A plan to make drugs in orbit is going commercial

<div data-chronoton-summary="

  • A big deal: Varda Space Industries says it has signed a pharmaceutical company as a commercial customer, marking what could be a landmark moment for in-orbit manufacturing.
  • Space as a lab: The bet is that microgravity causes drug molecules to crystallize into atomic arrangements impossible on Earth, potentially unlocking new versions of existing medicines.
  • Economics favor drugs: At $7,000 per kilogram to reach orbit, space manufacturing is impractical for most industries — but blockbuster drugs can be worth over $100 million per kilogram, making them a rare exception to the brutal math of rocket launches.
  • Still more experiment than factory: Despite the excitement, no product has ever been manufactured in space, brought back, and sold on Earth.

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Varda Space Industries, a startup that’s been pitching its ability to perform drug experiments in space, says it has signed up the pharmaceutical company United Therapeutics in what may be remembered as a notable step toward in-orbit manufacturing.

The idea of building things in outer space for use on Earth has so far been explored mostly on board the International Space Station, and only in small-scale experiments backed by governments.

But Varda, based in El Segundo, California, is now telling drug companies it has a practical, and repeatable, way to produce novel molecules in microgravity. 

“This is the first commercial path to products made in space,” says Michael Reilly, Varda’s chief strategy officer.

The scientific idea is that chemical mixtures have different properties under weightless conditions. For instance, water will hang together in a wiggly sphere, since without gravity, surface tension is the strongest force present.

The plan is to launch versions of United Therapeutics’ drugs into orbit, where they can be allowed to form solid crystals. The hope is that in microgravity, they’ll take on atomic arrangements not seen on Earth, possibly leading to new versions with improved stability or other valuable properties.

United is led by CEO Martine Rothblatt, who worked on early telecommunications satellites. Since then, she’s built a multibillion-dollar health franchise with a succession of drugs to treat a lung disease called pulmonary arterial hypertension, which her daughter suffers from, and a subsidiary developing genetically modified pigs as a source of organs for transplantation.

Rothblatt says space could be the next step if orbital conditions permit United to identify “even more amazing” versions of its drugs.

Space to reformulate

Pharmaceutical companies often try to keep their blockbuster franchises alive by creating improved versions of drugs or reformulating them—for example, making the switch from a pill to an inhaled version, as United has done with some of its products. Doing so can keep imitators at bay and create extra decades of patent protection.

Assisting drugmakers are specialist companies, such as Halozyme and MannKind, that earn profits by helping to reformulate other companies’ drugs, often taking a royalty on future sales.

That’s the business Varda has been trying to break into—by using excursions into space instead of nebulizers, patches, or nanoparticles. The company was formed in 2021 by Delian Asparouhov, a partner at Peter Thiel’s Founders Fund, along with Will Bruey, a former avionics engineer with Elon Musk’s SpaceX who is now Varda’s CEO.

The pair’s bet is that space manufacturing will become viable once rocket launches become frequent enough—and cheap enough—to support a business model in which raw materials are sent into orbit, processed, and then returned to Earth in a new form.

And that’s starting to happen. To get into space, Varda has been purchasing rides from SpaceX—which now launches a rocket every two or three days, usually a reusable Falcon 9. 

Those rockets have a nose cone, or payload fairing, about the size of a moving truck that gets filled with satellites or instruments, which are then released into orbit.

Starting in 2023, Varda began sending up small satellites that have a boulder-size capsule attached. The capsule contains equipment to carry out experiments, and it can detach and fall back to Earth, entering the atmosphere at a speed of around Mach 25 before slowing via air resistance and eventually drifting to land with a parachute. (Varda lands its craft in the Australian outback.)

That speedy reentry has also drawn interest from the US military, including the Air Force, which has paid Varda to fly instruments and take measurements relevant to hypersonic missile technology. Of the six craft Varda has paid to put into orbit so far, half have been dedicated to military research and half carried drug-related demonstrations. 

At Varda, such “dual use” of technology is accepted as part of being in the space business, which remains reliant on government support. The company’s founders say Varda may be the only company that employs hypersonic engineers and pharmaceutical chemists under the same roof.

At Varda’s headquarters, drug samples are loaded into a spinning arm that creates extra-high g-forces. While that’s the opposite of microgravity, increased weight can provide clues into whether a drug will act differently under new conditions.
COURTESY VARDA

Launching industries

Actual space manufacturing still remains mostly an aspirational project. In 2021, Jeff Bezos, after his first trip aloft in a rocket, suggested that polluting industries should be moved beyond the atmosphere. “We need to take all heavy industry, all polluting industry, and move it into space. And keep Earth as this beautiful gem of a planet that it is,” he told MSNBC.

Weight is the big obstacle to such dreams. It still costs around $7,000 to launch a single kilogram of payload into orbit, which makes it impractical to, say, send cotton into space to be dyed there, or even to launch the acids and solvents needed to make a semiconductor chip.

But drugs may be among the few exceptions to this economic rule, since pound for pound, they can be as valuable as rare radioactive isotopes and fine-cut diamonds.

For instance, just one kilogram of the weight-loss drug Ozempic is worth more than $100 million at retail. (The reason your Ozempic bill is only $1,000 a month is that minute quantities of the active ingredient are present in the shots.)

That’s why Varda thinks it may eventually be able to manufacture drugs in orbit. However, its effort with United is more of a flying experiment to learn whether the company’s lung medicines will crystallize differently in microgravity.  

The terms of the deal between Varda and United aren’t public, and the companies haven’t said which specific drugs the collaboration will study. But Rothblatt did confirm that United is paying Varda to help it identify new crystal forms of its drugs (also called polymorphs), which it hopes could have improved properties.

“One has to do the experiment to find out if that is so. The first part of the experiment is to see what polymorphs of these molecules can be made without the influence of gravity,” she says. “Then, once we have those polymorphs, we will test them.” 

There is good evidence that crystals form differently in space. For instance, in 2017 the pharmaceutical giant Merck sent samples of its cancer immunotherapy drug Keytruda to the International Space Station, where it was found to form crystals of a single size. On Earth, the drug tended to form two different sizes at once.

That experiment offered clues for how to formulate the drug as a shot instead of administering it intravenously. Still, when Merck introduced a Keytruda injection last year, it ended up using a different approach. That means there’s still no straight-line connection between orbital discoveries and any drug here on Earth. Actual space factories are another step further from reality. 

“We’ve been learning from space for years, but I can’t name anything manufactured in space, brought down to Earth, and sold,” says Reilly. “So that is a first—or it will be a first.”

Reilly says that Varda anticipates launching United Therapeutics’ drugs into orbit sometime early next year. 

The Download: making drugs in orbit and NASA’s nuclear-powered spacecraft

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.

A plan to make drugs in orbit is going commercial

A startup called Varda Space Industries is betting that the future of pharmaceuticals lies in orbit. The company has signed a deal with United Therapeutics to test whether drugs crystallize differently in microgravity, potentially creating improved versions with new properties.

The idea sounds futuristic, but falling launch costs and reusable rockets are making space-based manufacturing seem increasingly plausible. Varda says the partnership could mark an important step toward building products in orbit for use back on Earth.

Discover how space could become the next frontier for drug development.

—Antonio Regalado

MIT Technology Review Narrated: NASA is building the first nuclear reactor-powered interplanetary spacecraft. How will it work?

Just before Artemis II began its historic slingshot around the moon, NASA revealed an even grander space travel plan. By the end of 2028, the agency aims to fly a nuclear reactor-powered interplanetary spacecraft to Mars.

A successful mission would herald a new era in spaceflight—and might just give the US the edge in the race against China. But the project remains shrouded in mystery.

MIT Technology Review picked the brains of nuclear power and propulsion experts to find out how the nuclear-powered spacecraft might work.

—Robin George Andrews

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

The must-reads

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

1 Sam Altman claims Elon Musk tried to seize control of OpenAI
Altman said Musk initially wanted 90% of the equity. (AFP)
+ And that control should go to his children when he dies. (BBC)
+ Altman also accused Musk of twice trying to end its non-profit status. (NPR)
+ Musk’s motivations for the suit are under scrutiny. (MIT Technology Review)

2 Google and SpaceX are in talks to launch data centers into orbit
SpaceX could join Suncatcher, Google’s orbital data center project. (WSJ $)
+ The project’s first launch is slated for early 2027. (Guardian)
+ Anthropic and SpaceX have also discussed orbital data centers. (Wired $)
+ But there are a few hurdles to overcome. (MIT Technology Review

3 Jensen Huang has joined Donald Trump’s high-stakes mission to China
Nvidia is lobbying to sell its AI chips in the country. (Bloomberg $)
+ Elon Musk and Tim Cook are also on the trip. (CNBC)
+ But a tech rivalry and distrust have sapped hopes for big deals. (Reuters $)

4 ICE agents have a list of 20 million people on their iPhones, thanks to Palantir
An ICE official said Palantir is speeding up raids and arrests. (404 Media)
+ ICE has also used facial recognition and Paragon spyware. (TechCrunch)

5 Defense tech firm Anduril just doubled its valuation to over $60 billion
In a $5 billion funding round led by Thrive Capital and a16z. (FT $)
Anduril, which makes AI-backed weapons, may go public next year. (NYT $)

6 Meta employees are protesting computer-tracking at work
Flyers posted at offices are urging staff to oppose the program. (Reuters $)
+ Meta plans to track workers’ clicks and keystrokes to train AI. (CNBC)

7 OpenAI is facing another wrongful death lawsuit over ChatGPT medical advice
The chatbot’s tips allegedly led to a teenager’s overdose. (Ars Technica)

8 The Canvas learning platform has paid hackers to delete stolen student data
It caved to ransomware demands after the biggest-ever edtech breach. (BBC)

9 Scientific researchers are thinking twice about using AI
Due to price hikes, usage limitations, and unreliable outputs. (Nature)

10 The latest AI compute solution? Putting data centers in your home
Hardware hosts get subsidized electricity and internet. (Ars Technica)

Quote of the day

“Mr Musk did try to kill it.”

—Sam Altman claims that Elon Musk tried to destroy rather than protect OpenAI’s non-profit operations, the Guardian reports.

One More Thing

YOSHI SODEOKA


Why does AI hallucinate?

Chatbot fails are now a familiar meme. Meta’s short-lived scientific chatbot generated wiki articles about the history of bears in space. Lawyers have submitted court documents filled with legal citations fabricated by ChatGPT. Air Canada was ordered to honor a refund policy invented by its customer service chatbot.

This tendency to make things up—known as hallucination—is one of the biggest obstacles holding chatbots back from more widespread adoption. Here’s why they do it—and why we still can’t fix it.

—Will Douglas Heaven

This story is part of MIT Technology Review Explains, our series untangling the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here

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.)

+ A historian has unearthed the etymology of every single dinosaur name.
+ Humus on the moon is getting closer to reality after scientists grew chickpeas in lunar soil.
+ Witness the patience of a master paper artist in this gallery of intricate, handmade sculptures.
+ Want to tell the time alphabetically? Me neither, but this cursed clock is an intriguing reason to try.

AI chatbots are giving out people’s real phone numbers

People report that their personal contact info was surfaced by Google AI—and there’s apparently no easy way to prevent it. 

A Redditor recently wrote that he was “desperate for help”: for about a month, he said, his phone had been inundated by calls from “strangers” who were “looking for a lawyer, a product designer, a locksmith.” Callers were apparently misdirected by Google’s generative AI. 

In March, a software developer in Israel was contacted on WhatsApp after Google’s chatbot Gemini provided incorrect customer service instructions that included his number. 

And in April, a PhD candidate at the University of Washington was messing around on Gemini and got it to cough up her colleague’s personal cell phone number. 

AI researchers and online privacy experts have long warned of the myriad dangers generative AI poses for personal privacy. These cases give us yet another scenario to worry about: generative AI exposing people’s real phone numbers. (The Redditor did not respond to multiple requests for comment and we could not independently verify his story.)

Experts say that these privacy lapses are most likely due to personally identifiable information (PII) being used in training data, though it’s hard to understand the exact mechanism causing real phone numbers to show up in the AI-generated responses. But no matter the reason, the result is not fun for people on the receiving end—and, even more worryingly, there appears to be little that anyone can do to stop it. 

A 400% increase in AI-related privacy requests

It’s impossible to know how often people’s phone numbers are exposed by AI chatbots, but experts say they believe that it is happening far more than is reported publicly. 

DeleteMe, a company that helps customers remove their personal information from the internet, says customer queries about generative AI have increased by 400%—up to a few thousand—in the last seven months. These queries “specifically reference ChatGPT, Claude, Gemini … or other generative AI tools,” says Rob Shavell, the company’s cofounder and CEO. Specifically, 55% of these concerns about generative AI reference ChatGPT, 20% reference Gemini, 15% Claude, and 10% other AI tools, Shavell says. (MIT Technology Review has a business subscription to DeleteMe.)

Shavell says customer complaints about personal information being surfaced by LLMs usually take two forms: Either “a customer asks a chatbot something innocuous about themselves and gets back accurate home addresses, phone numbers, family members’ names, or employer details.” Alternatively, a customer may be confronted with and report the exposure of someone else’s personal data, when “the chatbot generates plausible-but-wrong contact information.” 

This aligns with what happened to Daniel Abraham, a 28-year-old software engineer in Israel. In mid-March, he says, a stranger sent him a “weird WhatsApp message from an unknown number” asking for help with his account in PayBox, an Israeli payment app. 

“I thought it was a spam message,” he wrote to MIT Technology Review in an email—“someone who was trying to troll me.”

But when he asked the stranger how they had found his number, they sent him a screenshot of Gemini’s instructions to contact PayBox customer service via WhatsApp—giving his personal number. Abraham does not work for PayBox, and PayBox does not have a WhatsApp customer service number, Elad Gabay, a customer service representative for the company, confirmed.

Later, Abraham asked Gemini how to contact PayBox, and it generated another person’s WhatsApp number. When I recently asked, Gemini again responded with an Israeli phone number—it belonged not to PayBox, but to a separate credit card company that works with PayBox.

Screenshot of the second part of a Google Gemini conversation. Gemini provides an incorrect phone number for PayBox.
Screenshot: Google Gemini provides MIT Technology Review with the incorrect number for PayBox.

Abraham’s exchange with the stranger ended quickly, but he said he was concerned about how other potential exchanges could quickly turn sour, including “harassment or other bad interactions.” “What if I asked for money in order to ‘solve’ that [customer service] issue?” he said.

To try to figure out how this happened, Abraham ran a regular Google search on his phone number, and he found that it had been shared online once, back in 2015, on a local site similar to Quora. Though he’s not sure who posted it there, it may explain how it ended up being reproduced by Gemini over a decade later. 

Chatbots like Gemini, Open AI’s ChatGPT, and Anthropic’s Claude are built on LLMs that are trained on huge amounts of data scraped from across the web. This inevitably includes hundreds of millions of instances of PII. As we reported last summer, for example, the large popular open-source data set DataComp CommonPool, which has been used to train image-generation models, included copies of résumés, driver’s licenses, and credit cards. 

The likelihood of PII appearing in AI training data is only increasing as public data “runs out” and AI companies look for new sources of high-quality training data. This includes information from data brokers and people-search websites. According to the California data broker registry, for instance, 31 of 578 registered data brokers operating in the state self-reported that they had “shared or sold consumers’ data to a developer of a GenAI system or model in the past year.” 

Furthermore, models are known to memorize and reproduce data verbatim from training data sets—and recent research suggests that it is not just frequently appearing data that is most likely to be memorized.

Imperfect Measures

It’s standard practice now to build guardrails into an LLM’s design to constrain certain outputs, ranging from content filters meant to identify and prevent chatbots from releasing PII to Anthropic’s instructions to Claude to choose responses that contain “the least personal, private, or confidential information belonging to others.” 

But as a pair of University of Washington PhD students researching privacy and technology saw firsthand recently, these safeguards don’t always work.

“One day, I was just playing around on Gemini, and I searched for Yael Eiger, my friend and collaborator,” Meira Gilbert says. She typed in “Yael Eiger contact info,” and after Gemini provided an overview of Eiger’s research, which Gilbert had expected, Gemini also returned her friend’s personal phone number. “It was shocking,” Gilbert says.

When she saw the Gemini result, Eiger remembered that she had, in fact, shared her phone number online in the previous year, for a technology workshop. But she had not expected it to be so visible to everyone on the internet. 

Have you had your PII revealed by generative AI? Reach the reporter on Signal at eileenguo.15 or tips@technologyreview.com.

“Having your information be … accessible to one audience, and then Gemini making it accessible to anyone” feels completely different, Eiger says—especially when she found that the information was buried in a normal Google search.

“It was severely downgraded,” Gilbert confirms. “I never would have found it if I was just looking through Google results.” (I tried the same prompt in Gemini earlier this month, and after an initial denial, the tool also gave me Eiger’s number.)

After this experience, Eiger, Gilbert, and another UW PhD student, Anna-Maria Gueorguieva, decided to test ChatGPT to see what it would surface about a professor. 

At first, OpenAI’s guardrails kicked in, and ChatGPT responded that the information was unavailable. But in the same response, the chatbot suggested, “if you want to go deeper, I can still try a more ‘investigative-style’ approach.” Their inquiry just had to help “narrow things down,” ChatGPT said, by providing “a neighborhood guess” for where the professor might live, or “a possible co-owner name” for the professor’s home. ChatGPT continued: “That’s usually the only way to surface newer or intentionally less-visible property records.” 

The students provided this information, leading ChatGPT to produce the professor’s home address, home purchase price, and spouse’s name from city property records. 

(Taya Christianson, an OpenAI representative, said she was not able to comment on what happened in this case without seeing screenshots or knowing which model the students had tested, though we pointed out that many users may not know which model they were using in the ChatGPT interface. In response to questions about the exposure of PII, she sent links to documents describing how OpenAI handles privacy, including filtering out PII, and other tools.) 

This reveals one of the fundamental problems with chatbots, says DeleteMe’s Shavell. AI companies “can build in guardrails, but [their chatbots] are also designed to be effective and to answer customer questions.”

The exposure issue is not limited to Gemini or ChatGPT. Last year, Futurism found that if you prompted xAI’s chatbot Grok with “[name] address,” in almost all cases, it provided not only residential addresses but also often the person’s phone numbers, work addresses, and addresses for people with similar-sounding names. (xAI did not respond to a request for comment.) 

No clear answers

There aren’t straightforward solutions to this problem—there’s no easy way to either verify whether someone’s personal information is in a given model’s training set or to compel the models to remove PII. 

Ideally, individual consumers should be able to request that their PII be removed, says Jennifer King, the privacy and data fellow at Stanford University Institute for Human-Centered Artificial Intelligence. But this is typically interpreted to apply only to the data that people have directly given to companies—like when they interact with a chatbot, King explains.

“I don’t know if Google even has the infrastructure … to say to me, ‘Yes, we have your data in our training data, we can summarize what we know about you, and then we can delete or correct things that are wrong or things that you don’t want in there,’” she says. 

Existing privacy legislation, like the California Consumer Privacy Act or Europe’s GDPR, does not cover the “publicly available” information that has already been scraped and used to train LLMs, especially since much of this is anonymized (though multiple studies have also shown how easy it is to infer identities and PII from anonymized and pseudonymous data). 

As to “whether they [AI companies] have ever systematically tried to go back through data that had already been collected from the public internet and minimized that stuff?” King adds. “No idea.” 

The next best solution would be that the companies are “taking out everybody’s phone numbers or all data that resembles [phone numbers],” King says, but “nobody’s been willing to say” they’re doing that. 

Hugging Face, a platform that hosts open-source data sets and AI models, has a tool that allows people to search how often a piece of data—like their phone number—has appeared in open-source LLM training data sets, but this does not necessarily represent what has been used to train closed LLMs that power popular chatbots like Claude, ChatGPT, and Gemini. (Eiger’s number, for example, did not show up in Hugging Face’s tool.) 

Alex Joseph, the head of communications for Gemini apps and Google Labs, did not respond to specific questions, but he said that “the team” is “looking into” the particular cases flagged by MIT Technology Review. He also provided a link to a support document that describes how users can “object to the processing of your personal data” or “ask for inaccurate personal data in Gemini Apps’ responses to be corrected.” The page notes that the company’s response will depend on the privacy laws of your jurisdiction. 

OpenAI has a privacy portal that allows people to submit requests to remove their personal information from ChatGPT responses, but notes that it balances privacy requests with the public interest and “may decline a request if we have a lawful reason for doing so.” 

Anthropic describes how it uses personal data in model training, but it does not have a clear way for people to request its removal. The company did not respond to a request for comment.

The best option for anyone who wants to protect their private data right now is to “start upstream: get personal data off the public web before it ends up in the next scrape,” says Shavell. Since the start of the year, for instance, California has offered its residents a web portal to request that data brokers delete their information. Still, this doesn’t guarantee that your data hasn’t already been used for training—and will therefore not appear in a chatbot’s response. 

The Redditor who received incessant calls posted that he had “submitted an official Legal Removal/Privacy Request to Google, asking them to urgently blacklist my number from their LLM outputs,” but had not yet received a response. He also wrote last month that “the harassment continues daily.” 

Abraham, the Israeli software developer, says he contacted Google’s customer service on March 17, the day after his phone number was exposed. He says he did not receive a response until May 4, and it simply asked for documentation that he had already provided. 

Meanwhile, inspired by her own exposure on Gemini, Eiger, along with Gilbert and Gueorguieva, is designing a research project to further study what personal information is being surfaced by various AI chatbots—and what they may know, even if they’re not telling us. 

Some of that information may “technically be public,” says Gilbert, but chatbots may be altering “the amount of effort you would put into finding” it. Now instead of searching through 10 pages of Google search results, or paying for the information from a data broker site, “does generative AI just lower the barrier to entry to target people?” 

This piece has been updated to clarify OpenAI’s response.

Liquid Web WordPress Plugin Rebrand Triggers Backlash via @sejournal, @martinibuster

Liquid Web inadvertently started a cascading series of controversies after it folded a group of well-known WordPress plugin brands into a new software lineup. The reconfiguration and rebranding caught users by surprise, leading to confusion and significant online backlash against Liquid Web across social media.

A Dynamic WordPress Facebook group admin started a discussion about the Liquid Web plugin and branding controversy that reflected the confusion at the time, writing:

“It looks like a bit of chaos in the Kadence FB group as LiquidWeb moves to integrate their tools under a single umbrella. What’s interesting is that they’ve dropped Lifetime Bundle (LTD) and now have 3 packages:

  • $99 Essentials (theme and blocks),
  • $219 Pro (which includes ShopKit)
  • and $399 Elite.

Ultimately, people aren’t happy. It appears that their licenses aren’t working. That’s something they should be able to fix. However, it’ll be interesting to see what level of access people get. Will LTD owners still retain access to addons like ShopKit and Kadence Conversions?”

One person in that Dynamic WordPress Facebook group discussion blamed private equity investments in web hosting for the issue, a sentiment that was echoed on X, where @jeffr0 suggested that maybe Matt Mullenweg had a point about private equity firms and WordPress hosting investments.

@jeffr0 tweeted:

“So I guess @photomatt had a point. Private Equity in the WordPress ecosystem blows.”

Someone else disagreed with blaming private equity investors, responding:

“I’m not sure I agree. First, WPE wasn’t doing anything wrong. …I’m also not sure what’s happening to these plugins is a result of LW being owned by PE.”

Reflecting the confusion in the moment, @srikat tweeted:

“I can’t find the downloads for my lifetime Kadence purchase. Just sent them a support email ticket..”

Nexcess/Liquid Web Branding and Rebranding

Part of the confusion stems from a yearslong series of Liquid Web and Nexcess branding flip-flops.

  • Liquid Web acquired Nexcess in 2019.
  • The two brands later moved toward a unified Liquid Web identity.
  • By late 2025, users who typed in nexcess.net were often redirected to liquidweb.com.
  • In April 2026, Nexcess relaunched as a “Specialty Cloud” brand combining Liquid Web’s managed hosting expertise with Servers.com’s bare-metal infrastructure.

Meanwhile, Liquid Web is now the managed hosting brand within the Nexcess ecosystem.
StellarWP Disappears: Plugins Emerge In Nexcess And Liquid Web
Previously, the plugins lived under the StellarWP brand, with many maintaining their own standalone websites. The new branding is confusing for some users because both Nexcess and Liquid Web describe the same WordPress products as part of their own ecosystem.

The Nexcess relaunch announcement from April 8, 2026 says:

“We’re expanding your toolkit by bringing leading software solutions, like Kadence, GiveWP, The Events Calendar, and LearnDash, directly into the Nexcess ecosystem.”

Liquid Web’s May 12, 2026 web page describes the same products as part of the Liquid Web by Nexcess software portfolio:

“Liquid Web by Nexcess is concentrating its diverse WordPress software portfolio into four core products…”

The overlapping language between both brands helps explain why the rollout appeared confusing from the outside. The products were described as moving into both Nexcess and “Liquid Web by Nexcess,” and StellarWP seemingly disappeared without notice.

Liquid Web’s software announcement says its WordPress software portfolio is now concentrated into four core products: Kadence, LearnDash, The Events Calendar, and Give. The company says SolidWP, Iconic, Restrict Content Pro, and MemberDash are no longer sold as standalone products, with their features folded into Kadence or LearnDash.

What It Means For Plugin Subscribers

For existing customers, Liquid Web says the change is optional. The company says customers can keep their current features, plans, pricing, tools, and license keys unless they choose to upgrade to one of the new software plans.

But the public rollout appears to have created confusion among plugin users, including lifetime deal customers who were unsure what happened to the products they had purchased. Social media posts described product pages disappearing, redirects not working as expected, and users trying to determine whether their plugins had been discontinued, renamed, or moved.

In a post to a discussion in the Dynamic WordPress Facebook group, Jack Kitterhing, Strategic Product Leader at Nexcess, confirmed that lifetime subscription plugin customers would retain what they already had and that every customer was being grandfathered in. He also acknowledged login issues and missing invoices, describing the move as a “massive migration and change of systems” that came with challenges.

Kitterhing posted an explanation of what’s going on:

“Just to confirm Lifetime customers retain everything they already had. We aren’t removing anything or watering it down. If you owned it you still own it today. Every single customer is being grandfathered in.

And we re-positioned Kadence Essentials so for those of you who just want the theme and blocks it’s now cheaper than it used to be ($99 vs $129) to get the core components of Kadence.

There are currently issues with logging in for some customers and missing invoices which the team is fixing as I type and we expect to be fully fixed in a few hours.
This was a massive migration and change of systems and like anything of such magnitude it comes with challenges. Thanks for bearing with us as we get this all up and running today.”

Takeaways

  • Liquid Web says existing customers keep their current features, pricing, plans, tools, and license keys.
  • Lifetime customers were told they retain what they already had.
  • The backlash appears to have been driven by confusion during the rollout, not only by the product consolidation itself.
  • Years of Liquid Web and Nexcess branding changes made the plugin migration harder to understand.
  • Clearer advance communication may have reduced the confusion around product pages, redirects, licenses, and lifetime deal access.

There appears to have been insufficient communication from Liquid Web and Nexcess, compounded by the two companies’ branding flip-flops. The situation appears to be on the way to being resolved.

Featured Image by Shutterstock/hoangpts

Google Quietly Changed How Search Terms Are Reported For Some AI Queries via @sejournal, @brookeosmundson

Google quietly updated one of its Google Ads help pages with a clarification that could raise concerns for some advertisers.

The updated documentation suggests that search terms shown in reporting for AI-powered Search experiences may not always reflect a user’s exact query. Instead, some reported search terms may represent Google’s interpretation of user intent.

The change applies to experiences tied to AI Mode, AI Overviews, Google Lens, and autocomplete.

Search Terms Reports have long been used to understand query intent, identify negative keywords, review compliance concerns, and spot optimization opportunities. While the report has never provided full visibility, advertisers generally assumed that when a search term appeared in reporting, it reflected the actual query entered by the user.

For some newer AI-powered Search experiences, that may no longer be the case.

What Google Changed

The updated language appears within Google’s help documentation around ad group prioritization. The page explains how Google determines which ad group enters an auction when multiple keywords or targeting methods are eligible to match the same search.

It was first discovered by Anthony Higman who posted about his findings on LinkedIn.

Within that documentation, Google now explains that search terms associated with AI-powered experiences may reflect the inferred meaning or intent behind a search instead of the literal query itself. The clarification specifically references AI Mode, AI Overviews, Lens, and autocomplete.

In practice, that means advertisers could see search terms in reporting that were never directly typed by the user. Instead, Google may surface a normalized or interpreted version of the interaction.

Historically, many advertisers viewed the Search Terms Report as a fairly direct reflection of user behavior. A user searched for something, a keyword matched, and the advertiser could review that query inside reporting.

For some AI-powered Search experiences, Google is now signaling that the reporting process may involve more interpretation before those search terms appear in the interface.

Why Google Likely Made This Change

This update likely reflects the practical challenges of reporting on newer AI-powered Search experiences, especially with the recent announcements of more ads coming to AI experiences.

Traditional Search reporting was built around direct keyword queries. AI-powered experiences like AI Mode, AI Overviews, Lens, and autocomplete do not always work that way.

Users may refine searches across multiple prompts, search visually instead of typing, or rely on autocomplete suggestions before finishing a query. In some cases, there may not be a single clean keyword query for Google to surface inside a traditional Search Terms Report.

From Google’s perspective, intent approximations may help standardize reporting across those interactions. A conversational AI search, a Lens query, and an autocomplete-assisted search may all require some level of interpretation before they can appear in reporting.

There’s probably also a privacy component to this.

As Search becomes more conversational, users naturally provide more context in their interactions. Google may not want to expose every raw AI prompt, image-based search, or conversational refinement directly inside advertiser reports.

Many advertisers will likely understand that reasoning. The problem is that some may also see this as another reduction in transparency at a time when Google Ads already relies heavily on automation, modeling, and inferred signals.

Should Advertisers Be Concerned About This Change?

Many advertisers will likely view this as part of a broader trend inside Google Ads.

Over the past several years, advertisers have already adjusted to reduced search term visibility, heavier automation, broader matching behavior, and more modeled reporting. This update adds another layer to that shift by signaling that some visible search terms may not represent the exact user query.

For advertisers who rely heavily on search term analysis, that creates obvious concerns.

Highly regulated industries often review search terms closely for compliance and brand safety. B2B advertisers use query reports to identify customer pain points and emerging use cases. Ecommerce advertisers use Search Terms Reports to build negative keyword lists, refine product segmentation, and better understand shopping behavior.

If reported terms become interpreted summaries instead of direct queries, advertisers may start questioning how confidently they can optimize against that data.

There are also still several unanswered questions around how these approximations actually work.

Google has not publicly explained how much interpretation occurs, whether advertisers can distinguish modeled terms from literal queries, how negative keywords interact with interpreted intent, how closely approximated terms reflect the original user phrasing, or whether reporting consistency could change as AI models evolve.

That lack of detail will likely make some advertisers uneasy.

A marketer could review a search term report and assume they are looking at direct customer language when the term may actually represent Google’s interpretation of the interaction. That distinction matters when advertisers are making optimization decisions, reviewing compliance concerns, or reporting insights internally.

Some Advertisers May Be Comfortable With This Change

On the other hand, there’s probably lots of advertisers who won’t see this as a big deal.

Some advertisers already optimize more around intent themes, conversion quality, and broader performance patterns than exact query language. For accounts heavily using broad match and Smart Bidding, interpreted search terms may not feel dramatically different from how optimization already works today.

There is also a practical challenge Google is trying to solve.

AI-powered Search interactions do not always produce simple keyword queries that fit neatly into traditional reporting. In some cases, a normalized intent summary may actually be easier for advertisers to review than fragmented conversational prompts or image-based searches.

That does not remove the transparency concerns, but it does help explain why Google may view interpreted reporting as a necessary adjustment for AI-powered Search experiences.

What Does This Mean For Future Optimization?

This update may push advertisers to rely less on literal query analysis over time, especially as more Search activity moves into AI-powered experiences.

For years, Search optimization has centered heavily around search term analysis. Advertisers mined queries for negatives, refined match types, identified customer language, and built campaign structures around tightly grouped intent.

If Search Terms Reports increasingly include interpreted intent instead of direct queries, some of those workflows may become less precise.

Optimization may shift further toward broader signals like landing page alignment, first-party data, conversion quality, audience behavior, CRM integrations, and overall content relevance.

That doesn’t make search term reports useless, though.

Advertisers may need to treat them more as directional insight rather than exact representations of customer language.

This could also change how marketers communicate reporting internally.

Many teams still use Search Terms Reports to demonstrate customer intent to executives, clients, or other stakeholders. If some reported terms now reflect modeled interpretations instead of literal searches, marketers may need to be more careful about how those insights are presented and explained.

A reported term may still reflect the general intent behind a search. It just may not represent the exact words the customer used.

Looking Ahead

This documentation update may end up being more important than it initially appears.

Search Terms Reports have long been one of the few places advertisers could directly connect user queries to campaign behavior. Google is now signaling that some of those reported terms may involve interpretation before they appear in reporting.

That will likely become more noticeable as AI-powered Search experiences continue expanding across Google Search.

For advertisers, the bigger issue may simply come down to clarity. If interpreted search terms become more common, many advertisers will likely want more visibility into how those terms are generated and how closely they reflect actual user behavior.

Featured Image: vittaya pinpan / Shutterstock

Condé Nast CEO: Plan As If Search Traffic Will Be Zero via @sejournal, @MattGSouthern

Condé Nast CEO Roger Lynch says 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. He described three consecutive years in which internal budget forecasts underestimated actual declines in search traffic.

Lynch said:

“Each of the last three years, we would do our budgets, and we’d put forecasts in of search traffic declining… Because we’d seen the pattern of algorithm changes. And generally those algorithm changes were negative.”

“Every year, our search traffic was down more than we had forecast. So last year I told our teams, ‘Assume there’s no search.’ You have to have your businesses planned as if search is zero.”

Lynch told TBPN that Condé Nast doesn’t expect search traffic to literally reach zero. He expects it to settle at a single-digit percentage of total traffic.

What Changed

Lynch described how the search results page has changed, based on a comparison his team prepared for a recent board meeting. Lynch recalled:

“We took a snapshot of search results from seven or eight years ago. And what you saw were a few sponsored links, then the ten blue links.”

“Do the same search today, you get an AI overview, then you get rows and rows and rows of commerce links, then you get sponsored stuff.”

He noted that someone had recently asked him how search revenue could be up. “Have you done a search recently?” Lynch replied. “I basically have to go to the second page to get an organic result.”

Lynch acknowledged that changes in search traffic have affected Condé Nast’s business. The company has continued to grow revenue and profitability despite the decline, which he called a “headwind” rather than a crisis.

The Barbell Effect

Lynch described what he called a barbell effect across the Condé Nast portfolio. In his telling, large, authoritative brands and small niche publications with loyal audiences are performing well. Brands caught in the middle are the most exposed.

“Vogue has grown every year I’ve been at the company. It grows revenue, grows profitability every year,” Lynch said.

The New Yorker had its most successful year ever, he added. On the other end, Lynch pointed to Pitchfork, which represents about 1% of Condé Nast’s revenue but has a loyal audience in its category.

Lynch explained:

“If you try to be too broad, too large of an audience, this is not the era for that… You either need to be large and authoritative in a big category… or you need to be really nailing a specific niche where you have a loyal audience that’s willing to pay.”

Lynch added that brands in the middle of that barbell, those without deep authority in a category or strong enough niche focus, don’t have a clear path forward.

He added:

“If you don’t have really strong authoritative brands, or brands that have very strong niche in certain areas, or direct audiences, then you’re just going to be fighting that all the way down.”

Subscriptions As The Replacement

Condé Nast’s digital subscriptions grew 29% in revenue last year, according to Lynch. The company reported double-digit growth, which is continuing this year.

Lynch noted the company has raised subscription prices “fairly materially” over the past couple of years. He expected retention to decline with each increase. Instead, retention improved every year.

The company is also expanding subscriptions to smaller brands. Pitchfork and Tatler both launched paid digital subscriptions recently.

Why This Matters

Lynch’s comments are consistent with third-party measurements indicating that publisher search referrals are under pressure. Chartbeat data reported in March showed search referral traffic fell 60% for small publishers over two years. A Reuters Institute survey found media leaders expect search traffic to decline by more than 40% over three years.

Google’s VP of Search, Liz Reid, has reframed those losses as reductions in low-quality “bounce clicks.” Google hasn’t shared publisher-facing data to support that claim.

Lynch’s directive carries weight because of the portfolio behind it. Condé Nast operates Vogue, The New Yorker, GQ, Vanity Fair, Architectural Digest, Condé Nast Traveler, Wired, and Pitchfork, among others. When the CEO of a portfolio that includes those brands says teams should budget for zero search traffic, it gives industry data a concrete example from a major publisher.

The barbell observation matters for anyone managing a publisher caught between the two extremes. Lynch is describing a version of the pressure Chartbeat’s size-segmented data has tracked. Small and mid-tier publishers without deep category authority or direct audience relationships face the steepest declines.

Looking Ahead

Lynch told TBPN the company has started evaluating each brand’s plan for a low-search future. The company is prioritizing brands that can show a path forward without search traffic.

Lynch’s comments may put pressure on other large publishers to formalize similar planning. The trend data has been consistent enough that budgeting for search decline is already common. Budgeting for zero is a different level of preparation.

Why Your SEO Work Isn’t Getting Implemented (The IT Line Of Death) via @sejournal, @billhunt

I recently spoke with an SEO who, along with his entire team, had just been laid off. The company was rapidly losing organic traffic, leadership was frustrated, and from their perspective, nothing was being done to fix it. The SEO saw it very differently. They had submitted more than 1,400 tickets over the previous 18 months, each documenting an issue and outlining the importance of what needed to be done. The backlog was extensive, detailed, and, in their mind, proof that the SEO team was working hard to reverse the decline. The problem was that none of the requested actions had been implemented. Engineering time had been consistently redirected to CEO initiatives, product launches, and other internal priorities that always seemed to matter more. From the SEO’s point of view, the work existed. From the business’s point of view, nothing had changed. Traffic declined, visibility dropped, and eventually a decision was made to eliminate this underperforming team.

A backlog is not progress. It is an unimplemented intent.

This is the uncomfortable reality many practitioners struggle to accept. Submitting tickets is not the job. Getting them implemented is. If your recommendations never make it into production, they do not exist in any meaningful way. They do not drive traffic, they do not improve visibility, and they do not protect the business as Google continues to evolve. And right now, that evolution is accelerating, which makes the gap between activity and impact even more dangerous.

Align With What Already Matters

You can see how organizations are frantically responding to the pressure to perform in AI Search, albeit subtly. Work that sat untouched for months as “SEO improvements” suddenly gets prioritized when it is reframed as AI readiness, Generative Engine Optimization, or content structuring for AI discovery. Nothing about the underlying work changes, but the framing does, because it aligns with what leadership believes matters in that moment. It may feel frustrating, even cynical, but it reveals a deeper truth.

At IBM, we struggled to get many SEO initiatives prioritized. A report later flagged our site search experience as poor and negatively impacting sales of our own search product. The required improvements were largely the same as those we had been recommending for external SEO. By relabeling them as “site search fixes” under this new mandate, we were able to accelerate implementation and improve both internal and external search performance. Work is not prioritized because it is the right thing to do. It is prioritized because it aligns with the current narrative of impact and executive priorities. To understand why so much SEO work fails to cross that threshold, you have to look at where decisions are actually made.

The Line You Don’t See Until It Stops You

After selling my agency, I took on a project for a company that was already performing well in organic search. Then Google launched paid search, and everything shifted. Large advertisers began reallocating their budgets because buying search traffic directly from Google suddenly looked more efficient than advertising on websites that simply arbitrage organic traffic to generate the ad impressions they had purchased.  The board’s response was immediate and direct. They wanted to dominate every aspect of their category and be in the top three across the board, and they were willing to provide me with whatever resources were necessary to make that happen.

So I went to engineering with my plan and list of activities for total domination, expecting complete alignment and momentum. Instead, the CTO walked me to a whiteboard and pointed to a faint dotted line. Anything above that line, he explained, might get implemented this fiscal year. Anything below it would not. There was no debate or negotiation. Every idea, no matter how strategically sound, had to either fit above that line or displace something already there. It was a simple constraint of available resources, and it made one thing clear: what was already there mattered. He told me that those initiatives were also blessed by the same executives who greenlit mine. These existing initiatives were tied directly to revenue, others to compliance or security, and some were simply protected by stakeholders with enough influence to keep them in place.

That was the moment the reality became clear. This line, invisible in every audit and absent from every SEO tool, determines what actually gets built. I call it the “IT line of death.” Your mission, as an SEO or GEO manager, is to find creative ways to get your activities into or to replace one of those above-the-line projects.

From Tasks To Contribution Value

Most SEO recommendations do not fail because they are wrong. They fail because they are not competitive within that resource allocation system. This means everything is a trade-off. Engineering does not evaluate your recommendation in isolation; they evaluate it against everything else competing for their time and resources. Revenue-driving features, compliance requirements, infrastructure improvements, and existing commitments all carry weight. And so does the requester. When SEO shows up as a collection of disconnected fixes, it struggles to compete because it lacks a clearly articulated cost, ownership, and relative impact.

That realization forces a shift in how SEO needs to be approached. It is no longer enough to identify issues. You have to justify why they deserve to exist above the line and are as important as or more important than another activity. That means translating work into effort, impact, and trade-offs. It means moving from tasks to contribution value. Audits, tickets, and backlogs describe activity, but engineering teams do not fund activity. They fund outcomes. If you cannot explain why your recommendation is worth more than another team’s request, it will not get done.

This is where many SEO programs stall. They are rich in insight but weak in prioritization, and that gap becomes even more visible when you look at how work actually gets implemented. It is often difficult to tie SEO activities directly to revenue or basket size, but that does not remove the responsibility to try.

Fix The Systems, Not The Symptoms

Once you understand your organization’s IT line of death, the question becomes practical. How do you get work implemented in an environment where everything is competing? The answer is not to push harder, but to work differently within the system. In most organizations, the fastest path to implementation is not to create new work but to align with work already in motion. Engineering teams are constantly updating templates, redesigning page structures, migrating platforms, or refactoring components. Those initiatives already sit above the line. They already have a budget, attention, and momentum. When SEO is introduced as a separate request, it has to fight for priority. When it is embedded into an existing initiative, it inherits that priority. Some of the most impactful SEO changes are implemented this way, folded into broader projects rather than introduced as standalone efforts.

This becomes even more effective when you focus on scale. Isolated fixes rarely justify prioritization, but changes that act as force multipliers do. Updating a template rather than a single page can affect thousands of URLs. Adjusting CMS logic can eliminate entire categories of issues. Fixing navigation or internal linking can reshape how the entire site is understood and crawled. These are the types of changes that connect relatively small effort to large-scale impact, which makes them far more competitive at the line.

Even then, success depends on understanding the problem at its source. One of the most common failure points in SEO is diagnosing symptoms instead of causes. Large numbers create urgency, but they can also mislead. Thousands of redirects, tens of thousands of 404 errors, and duplicate pages across a site often trigger large remediation efforts, yet they are frequently just the visible output of a much smaller issue.

I worked with a company that generated pages from a product feed daily, with URLs based on the product name and its first attribute. It seemed logical, but the attribute was not stable. Every time it changed, the URL changed with it. That single design decision created a cascade of problems. New pages were constantly being created, old URLs turned into 404s, and the site effectively churned its own index. The Search Console error log reflected this chaos, filled with tens of thousands of issues that needed fixing. But none of those issues was the real problem. The solution was not to clean up the errors; it was to stop creating them. By realigning the URL structure to a stable identifier such as a SKU, the entire system stabilized. The errors disappeared because the mechanism producing them was removed. One change replaced thousands of remediation tasks.

This is the difference between work that stays below the line and work that crosses it. The former treats symptoms, the latter resolves the system that generates them. This dynamic is not unique to a single company or a single moment in time. It shows up consistently across organizations, industries, and levels of Search maturity. Whether the constraint is engineering bandwidth, compliance requirements, or competing product priorities, the outcome is the same. Work that cannot justify itself at the line does not happen. We explored this further in a podcast episode, breaking down how this pattern repeats and why so many well-intentioned initiatives stall before they ever reach production. The conclusion was consistent. Most SEO work does not fail because it is wrong; it fails because it is not framed in a way the organization can act on.

Once you understand that, the role of SEO changes. You are no longer just identifying issues; you are shaping decisions. You are defining what is worth doing, why it matters now, and what impact it will have relative to everything else competing for attention. That is what moves work from backlog to implementation.

In the end, nothing gets done because it is best practice. It gets done because it is worth doing.

More Resources:


Featured Image: Roman Samborskyi/Shutterstock

Google Research’s ALDRIFT: AI Answers That Do More Than Sound Plausible via @sejournal, @martinibuster

Google Research published a paper that studies how to make generative AI systems produce answers that do more than sound plausible. The researchers say that their ALDRIFT framework “opens exciting avenues” for moving beyond answers that merely have a high probability.

The paper, titled “Sample-Efficient Optimization over Generative Priors via Coarse Learnability,” examines a problem in which generated answers must remain likely under a model while also moving toward a separate goal. The research points toward new avenues for addressing the AI plausibility trap.

Google ALDRIFT

The evidence in the paper centers on a framework called ALDRIFT (Algorithm Driven Iterated Fitting of Targets). The method repeatedly refines a generative model toward lower-cost answers and uses a correction step to reduce accumulated error during the process.

The paper also introduces “coarse learnability.” The term means the learned model does not need to perfectly match the ideal target. It needs to keep enough coverage over important parts of the answer space so useful possibilities are not lost too early. Under that assumption, the authors prove that ALDRIFT can approximate the target distribution with a polynomial number of samples.

ALDRIFT Operates On A Two-Part Setup

ALDRIFT operates on a two-part setup:

  1. The generative model represents what kinds of answers remain likely under the model.
  2. The outside scoring process measures whether a candidate answer performs well against the target goal.

The authors describe that score as a “cost.” The word “cost” refers to the measured penalty assigned to a candidate answer. A lower cost means the candidate did better according to the requirement being checked. ALDRIFT does not simply search for any low-cost answer. It searches for answers that score well while still remaining likely under the generative model.

Some AI Answers Need To Work As A Whole

The researchers are focused on AI answers for problems where the response has to function in the real world such as their examples of route planning and conference planning.

  • Route planning: The paper explains that an LLM may evaluate whether individual route segments are scenic, but may struggle to ensure that those segments connect into a valid path.
  • Conference planning: An LLM may group sessions by topic, while a classical algorithm may be needed to schedule those sessions into a timetable without conflicts.

These examples show why the paper treats plausible answers as only part of the problem. The harder issue is producing answers that remain coherent when separate parts have to work together as one complete solution.

The Coarse Learnability Assumption

The paper treats this as a problem of guiding a generative model toward answers that hold together across all their parts. The authors connect the problem to inference-time alignment, where a model is adjusted during use based on whether a specific answer works as a complete solution. That connection gives the research practical relevance, although the paper’s contribution remains theoretical and depends on the coarse learnability assumption.

The phrase “coarse learnability assumption” means the paper’s theory depends on an assumption that the model can keep enough useful possibilities available while it is being pushed toward better answers.

It does not mean the model has to learn the target perfectly. It means the model has to preserve enough coverage of the answer space so the process does not get stuck too early or lose possible better answers.

Existing Optimization Methods Leave Sample-Limited Gaps

The paper identifies several gaps in how existing optimization methods are understood:

  • Limitation of existing methods: Classical model-based optimization methods rely on “asymptotic convergence arguments.” This means they are theoretically understood after very large amounts of sampling, but not necessarily in practical settings with limited samples.
  • Failure with expressive models: The paper says these classical assumptions “break down” when using expressive generative models like neural networks.
  • Gap in understanding: The authors say the “finite-sample behavior” of optimization in this setting is “theoretically uncharacterized.” That means the theory does not fully explain how these methods behave when only limited samples are available.

The paper’s solution is to introduce “coarse learnability” to explain how a generative model can be pushed toward better answers while keeping enough useful possibilities available along the way.

The LLM Evidence Is Limited

The paper’s main proof applies to analytic generative models, which are easier to analyze mathematically than modern LLMs. The LLM evidence is narrower: the authors use GPT-2 in simple scheduling and graph-related problems, showing behavior that supports the idea without proving that the same assumptions hold for modern LLMs.

The Research Points To A Foundation For Future Research

The paper offers a theoretical foundation for studying how generative models could be combined with external checking processes.

The research shows that Google researchers are exploring a framework for addressing the “plausible answer” problem, and the authors write that the “framework opens exciting avenues for future research.” They conclude that this research points “toward a principled foundation for adaptive generative models.”

Takeaways

  • The “Coverage” Requirement:
    Coarse learnability means the model does not have to learn the target perfectly. It needs to avoid losing useful areas of the answer space where better solutions might exist.
  • The Correction Step Matters:
    ALDRIFT uses a correction step to keep the search closer to the intended target as the model is pushed toward better answers.
  • Two-Part Approach:
    The framework uses a division of labor. The generative model handles qualitative or semantic preferences, while a separate process checks whether the answer works as a complete solution.
  • Limited LLM Evidence:
    Tests with GPT-2 showed behavior that supports the idea in simple scheduling and graph-related examples, but not proof that the same assumptions hold for modern LLMs.
  • Real-World Use Is The Larger Goal:
    The research matters to SEOs and businesses because AI answers are increasingly expected to do more than summarize information. They need to support decisions, plans, and actions that hold together outside the chat interface. While the framework is likely not being used in production, it does show Google is making progress on providing answers that are more than plausible.

Read the research paper here:

Sample-Efficient Optimization over Generative Priors via Coarse Learnability (PDF)

Featured Image by Shutterstock/Faizal Ramli

Data Shows AI Overviews Exposing Negative Reviews Without User Intent. What To Do Next via @sejournal, @EraseDotCom

This post was sponsored by Erase.com. The opinions expressed in this article are the sponsor’s own.

Why does AI pull a 2023 Reddit thread into a 2026 comparison query?
What makes AI cite some complaints about my brand and skip others?
How do I get AI to stop citing old complaints in unrelated queries?

Four signals decide what AI exposes, and once you know them, you can work them.

Q1 2026 analysis surfaces four consistent patterns in what AI engines cite: recency plus volume, specificity that names features, platform authority (Reddit, major review sites), and recurrence across sources. The complaints that hit all four are the ones that show up unprompted in queries where users were looking for solutions, not problems. The fix isn’t a single takedown request; it’s a four-step audit-and-rebuild framework mapped to those same four signals.

When someone asks ChatGPT “which CRM should I choose,” these AI engines don’t just list features. They pull in user complaints, Reddit gripes, and years-old forum threads as part of their comparison. Your brand’s negative signal can appear in an answer about your competitor. Even more concerning, as Fast Company recently reported, there’s growing evidence of AI engines misquoting or misrepresenting brand statements, compounding the challenge of maintaining an accurate reputation in AI-generated summaries.

AI Comparison Queries Are Now Reputation Audits. Here’s What That Means.

Traditional reputation management focused on suppressing results when someone searched “[your brand] + reviews.” That’s still important, but it’s no longer sufficient.

It’s time for a reputation audit.

AI Overviews and LLM-powered search engines treat every product comparison as an opportunity to synthesize user sentiment. When evaluating options, these tools actively scan for negative reviews on complaint sites, Reddit discussions, forum threads, gripe site entries, and customer support complaints that made it into public view.

The critical difference: users aren’t asking about problems. They’re asking about solutions. But AI engines interpret “helping” as including negative signals from your brand footprint.

Why Some Complaints Show Up in AI Answers & Others Don’t

Not every negative mention gets pulled into AI-generated answers, but certain patterns increase surfacing likelihood:

  • Recency + volume: Fresh complaints with multiple corroborating sources rank high.
  • Specificity: Vague posts get filtered out. Detailed complaints that include product names and outcomes are weighted as valuable context.
  • Platform authority: Reddit, Trustpilot, G2, and industry forums get treated as trusted sources.
  • Recurrence across sources: If the same issue appears in multiple places, AI engines treat it as a verified pattern.

The 4-Step Framework: How to Audit, Remove, Rebuild, and Suppress Your Brand’s AI Reputation Signals

Understanding what’s in your negative signal footprint, prioritizing what can and should be addressed, and building a positive content layer that represents your brand accurately when AI tools pull information is the key to success.

Map what AI engines can access about your brand across platforms where complaints surface.

  1. Open ChatGPT or Perplexity and type: “What are the pros and cons of [your brand] vs [top competitor]?” Take a screenshot of the response and note any negative claims.
  2. On Google, search site:[key platform].com “[your brand name]” + “scam” OR “complaint”. This forces the search engine to show you only the filtered conversations AI models are currently scraping.
  3. Search for your brand on Google and check the featured snippets for anything negative, other SERP features like People also ask for negative or adversarial searches.

Key platforms to check:

  • Review platforms (Trustpilot, G2, Capterra, Yelp, Google Business Profile).
  • Reddit (search your brand name + product category + complaint terms).
  • Industry forums (Stack Overflow for tech, niche communities for specialized services).
  • Facebook groups and community pages (particularly industry-specific or local groups where your customers congregate).
  • Social media (Twitter/X, LinkedIn discussions, TikTok comments).
  • Legacy gripe sites (RipoffReport, Complaintsboard); while largely deindexed, content may still be cited by AI engines.

Document these details:

  • Content type and platform.
  • Date posted.
  • Specific claims made.
  • Factual accuracy.
  • Current visibility in Google and AI summaries.

Focus on detailed complaints with enough context that AI engines might treat them as credible sources.

Step 2: Prioritize Based on Surfacing Likelihood

Focus on:

  • High priority: Recent complaints with specific details, issues mentioned across multiple platforms, content on high-authority platforms (Reddit, major review sites), complaints naming features or pricing specifically.
  • Medium priority: Older complaints (1-2 years) still in search results, isolated reviews without corroboration.
  • Low priority: Very old content (3+ years) with low engagement, complaints about discontinued products.

How To Create A Priority Matrix

Create a simple scoring matrix to decide what to tackle first:

  • High Priority: Content that appears in AI summaries AND has high organic visibility (check Semrush or Ahrefs for estimated monthly visits to that specific URL) or compare them against queries for those keywords that you have available in search console – if it’s a branded search, you should have full visibility on this from search console.
  • Verified Impact: For platform-specific reviews (G2, Trustpilot, Google Business), use your internal analytics to track how many users are clicking “Helpful” on negative reviews. A review with 50+ “Helpful” votes is a massive signal that AI engines will not ignore.

Step 3: Remove or Respond Where Possible

Some negative content can be removed outright. Some deserve a response, and some require both.

How to Get Negative Content Taken Down

If the content violates platform policies (false information, impersonation, harassment), request removal through the platform’s reporting process.

For legacy complaint sites and gripe sites, professional content removal services can often negotiate takedowns based on inaccuracies or policy violations, though as reputation defense strategies evolve for AI, the focus has shifted from simply removing content to building stronger positive signals.

For content that mentions you but doesn’t necessarily focus on your brand (like a Reddit thread comparing five tools where yours gets one negative mention), removal usually isn’t an option, but you can dilute its impact by ensuring positive mentions appear more frequently in similar discussions.

When Responding Publicly Actually Helps You

Legitimate complaints about real issues, misunderstandings you can clarify with facts, or service failures where an explanation adds credibility. Keep responses factual, non-defensive, and focused on resolution. AI engines can pull your response into summaries, giving you a chance to reframe the narrative.

When Engaging Makes Things Worse — Skip It

Fake reviews, emotional rants without substance, old complaints about discontinued products, or situations where engagement will amplify visibility.

Step 4: Build a Positive Content Layer That AI Engines Prefer

This is where ongoing reputation management becomes critical. You need owned and earned content that AI engines will preferentially cite when answering comparison queries.

What Goes Into A Positive Content Layer

  • Structured FAQ content: Create pages answering common objections and questions with clear headers and schema markup.
  • Case studies: Detailed examples with metrics, timelines, and direct customer quotes give AI engines concrete data to cite.
  • Community presence: Contribute to Reddit and forums where your audience asks questions. Build credibility through value, not promotion.
  • Third-party validation: Get featured in roundups and comparison articles on authoritative sites.
  • Regular content updates: AI models prioritize recent content. Keep your owned content fresh.
  • How this plays into broader online reputation management: What you’re building isn’t just an AI strategy—it’s a defensible reputation infrastructure. Comprehensive, recent, authoritative content across multiple touchpoints creates a buffer that makes it harder for isolated negative signals to dominate.

How To Build A Positive Content Layer 

  1. Turn your FAQ into a knowledge base that addresses common objections (e.g., “Is [your brand] worth the price?”). Depending on how much reach and authority your brand has, it can be worthwhile to publish these as their own pages with a clear H1 question as the headline and breadcrumb the Q and As in a format like /faq/[service area]/[objection] to create more internal linking opportunities and depth rather than just having everything on a massive FAQ page.
  2. Reach out to some of your satisfied customers and ask for a 2–3 sentence quote about a specific outcome they achieved. Publish these as a case study snippet on your site. Specificity (metrics, timeframes) helps to ensure LLMs treat content as credible evidence rather than marketing copy. Link to their LinkedIn or business website, if possible, to help reinforce that it is a real review for a real customer.
  3. Identify high-authority “Best of” lists or industry roundups where your brand is missing and email the editors to provide a unique expert insight or updated product data for inclusion. These seed high-trust citations that AI engines prioritize when synthesizing brand comparisons and reputation summaries. The higher they rank on Google, the better.

Monitoring becomes essential at this stage. Track which keywords trigger AI Overviews that mention your brand, watch for new complaints surfacing in high-authority platforms, and measure whether your positive content is getting cited in AI-generated comparisons. This isn’t a one-time project; it’s an ongoing program.

Start Here: Your Easy Steps to Managing Your AI Reputation

If you’re dealing with high-stakes reputation issues where missteps could amplify problems, specialized online reputation management services and experts like our team at erase.com can help you move faster and avoid pitfalls. The goal isn’t just reacting to what’s already out there; it’s building a system where positive signals consistently outweigh isolated negatives when AI engines scan for information.

The shift is already here. The question is whether you’re managing it proactively or discovering it reactively when a prospect mentions “something they saw in ChatGPT.”


Image Credits

Featured Image: Image by Erase.com. Used with permission.

Lessons Learned From Adobe’s 2026 Q2 AI Traffic Report via @sejournal, @slobodanmanic

The sign on AI-referred traffic conversion flipped. I’m not sure if enough of us have noticed.

Twelve months ago, visitors arriving at U.S. retailers from AI assistants converted at roughly half the rate of visitors from other channels. In March 2026, they converted 42% better. Same channel. Same stores. Different year.

Adobe Analytics published the 2026 Q2 AI Traffic Report on April 16 (Adobe’s fiscal Q2 covers calendar Q1 2026). The growth numbers land first: AI-referred traffic to U.S. retailers grew 393% year-over-year in Q1 2026, peaking at 1,151% YoY in December. Engagement up 12%, time spent up 48%, pages per visit up 13%, revenue per visit up 37%. All measured against non-AI traffic in March 2026, using Adobe’s own analytics data from retailers running on the Adobe platform.

The real story is the conversion sign flip. The channel went from worst-performing in U.S. retail to best-performing. In 12 months.

If you run or optimize a website, this changes which number actually matters to you.

One caveat worth naming up front. Adobe publishes this report alongside Adobe LLM Optimizer, a product they sell for making websites more visible to AI assistants. The research and the product roll out together, and the link sits inside the report itself. The underlying numbers are Adobe’s own, self-reported from their analytics platform, and the kind of data that would be hard to fake and easy to challenge if it weren’t accurate. But the framing should be read knowing the vendor also sells the tool that addresses the problem the report describes. Thanks to Els Aerts for flagging this.

2026 Adobe Report Suggests AI Traffic Converts Better Than Non-AI Traffic

This is not something slowly getting better. This is something that’s gone from pretty much broken to kind of working.

Maturation would look like half the non-AI rate to 25% worse to 10% worse to break-even to slight edge. Three, four years of grind. Slow curve. Predictable report cycles. That’s what maturation normally looks like for a new channel. Paid search did that. Mobile did that. Social did that. AI-referred traffic is not doing that. Two measurement checkpoints twelve months apart, sign flipped. Different kind of event.

The playbooks calibrated to “AI traffic is early, optimize gradually, the channel isn’t mature yet” are calibrated to the wrong curve. Any agency, consultant, or vendor still saying “early stage” or “not ready” about AI retail traffic hasn’t read this month’s numbers. The tell is in the timeline they propose. If the pitch is “let’s learn what works over the next year,” they missed the flip.

They’re working from a brief that’s twelve months out of date.

Why AI Agents Fail To Parse Non-Readable Retail Websites

Adobe’s report dedicates an entire section to what they call Citation Readability: how well a page can be understood, parsed, and surfaced by AI systems. The gap between top and bottom performers is brutal. Homepages from top-AI-visit-share retailers score 62% higher than the bottom. Search results pages, 32% higher. Blog and editorial content, 30% higher.

Read that as an operator’s diagnostic. Adobe is telling you why the growth is uneven.

The 393% aggregate is what’s getting through despite readability gaps. Retailers whose pages AI models can actually parse and cite are pulling the average up. Retailers whose pages AI can’t read reliably are dragging it down.

Most website owners don’t even know their website isn’t entirely readable by machines.

Not “we know we’re behind on AI.” Not “we’re testing.” Website owners who run their analytics every morning, review conversion rates every week, argue about CRO every quarter, have no visibility into what a GPTBot, ClaudeBot, or PerplexityBot sees when it crawls their product page. Their dashboards don’t show when an AI indexer fetched a shell. Their session recordings don’t capture bots. Their attribution rarely tags AI referrals cleanly.

The real conversion lift on websites that are actually machine-readable is higher than the aggregate suggests. The average is being held down by everyone else.

Comparing Dell’s Internal Data Vs. Adobe’s AI Traffic Trends

Eight days before Adobe published this data, Dell’s head of global consumer revenue programs told Digital Commerce 360 that agentic shopping is delivering “nothing to the point that is earth-shaking” yet.

Both things are true at the same time.

There’s a chance Dell’s website is bad. It’s not that the entire industry of AI-assisted shopping is wrong. Dell was measuring one website. Adobe was measuring aggregate traffic across many retailers. Dell looked at their own conversion data, saw flat numbers, published the number. Adobe looked at the set of websites AI models can read and cite, saw a channel inversion, published that.

If your conversion numbers look like Dell’s, don’t wait for the channel to mature. Audit the website. Dell’s admission is a diagnostic about dell.com. Adobe’s data is about where the channel is going. Don’t confuse them.

How AI-Assisted Research Shortens The Purchase Funnel

Traffic growth the way we were trained to think about it in the last 30 years, that doesn’t matter at all anymore.

Impressions. Sessions. Unique visitors. Page views. The vocabulary that defined SEO and CRO practice from 1998 to 2024. All of it assumed traffic meant humans arriving to decide. You grew top-of-funnel, so more humans entered deliberation. You optimized the funnel so more of them converted. That was the arithmetic.

AI-referred traffic doesn’t work like that.

When someone clicks through from ChatGPT, Perplexity, or Gemini, they’ve already done their research inside the assistant. They compared options. They asked follow-up questions. They landed on a shortlist. The click to your website is the last step in a decision, not the first. Adobe’s numbers reflect this: 12% higher engagement, 48% longer time per visit, 37% higher revenue per visit. That’s not a better funnel. It’s a shorter funnel. Most of the consideration happened off your website.

If you’re optimizing for volume (more impressions, more sessions, more referrals), you’re optimizing for the old economy. The retailers winning this 393% growth are the ones the AI assistants actually cite, link to, and send pre-qualified buyers to. That’s a legibility problem, not a visibility one.

Technical Audit For AI Crawlers And JavaScript Readability

Two things you can verify this weekend, without tools, without a team, without budget.

Disable JavaScript. Fresh browser profile, JavaScript off, reload a product page. Is the price there in the HTML? The name? The stock status? The buy button? Most AI crawlers that index pages for citation don’t execute JavaScript, or execute it inconsistently. If the critical facts need JavaScript to render, the AI can’t cite what it can’t see, and your page won’t surface as a reference in the assistant’s answer.

Check the answer-first test. Does your product page lead with what the thing is, what it costs, and whether it’s available? Or does it lead with brand nav, hero imagery, lifestyle copy, and a carousel? AI models retrieving and summarizing your page pick up the first dense, structured facts they find. Humans tolerate brand theater. AI indexers don’t scroll past it to find the price.

If both check out, flat AI numbers are a distribution problem. You’re not being referred. Work on that separately. If either fails, it’s an architecture problem. The 393% is passing you by.

Legibility Vs. Optimization For AI Referral Traffic

AI-referred traffic doesn’t reward optimization. It rewards legibility. Those are not the same thing.

More Resources:


This post was originally published on No Hacks.


Featured Image: Thefirst7/Shutterstock