

A central question raised in the last week of trial was whether OpenAI remains a nonprofit committed to developing AGI safely for the benefit of humanity. Eddy, the OpenAI lawyer, argued that the nonprofit still controls the for-profit and seeks to “help AGI turn out well for humanity.” “The OpenAI nonprofit is the best-resourced nonprofit in the world,” thanks to the for-profit, she added.

Molo countered that while the OpenAI’s nonprofit nominally controls the company, it does not do so in practice. OpenAI’s nonprofit and for-profit are controlled by the same people—seven of the nonprofit’s eight board members are on the for-profit’s board. The nonprofit hired employees only a month before the trial started and does work only in grant-making rather than AI research. 

Molo played a video interview of Altman saying that the nonprofit board’s failure to fire him in 2023 was “its own kind of governance failure.”

“We’re left with this nonprofit that doesn’t have any voice,” Jill Horwitz, a law professor at Northwestern University who studies nonprofits, told MIT Technology Review. “It doesn’t have much money, and OpenAI doesn’t think it has any obligation to fund it. It barely has a staff,” she says. “It’s unclear how on earth the nonprofit is supposed to exercise its duties and control the entire company.” 

Civil society groups and policymakers have spoken out against OpenAI’s restructuring over the years. So has Musk, although his own stake in the AI race makes him a dubious champion for the public interest. 

“The public interest in the nonprofit loses, no matter who wins or loses this trial,” says Horwitz.

Jackass for AI safety

Despite US District Judge Yvonne Gonzalez Rogers’s warning during the first week that this trial was not about AI safety, the issue stole the show again. Throughout the trial, the lawyers from both sides traded barbs over the safety track records of ChatGPT (which has allegedly caused teen suicides) and Grok (which has flooded X with porn).  

On the last day of testimony, OpenAI’s lawyer Bradley Wilson handed the judge a small golden trophy of a donkey’s ass, inscribed: “Never stop being a jackass for safety.” 

The trophy belonged to Joshua Achiam, OpenAI’s chief futurist. He testified that he’d warned, when Musk announced in 2018 that he was leaving OpenAI to race toward building AGI, that speed could compromise safety. Musk snapped and called him a “jackass,” said Achiam. His colleagues, including Dario Amodei, now CEO of Anthropic, gave him the trophy to enshrine the diss.

“I don’t want it,” said the judge.
The shenanigans spilled out into the street too. In front of the Oakland courthouse, a protester paraded around wearing a costume of Musk holding a bag of ketamine and driving a Cybertruck. Another held a photo of Sam Altman and a poster reading, “Stop AGI or we’re all gonna die.”

Chord Commerce CEO on Pivoting to Data

When he and I last spoke, in 2021, Bryan Mahoney had co-founded Chord, an ecommerce platform that separated the public frontend from the nuts-and-bolts backend. It was a “headless” structure that enabled merchants to connect with their preferred external providers rather than a one-size-fits-all solution.

The problem, Bryan says in retrospect, was a complicated setup that required replatforming. What merchants wanted instead was the platform’s component that consolidated data from the external providers, providing a holistic view across channels, customers, and more.

So he pivoted. In 2023 Chord became an ecommerce-focused data management company, no longer requiring customers to use its frontend layer.

In our recent conversation, Bryan shared the transition details and the importance of data for today’s brands. Our full audio is embedded below. The transcript is edited for clarity and length.

Eric Bandholz: Tell our listeners what you do.

Bryan Mahoney: I’m the co-founder and CEO of Chord Commerce. We help brands leverage their first-party data and digital channels to grow their businesses.

I co-launched the company in 2021. Before that I was chief operating officer at Glossier, the cosmetics brand.

We launched Chord as a “headless” ecommerce platform, one that separated the frontend presentation layer from the backend management tools. The problem was that it required merchants to maintain a lot of infrastructure. It proved to be too complicated. So we pivoted the company to what it is now, more of a data platform.

Bandholz: Why pivot the company rather than close it?

Mahoney: I’m addicted to commerce. I’m addicted to brand. The saving grace for Chord was that we were never only a headless commerce company. We knew early on that a headless platform would create a mountain of data issues from merchants connecting to multiple external solutions. Data would live on each of those platforms.

So we were a headless commerce and a data platform at the same time. But the companies that we met with kept pointing to the data component and asked if they could use it. We initially said they had to fully replatform, which was crazy.

I got tired of hearing no. And so, around 2023, we shifted from being dogmatic about headless to being platform-agnostic. Our customers no longer had to use our frontend. Shopify, Magento, whatever your public-facing provider, we can help solve the data problems.

And that really opened up the opportunity for us to work with more brands. Their data lived in different places. We got lucky because that’s when genAI systems burst onto the scene. It’s more important than ever to have your data in one place and accessible. Our market penetration has accelerated.

Bandholz: How does consolidated data benefit ecommerce companies?

Mahoney: You need a single source of truth to achieve your goals, such as a successful customer-acquisition campaign or a product launch.

You might use Google Analytics, Shopify, and Klaviyo. Perhaps you use Recharge for your subscriptions. Data on each of those providers could impact your acquisition messaging or your new product features. You need a holistic view of all of them to make informed decisions.

Bandholz: How do you standardize that data, once consolidated?

Mahoney: We’ve been working on it for years. Ours is purpose-built for commerce, an important distinction. There’s an awful lot of very good general-purpose data warehouses that are not opinionated about commerce. They don’t understand an ecommerce business the way that we do.

Bandholz: We’re launching a product at Beardbrand this month. We discussed in our team meeting this morning the best day to roll it out. Should we launch it after Memorial Day or before? A weekday or a weekend? Chord could have helped us, rather than relying on gut feel.

Mahoney: Yes, the key is to be data-informed. In your case, what happened in May in previous years? Is it a trend, or was there something else going on in the world at that time that may have affected the outcome? How have customer acquisition costs trended over the last month?

You can ask all these questions, but ultimately you’ve got to make the decision.

You can probe the data, which may be inconclusive, and decide to launch on Thursday. You then put your resources around that decision and remember the questions. A year from now or five months from now, when you launch another product, you can bring up that process.

I don’t ever want to propose a platform that removes the gut instinct of operators who know their brand, products, and customers better than we ever will.

But yes, we can provide the data to be better informed.

Bandholz: Are you pulling in data from similar companies to enable comparisons, such as repeat order rate versus an industry average?

Mahoney: Yes, we’re absolutely pulling in that information. We’ve been accumulating it for five years. Our customers can opt in to our own anonymized data co-op. We accumulate that data and provide benchmarks and industry standards.

But we never share personal information, and all of our tenants are completely isolated.

Bandholz: You don’t publish prices on your site, which tells me they may be higher than my company would pay. What sort of business will get the most from Chord?

Mahoney: We don’t have a pricing page up yet, though we do want our rates to be transparent. We charge a platform access fee to use our data connectors, the unification layer, and our core AI. It normally starts around $2,000 a month.

Our ideal customer is a company selling products online, D2C or retail, likely across multiple channels. The company wants its data in one place and to harness it via AI to drive growth. That can range from businesses with $10 million in annual GMV to those with $1 billion or more.

 Our customers get an awful lot of infrastructure. It’s a great value for the money.

Bandholz: Where can people check out Chord and reach out?

Mahoney: Chord.co. I am on LinkedIn, and I host a podcast called “Brilliant Commerce.”

Google’s New AI Search Guide Calls AEO And GEO ‘Still SEO’ via @sejournal, @MattGSouthern

Google published a new documentation page to help websites optimize for generative AI features in Search, including AI Overviews and AI Mode.

The page, “Optimizing your website for generative AI features on Google Search,” expands Google’s prior AI features documentation published in 2025. The earlier page explains how AI features work, how inclusion is controlled, and how performance is reported. The new guide focuses more directly on optimization advice and tactics Google says site owners can ignore.

Two sections are specifically worth highlighting. Google directly names popular optimization tactics it says aren’t necessary, and it redefines the AEO/GEO conversation as part of standard SEO.

Google Says AEO And GEO Are ‘Still SEO’

Google opens by confirming that foundational SEO best practices remain relevant for generative AI search. Its AI features are “rooted in our core Search ranking and quality systems” and rely on retrieval-augmented generation (RAG) and query fan-out to surface content from the Search index.

On the terminology debate, Google is direct. It defines “AEO” as “answer engine optimization” and “GEO” as “generative engine optimization,” then states:

“From Google Search’s perspective, optimizing for generative AI search is optimizing for the search experience, and thus still SEO.”

This echoes positions Google employees have taken at conferences. Gary Illyes and Cherry Prommawin told Search Central Live attendees that GEO and AEO don’t require separate frameworks. The position now appears in Google’s published documentation, providing an official reference to cite.

What Google Says You Don’t Need To Do

The guide includes a “Mythbusting generative AI search” section listing tactics it calls unnecessary for Google Search. The guide is more explicit than Google’s prior AI features page, particularly in naming llms.txt, chunking, inauthentic mentions, and AEO/GEO directly.

The guide says site owners can ignore the following for Google Search.

On llms.txt files and other “special” markup, Google says you don’t need to create machine-readable files, AI text files, markup, or Markdown to appear in generative AI search. Google may discover and index many file types beyond HTML, but that doesn’t mean those files receive special treatment.

On “chunking” content, the guide says there’s no requirement to break content into small pieces for AI systems. Google’s systems “are able to understand the nuance of multiple topics on a page and show the relevant piece to users.” Danny Sullivan made similar comments in January 2026, saying he’d spoken with Google engineers who recommended against chunking.

On rewriting content for AI systems, Google says AI systems can understand synonyms and general meanings. Site owners don’t need to capture every long-tail keyword variation or write in a specific way for generative AI search.

On seeking inauthentic “mentions,” the guide acknowledges that AI features can surface what’s said about products and services across blogs, videos, and forums. But it says seeking inauthentic mentions “isn’t as helpful as it might seem” because core ranking systems focus on quality while other systems block spam.

On structured data, the guide says it isn’t required for generative AI search and there’s no special schema.org markup to add. It recommends continuing to use structured data as part of an overall SEO strategy for rich results eligibility.

Several recommendations run counter to advice that appears in some AI search optimization guides. Multiple GEO resources have promoted chunking and structured data as priorities for AI search visibility.

What Google Says To Focus On

The optimization advice follows familiar SEO territory, though Google contextualizes it for AI features.

Google puts particular emphasis on “non-commodity content.” It contrasts commodity content (“7 Tips for First-Time Homebuyers”) with a non-commodity alternative (“Why We Waived the Inspection & Saved Money: A Look Inside the Sewer Line”). The distinction is whether content provides unique insight beyond common knowledge.

On the technical side, pages must be indexed and eligible for snippets to appear in generative AI features. Google recommends following crawling best practices, using semantic HTML where possible, following JavaScript SEO best practices, providing good page experience, and reducing duplicate content.

Local and ecommerce optimization gets its own section. Google recommends Merchant Center feeds and Google Business Profiles for product and local business visibility in AI responses. It also mentions Business Agent, a conversational experience that lets customers chat with brands on Google Search.

Agentic Experiences Get Initial Guidance

A new section on agentic experiences describes AI agents as “autonomous systems that can perform tasks on behalf of people, such as booking a reservation or comparing product specifications.”

Google notes that browser agents may access websites by analyzing screenshots, inspecting the DOM, and interpreting the accessibility tree. The guide links to web.dev’s guide to agent-friendly website best practices and references the Universal Commerce Protocol (UCP) as an emerging protocol that “will allow Search agents to do more.”

Google announced UCP earlier this year, and Vidhya Srinivasan’s annual letter said it was co-developed with Shopify with more than 20 companies endorsing it.

Why This Matters

This guide gives Google’s most explicit guidance yet on what you should and shouldn’t do for generative AI features in Search. It consolidates positions that were previously scattered across conference talks, podcast appearances, and blog posts into a single reference.

The mythbusting section carries the most weight. Google is now telling you in its own documentation to skip tactics that a growing industry of AEO/GEO services has been promoting. That doesn’t settle the debate for non-Google AI platforms like ChatGPT or Perplexity, which may weight signals differently. But for Google’s own AI features, the guidance is now on record.

The agentic experiences section puts browser agents and UCP into Google’s official documentation for site owners. The guidance is early, and Google frames it as optional for businesses where agent access is relevant.

Looking Ahead

Google’s closing section says you don’t need to accomplish everything in the document to succeed. It notes that “plenty of content thrives in Google Search (including generative AI experiences) without any overt SEO at all.”

The agentic experiences guidance is labeled as something to explore “if this is something that’s relevant to your business and you have extra time.” That suggests Google sees agent optimization as forward-looking rather than urgent.


Featured Image: Anatolir/Shutterstock

GA4 Tracks AI Assistant Traffic, FAQ Results Gone – SEO Pulse via @sejournal, @MattGSouthern

Welcome to this week’s Pulse. The updates affect how you measure AI assistant traffic, what structured data does for visibility, and how a major publisher is planning for life after search.

Here’s what matters for you and your work.

Google Analytics Adds Native AI Assistant Channel

Google Analytics now assigns traffic from recognized AI chatbots to a dedicated “AI Assistant” default channel group. Custom channel groups with regex patterns are no longer the only way to separate AI assistant visits from referrals.

Key Facts

Sessions from recognized AI assistants now receive “ai-assistant” as the medium, route to a new “AI Assistant” default channel, and get a reserved “(ai-assistant)” campaign label. Google named ChatGPT, Gemini, and Claude as examples, but hasn’t published the full list of recognized referrers. All three changes happen automatically.

Why This Matters

Anyone running a custom channel group to isolate AI chatbot traffic can now compare their setup against Google’s native version. The custom regex patterns Google recommended last August still cover platforms outside the recognized referrer list. Both can run side by side.

The bigger question is what you do with the data once it’s visible. AI assistant traffic is now a distinct line item in acquisition, user, and channel reports. That makes it easier to compare conversion behavior, session quality, and volume against organic search without filtering or manual workarounds.

Google hasn’t said how quickly the recognized referrer list will expand as new platforms launch. If you track AI assistants beyond the three named examples, keep your custom groups running.

What Industry Professionals Are Saying

Kevin Indig, Growth Advisor at Growth Memo, commented on LinkedIn:

“Was about time! Literally complained about this on stage yesterday”

Johan Strand, Senior Digital Analyst and Partner at Ctrl Digital, wrote on LinkedIn:

“If you already have a Custom Channel Group set up to check for AI traffic, it´s probably a good idea to adapt it now.”

Read our full coverage: Google Analytics Adds AI Assistant As Default Channel Group

Google Completes FAQ Rich Results Deprecation

Google deprecated FAQ rich results, completing a removal that started a few years ago. The company added a notice to its FAQ structured data documentation without a blog post or separate explanation.

Key Facts

FAQ rich results stopped appearing in search results. Google will remove the FAQ search appearance filter in Search Console, the rich result report, and support for Rich Results Test in June. API support ends in August.

Why This Matters

If your reporting pipelines pull FAQ-specific data from the API, those API calls need to be updated before the August cutoff.

Leaving the markup in place shouldn’t create problems, but it no longer produces that visible result. Whether FAQ schema aids AI search is a separate question, and the deprecation doesn’t answer it.

Read our full coverage: Google Drops FAQ Rich Results From Search

Ahrefs Report: Adding Schema Didn’t Increase AI Citations

An Ahrefs report tracked 1,885 pages that added JSON-LD schema and found no meaningful increase in AI citations across Google AI Overviews, AI Mode, or ChatGPT.

Key Facts

Ahrefs matched each treated page against controls that never added schema and measured changes over 30-day windows. AI Overviews showed a 4.6% decline relative to controls, while AI Mode (+2.4%) and ChatGPT (+2.2%) showed changes too small to distinguish from noise.

Why This Matters

The correlation between schema and AI citations has been widely cited as evidence that structured data improves AI visibility. Ahrefs tested whether the relationship appeared causal and found no evidence of a meaningful lift, at least for pages already being cited. Sites with schema tend to also invest in better content, stronger authority, and more links. Those factors may explain the correlation better than the markup itself.

The report can’t say whether schema helps pages that aren’t yet visible to AI systems. That’s a different population that needs its own test. For pages already earning citations, though, adding JSON-LD is unlikely to be the unlock.

What SEO Professionals Are Saying

Chris Long, Co-founder of Nectiv, wrote on LinkedIn:

“this data is changing my viewpoint a bit on how effective it is at actually influencing AI citations.”

Read our full coverage: Schema Markup Didn’t Move AI Citations In Ahrefs Test

Condé Nast CEO: Plan As If Search Traffic Will Be Zero

Condé Nast CEO Roger Lynch said he told company teams to plan their businesses as if search traffic were zero. Lynch made the comments in an interview on TBPN, a tech talk show OpenAI acquired in April.

Key Facts

Lynch described three consecutive years in which internal forecasts underestimated the actual declines in search traffic. He expects search to settle at a single-digit percentage of total traffic, not literally zero.

Lynch pointed to a “barbell effect” in which large, authoritative brands and small, niche publications are performing well, while brands in the middle are most exposed. Condé Nast’s digital subscriptions grew 29% in revenue last year.

Why This Matters

Lynch is describing what the third-party data has been showing for months. Chartbeat reported a 60% decline in search referrals for small publishers over two years. The Reuters Institute found that media leaders expect search traffic to fall by more than 40% over three years. The difference is that a CEO running Vogue, The New Yorker, and GQ is now building budgets around those numbers.

The barbell observation is worth testing against your own client portfolio or publishing operation. Lynch’s argument is that brands without deep category authority or a strong niche focus lack a clear path forward. AI Overviews, commerce links, and sponsored results fill the page before organic listings appear.

What SEO Professionals Are Saying

Kevin Indig, Growth Advisor at Growth Memo, commented on LinkedIn:

“Makes sense, no escape hatch for publishers in AEO.”

Read our full coverage: Condé Nast CEO: Plan As If Search Traffic Will Be Zero

Theme Of The Week: The Measurement Is Catching Up To The Problem

The tools and signals that defined search visibility for years are being deprecated, questioned, or abandoned by the publishers who depended on them.

FAQ rich results are gone. Schema’s role in AI citations is weaker than the correlation suggested. A major publisher is planning as if search traffic won’t recover. Each story involves an environment where the old measurement infrastructure no longer matches the landscape.

The GA4 update is the other side of that coin. Google is building native tracking for the traffic source that’s growing while the traditional one contracts.

AI assistant traffic is a fraction of what search delivers. But it’s now visible by default, in the same reports, next to the channels it’s measured against.

Top Stories Of The Week:

More Resources:


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

Why Your AI Ad Strategy Is Only As Good As Your Data via @sejournal, @gregjarboe

Stop trying to out-calculate the machine and start feeding the machine better signals was the theme from Ginny Marvin, Google’s Ads Product Liaison, during a recent episode of the Ads Decoded podcast she hosts. To many, it sounded like a victory lap for automation and seemed to set the industry on fire. To others, it felt like a final surrender of the steering wheel.

We are currently navigating a mass handover of campaign control to automated systems, and the speed of this transition is frequently outpacing our understanding of what we are surrendering. The numbers confirm that this isn’t just a trend; it is the new baseline for performance marketing. More than 1 million advertisers have now adopted Google’s Performance Max globally. On Meta, Advantage+ campaigns now account for 35% of all U.S. retail ad spend. Even TikTok has seen its Smart+ automated solutions jump from a mere 9% to 42% of performance campaigns in a single year.

The platform narrative is seductive. Google recently rolled out new steering and reporting updates for Performance Max, including audience exclusions and budget reporting, to address the long-standing “black box” criticism. According to Meta’s own engineering data, advertisers who adopted Advantage+ creative features saw an average 22% increase in return on ad spend, although results vary significantly based on first-party data quality and campaign maturity. But there is a dangerous gap between these platform claims and real-world performance that every SEO and paid media specialist needs to acknowledge.

A new report from Adtaxi hits the nail on the head: AI does not replace strategy; it magnifies it. If you provide the algorithm with strong data inputs and a clear definition of business value, then you get powerful outcomes. If you provide weak inputs, then you simply produce “accelerated inefficiency.” The machine will spend your budget with incredible speed, but it cannot navigate the strategic complexity that exists outside its training data.

In the era of GEO and entity-based search, the discipline required to feed ad platforms accurate, high-quality signals is the same discipline that builds brand authority in organic and AI-driven search results. When we talk about “the machine,” we are really talking about an interconnected ecosystem of data. If your ad campaigns are optimizing for surface-level metrics rather than true business outcomes, then you are essentially training the platforms to misunderstand your most valuable customers. If your SEO campaigns don’t include the prompt topics that your target audience is using, then read this.

For instance, Google’s latest April 2026 updates for Performance Max allow for first-party audience exclusions. This sounds like a technical setting, but it is actually a strategic pivot. It allows marketers to stop wasting acquisition budget on existing customers and focus on true growth. However, this exclusion is only as good as the CRM data behind it. If your first-party data is messy, your “automated” efficiency is an illusion.

We see this in the attribution gap on platforms like TikTok, where traditional last-click models fail to capture up to 79% of the conversions that automated systems are actually driving. Without a human expert to validate and measure these systems against real-world goals, we are just watching the algorithm spend money in a vacuum.

I contacted Jennifer Flanagan, vice president of Marketing at Adtaxi by email, and she countered that the lack of transparency in these systems creates a genuine risk where systems optimize for platform-defined metrics rather than business health. She correctly identified human experts as the “steadying hand” of strategy that machine learning cannot replicate.

The Lesson For 2026

It’s a clear lesson that you cannot “set and forget” your way to market leadership. The most successful marketers follow a strict rule of resource allocation: Invest the vast majority of your energy into human talent and strategy, and let the remaining fraction go toward the tools themselves. AI is running more of your advertising than you probably realize. The only question that matters now is whether you are running the AI, or if you are simply watching it spend your budget.

More Resources:


Featured Image: Master1305/Shutterstock