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.
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 attacked the credibility of the two tech leaders. Sam Altman was accused of lying and self-dealing, while Elon Musk was portrayed as a power-seeker trying to control artificial general intelligence.
The case unearthed new details about the two arch-rivals and OpenAI’s contested nonprofit status, as well as a golden trophy of a donkey’s ass awarded to an employee who challenged Musk.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Trump traded hundreds of millions in tech stocks before favorable policy moves He bought shares in Nvidia, AMD, and Arm ahead of policy boosts. (Quartz) + And touted Palantir on Truth Social after buying its stock. (CNBC) + His crypto venture and Iran’s top exchange tapped the same networks. (Reuters $)
2 SpaceX plans to list on the Nasdaq stock exchange as soon as June 12 It wants to raise up to $75 billion at a $1.75 trillion valuation. (Reuters $) + BlackRock may invest up to $10 billion in the offering. (The Information $) + Cerebras’ blockbuster IPO has boosted hopes for the listing. (CNBC) + Which is set to dwarf many of the biggest IPOs on record. (Reuters)
3 Chinese AI groups have pulled ahead of US rivals in video generation ByteDance and Kuaishou’s models lead in realism and scale. (FT $) + AI is fueling China’s short-drama boom. (MIT Technology Review) + While its AI labs are betting big on open source. (MIT Technology Review)
4 Iran says it will charge Big Tech for using undersea internet cables The cables beneath the Strait of Hormuzcarry vast digital traffic. (CNN) + Tech bosses met at Uber HQ on Saturday to discuss Iran’s future. (404 Media)
5 Samsung has a “last chance” to stop a massive strike over AI Over 45,000 employees could walk out for 18 days this week. (CNBC) + They want a bigger share of the AI boom. (FT $) + Samsung and its largest labor union will resume talks on Tuesday. (Reuters $)
6 Old oil and gas wells could become a new source of clean energy US states plan to convert them into geothermal energy assets. (Wired $) + A balcony solar boom is coming to the US. (MIT Technology Review)
7 The ChatGPT era has triggered a 30% surge in grades at a top university Grades inflated in text-heavy courses but remained flat in others. (Axios) + Princeton has changed its honor code because of AI cheating. (WSJ $) + And real cheating rates may be far higher. (The Times $)
8 Ex-Google CEO Eric Schmidt was fiercely booed during an AI speech His graduation speech praising AI agents sparked uproar. (The Verge) + A populist backlash is building against AI. (MIT Technology Review)
9 Arm faces a US antitrust probe over its chip tech licenses Regulators are investigating whether it has an illegal monopoly. (Bloomberg $) + Qualcomm has accused Arm of anticompetitive conduct. (Reuters $)
10 ArXiv will ban researchers who submit AI slop Offending authors face year-long bans from the pre-print server. (TechCrunch)
Quote of the day
“When someone offers you a seat on the rocket ship, you do not ask which seat. You just get on.”
—Ex-Google CEO Eric Schmidt extolls the virtues of AI agents in a graduation speech at the University of Arizona, prompting a chorus of boos.
One More Thing
WYSS INSTITUTE AT HARVARD UNIVERSITY
Is this the end of animal testing?
In a clean room in his lab, Sean Moore peers through a microscope at a bit of human intestinal tissue growing on a plastic chip. It’s one of 24 so-called “organs-on-chips” his team bought three years ago. The technology is designed to mimic human biology—and could reduce the need for animal testing.
The appeal is not only ethical. Around 95% of drugs developed through animal research ultimately fail in people, and early studies suggest organ-on-a-chip systems may offer more accurate insights into how diseases behave and how drugs work. But the field still faces major technical and cost challenges before it can replace animal research.
Optimizing the “human as a weapons system”: Anduril is building smart glasses that let soldiers order drone strikes through eye-tracking and voice commands.
Two different bets: The company is pursuing both a $159 million Army contract and a self-funded helmet-headset combo called EagleEye. The latter is something the military never asked for, but Anduril is confident the Army will eventually prefer it.
The attention problem: Soldiers already drowning in information could reject the technology if it demands more mental bandwidth than it saves. And a smart glasses system tasked with identifying threats and recommending strikes would introduce massive new risks of mistakes.
A high bar, years away: The system must survive dust, explosions, and limited connectivity. The Army won’t even put a prototype into production until 2028, if it picks one at all.
The defense-tech company Anduril has shared new details about the augmented-reality headset for the military it’s prototyping with Meta, including a vision for ordering drone strikes via eye-tracking and voice commands.
Quay Barnett, who leads the efforts as a vice president at Anduril following a career in the Army’s Special Operations Command, says his fundamental goal is to optimize “the human as a weapons system.” The vision is undoubtedly cyborg-inspired: Barnett wants drones and soldiers to see together, share information seamlessly, and make decisions as one.
Anduril actually has two such projects in the works. The first is the Army’s Soldier Born Mission Command, or SBMC, for which the company won a $159 million prototyping contract last year to work with Meta on augmented-reality glasses to attach to existing military helmets. But Anduril has also embarked on a self-funded side quest, announced in October, to design its own helmet and headset combo called EagleEye. This is something the military has not asked for, but Anduril insists it will prefer it and purchase it in the end.
So far, both systems are years away. The Army isn’t expected to move its top choice for the SBMC program into production until 2028, if it picks one at all (the previous lead for the effort, Microsoft, was set to receive a $22 billion production contract that was ultimately cancelled when the glasses didn’t prove viable). But Barnett told MIT Technology Review about where both Anduril’s prototypes are headed.
Depending on the situation, the glasses for either prototype will overlay certain information onto a soldier’s field of view. This might be as simple as a compass or as complex as an entire map of the area, information about where nearby drones are flying, or AI-driven recognition of a target like a truck.
The soldier would then speak to the interface in plain language—for example, to order an evacuation for someone who’s been injured or to plan a route taking into account which areas are off limits. A large language model—Anduril is in tests with Google’s Gemini, Meta’s Llama, and even Anthropic’s Claude, despite the company’s conflict with the Pentagon—will be used to help translate a soldier’s speech into commands the software can follow. And the engine for it all will be Anduril’s software Lattice, which incorporates data from lots of different military hardware into one picture. The Army announced in March that it would spend $20 billion to integrate Lattice with essentially its entire infrastructure.
Barnett’s team is designing the headset to carry out multi-step tasks. A soldier might send a drone to surveil an area and instruct it to come back once it’s found something that looks like an artillery unit; then the system would recommend courses of action, like sending a nearby drone to strike, that would have to be approved by the normal chain of command. Leading the system through this, if all goes to plan, might not even require speech; the soldier could instead communicate through tracked eye movements and subtle taps.
That’s the idea, anyway. It’s worked on early prototypes, Barnett says, but there aren’t yet versions ready for the Army to test at scale. The component parts began arriving in March. Because of federal military contracting rules, these parts—unlike Meta’s commercial smart glasses—required new supply chains that don’t rely on Chinese companies.
It’s a lot for soldiers already bogged down in information overload, says Jonathan Wong, a former US Marine who works as a senior policy researcher at RAND on Army efforts to buy new tech. Both smart glasses projects aim to create a clean interface that presents only the right information at the right time. But it’s a product that soldiers will reject if it costs more of their attention than it saves. “How much mental bandwidth do you have to be both aware of your surroundings and to operate this technology in a way that makes you and your whole unit better?” he says.
Wong recalls that as a platoon commander, for example, he had a radio that operated on three different channels at once. “The moment that two people were on different channels talking at the same time, I immediately couldn’t comprehend anything that either one of them was trying to tell me, and I was probably not aware of my own surroundings,” he says. “I think there are limits to what you can take in.”
Ideally, Barnett says, smart glasses can ease that information overload. Anduril’s approach is to get creative with ways the user can access necessary information quickly. Voice commands and eye tracking are a piece of that strategy. But even if it’s all technically feasible, it might take years of field testing to know if the system is actually useful for soldiers, Wong says.
Such a system would mark a major escalation in how closely soldiers rely on imperfect AI systems. While computer vision models used to identify objects have long been employed by militaries, and chatbots have recently entered decision-making during the war in Iran, these technologies have not yet made their way to most frontline soldiers. A smart glasses system tasked with identifying threats and recommending strikes would introduce massive new risks of errors.
Anduril is not the only one competing to develop smart goggles for combat. Rivet, which specializes in wearable sensors for the military, received a $195 million prototyping contract the same time, and in March the Israeli defense-tech company Elbit received its own $120 million contract. This all comes after Microsoft lost its role leading the Army’s smart glasses effort, following a Pentagon audit that found the Army wasn’t properly testing the glasses, a mistake that could have wasted $22 billion.
For both Anduril’s prototypes, the company is testing a new system for digital night vision, which uses electronic sensors and algorithms to boost low levels of light. It’s been a promised technology for decades but has tended to work too slowly for practical use and produce grainy images. Anduril says it has found improvements over previous prototypes through techniques rooted in both new generative AI and older machine learning.
Much of the other hardware for both projects is being built by Meta, including the displays and the waveguides that send visuals to the user’s eye without blocking the view. That might be a surprise to anyone who knows the backstory: In 2017, Facebook (now Meta) ousted Anduril founder Palmer Luckey following an internal conflict involving his support for Donald Trump. The two are now back in the augmented-reality business together, while Mark Zuckerberg has also adopted a friendlier posture toward the second Trump administration.
For the Army initiative, this suite of smart glasses, night vision, and sensors will be attached to the helmets and other gear soldiers already wear, with a separate battery pack. The EagleEye version will instead incorporate the tech into the helmet itself. Even if the Army doesn’t prefer EagleEye in the end, Barnett says, Anduril will attempt to sell the system to foreign militaries.
Multiple challenges must still be overcome. Unlike Meta’s Ray-Ban glasses, the prototypes have to operate in an environment full of dust, explosions, and smoke. Adding the computing power and battery life they need also means more weight for soldiers already carrying upwards of 100 pounds. Then the technology has to work in environments without ubiquitous 5G cell connections; powerful computer vision and AI models will need to run locally on the device.
For the Army to want to buy it at scale, “it’s got to work, and it’s got to be pretty seamless,” Wong says. “It’s a high bar.”
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
When Google opens its doors tomorrow for its annual developer conference, I/O, it will do so as a clear third place in the foundation model race. A year ago, at Google I/O 2025, the situation looked very different: The company was still riding high from the launch of Gemini 2.5 Pro that March, and distinguishing among the top-tier large language models often felt like a subjective splitting of hairs.
But a foundation model’s reputation these days rests largely on its coding capabilities, and for months Google’s coding tools have been outgunned by Anthropic’s Claude Code and OpenAI’s Codex. Those systems are so dramatically superior to Google’s own offerings that the company has reportedly had to allow some engineers at DeepMind, its AI division, to use Claude for their work—lest they fall farther behind.
So when I arrive at the conference in Mountain View, California tomorrow, I’ll certainly be on the lookout for any efforts Google is making to claw its way back into frontrunner position. But I’m also eager to see new developments in areas where Google shapes the cutting edge, such as AI for science. The company’s moves there might receive less attention, but they will be no less consequential.
Here are three things I’ll be paying particular attention to over the next two days.
An attempted coding comeback
Google is taking its AI coding crisis seriously. According to reporting from The Information, there’s a new AI coding team at DeepMind. And the Los Angeles Times has reported that John Jumper, who shared a 2024 Nobel Prize in chemistry with DeepMind CEO Demis Hassabis for their work on the protein structure prediction software AlphaFold, is lending his talents to the efforts. I would be surprised if we don’t see a major new coding release at I/O, perhaps in the form of an update to the company’s Antigravity agentic coding platform.
That said, we shouldn’t expect anything transformative here. Googlers have access to models and products that are substantially ahead of those released to the public, yet they were still reportedly fighting over who got access to Claude Code last month. Unless the company has made astonishing progress since then, Google probably won’t make it back to the coding frontier in the next two days.
Science and health
Coding might be Google DeepMind’s weakness, but science is its conspicuous strength. It is the only frontier AI company to have earned a Nobel Prize. And as LLMs have come to dominate the AI-for-science landscape, Google has only solidified its lead. Last year, the company released multiple scientific AI tools, including the AI co-scientist, which formulates hypotheses and research plans in response to user questions and has been described as an “oracle” by one Stanford scientist, and AlphaEvolve, a system that iteratively discovers new solutions for mathematical and computational problems. If any new scientific tools are announced at I/O, they’ll be worth noting.
I’ll also be paying close attention to any moves Google makes in health and medicine. Google is doing some of the best research out there on LLM-basedhealth tools, but OpenAI has defined the health AI conversation since the release of ChatGPT Health in January. Google has announced that it will be making its AI-powered Health Coach publicly available tomorrow, but promotional material suggests that the tool is geared more toward providing advice on topics such as fitness and diet than to addressing users’ medical concerns. Is this another area where Google has fallen behind, or is the company exercising appropriate caution in a high-stakes domain?
The drama
While Google fans congregate down in Mountain View, roughly 30 miles north in Oakland the Elon Musk v. Sam Altman trial will be wrapping up. The past few months have seen more than their fair share of AI CEO drama—before the trial, the animosity between Altman and Anthropic CEO Dario Amodei took center stage as Anthropic and OpenAI worked to negotiate deals with the US Department of Defense. But DeepMind’s Hassabis has, for the most part, steered clear of such drama. He effectively presents himself as a Nobel Prize-winning nerd, and if he has written screeds about any of his peers, they haven’t been leaked to the press or appeared in legal discovery.
That’s not to say that Google is controversy free. Last month, a group of 600 employees, many of whom work for DeepMind, sent a letter to CEO Sundar Pichai protesting an impending DoD deal. Google signed that deal the next day. Hassabis, Pichai, and all the other big names will surely do their best to skirt these and other touchy subjects while on stage, but controversies will worm their way in regardless. It will be interesting to see whether Google can maintain its veneer of neutrality.
On Monday, the jury in Musk v. Altman dealt Elon Musk a major blow—reaching a unanimous advisory verdict that he had sued OpenAI too late and, as a result, his claims are barred by the applicable statutes of limitations. US District Judge Yvonne Gonzalez Rogers immediately accepted it.
Musk announced on X that he will be appealing the decision. “The judge & jury never actually ruled on the merits of the case, just on a calendar technicality,” he wrote.
OpenAI was cofounded by Musk and a group of researchers in 2015 as a nonprofit with a mission to develop AI for the benefit of humanity, unconstrained by a need to generate financial returns. Musk donated $38 million to the company during its early days, allegedly on the basis that OpenAI CEO Sam Altman and president Greg Brockman had promised to keep the company a nonprofit committed to the mission.
Musk brought two claims against OpenAI. First, he argued that Altman and Brockman breached the charitable trust he created through his donations by breaking their promise to keep the company a nonprofit and creating a for-profit subsidiary that ballooned over the years. Second, he argued that Altman and Brockman unjustly enriched themselves at Musk’s expense. He sued OpenAI in 2024.
Musk asked the court to unwind a 2025 restructuring that converted OpenAI’s for-profit subsidiary into a public benefit corporation and to remove Altman and Brockman from their roles.
OpenAI argued that the time for Musk to sue the company had run out before he brought the case. The statute of limitations on the breach of charitable trust claim is three years, while the statute of limitations on the unjust enrichment claim is two years. This means that Musk should have discovered, or had reason to discover, Altman and Brockman’s alleged breach of charitable trust no earlier than 2021 and their alleged unjust enrichment no earlier than 2022.
While Musk argued he discovered that Altman and Brockman had broken their promise only in 2022, OpenAI claimed that Musk had reason to think this well before 2021.
Musk told the jury that he has gone through “three phases” in his beliefs about OpenAI: In phase one, he was “enthusiastically supportive” of the company. In phase two, “I started to lose confidence that they were telling me the truth,” he said. In phase three, “I’m sure they’re looting the nonprofit.”
Here’s a deeper dive into a timeline of the events as testified in the trial. You can read my dispatches from all three weeks of the trial here and here and here.
2017: Musk proposes creating a for-profit subsidiary
In 2017, two years after OpenAI was founded, Musk and the other cofounders tried to create a for-profit subsidiary to raise enough capital to build artificial general intelligence—powerful AI that can compete with humans on most cognitive tasks. They fought a bitter power battle over who would get to control the entity. Musk also proposed merging OpenAI with his electric-car company, Tesla.
During the trial, OpenAI’s lawyers pressed Musk on these discussions, suggesting that Musk knew in 2017 about Altman and Brockman’s plans to pivot the company—even participating in such plans—and had reason to sue then.
“I was not opposed to there being a small for-profit that provides funding to the nonprofit,” Musk told the jury, “as long as the tail didn’t wag the dog.”
2019: OpenAI creates a for-profit subsidiary with capped profits
In 2019, OpenAI created a for-profit subsidiary, under which employees and investors would receive a capped return on their investment. At the same time, the company secured a $1 billion investment from Microsoft. OpenAI argued that Musk again had reason to sue the company then.
But Musk testified that he didn’t think the move was violating the nonprofit’s mission. “If you’ve got a capped-profit situation, it hasn’t violated the nonprofit’s goal,” Musk told the jury earlier in the trial. “There was no basis for me to file a lawsuit at that time.”
2020: Microsoft snags an exclusive license
In 2020, when Microsoft secured an exclusive license to OpenAI’s GPT-3 model, Musk posted on X: “This does seem like the opposite of open. OpenAI is essentially captured by Microsoft.” OpenAI once again argued that Musk had reason to sue then.
But Musk testified that after reading the post, Altman reassured him that “OpenAI was staying on the mission as a nonprofit.” Musk said although he was skeptical, he still had no reason to sue the company at that point.
2022: Microsoft prepares to invest $10 billion in OpenAI
It was only in 2022, Musk testified, that he discovered OpenAI had abandoned its nonprofit mission. At that time, 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,” Musk texted Altman after reading the news. “This is a bait and switch.”
Musk told the jury this was the moment that made him realize “the for-profit is the tail wagging the dog.” He thought Microsoft would give $10 billion only if it expected “a very big financial return.” He argued that this was the point he realized “OpenAI had become, for all intents and purposes, a for-profit company with a $20 billion valuation.”
“The 2023 deal was different,” Steven Molo, one of Musk’s lawyers, hammered home during his closing argument.
The jury sides with OpenAI
It was up to the jury to decide whether the evidence supported Musk’s claim that he first realized in 2023 that OpenAI was no longer a nonprofit committed to its mission. In the verdict announced today, they found Musk did in fact have reason to think that he was being misled by Altman and Brockman before 2021. They did not address whether he was in fact misled.
Courts often decide cases on procedural grounds like statutes of limitations when they can, because it can be the cleaner way to resolve a case than to grapple with its merits.
Musk has said he will appeal the decision to the Ninth Circuit Court of Appeals, a federal appellate court that reviews decisions from district courts in California and other states.
Google has finally published guidance for AI optimization. Yet “Optimizing your website for generative AI features on Google Search,” published May 15, offers nothing beyond traditional search engine optimization basics.
The guidelines apply to AI Overviews and AI Mode, but not necessarily to Gemini, Google’s standalone genAI platform, and certainly not to other models such as ChatGPT and Claude.
SEO
The guidelines start with confirming that traditional SEO remains as relevant as ever because (i) AI answers rely on search results and (ii) “fan-out” queries use actual human searches.
To Google, there’s no difference between “optimizing for AI” and “SEO,” stating:
From Google Search’s perspective, optimizing for generative AI search is optimizing for the search experience, and thus still SEO.
The guidelines list traditional SEO tactics, mainly:
Create original, helpful, people-first content with a unique point of view.
Structure content and pages for humans, not AI agents.
Use quality, helpful images and videos.
Ensure pages are crawlable by Google’s bots. (I use Search Console’s URL Inspection tool to confirm crawlability.)
Use the same semantic HTML structure for people and AI agents.
Minimize duplicate content.
Make sure content is visible with JavaScript disabled.
Emphasize user experience from multiple devices.
Ensure ecommerce product feeds are detailed and submitted to Merchant Center.
GEO Myths
Google’s guidelines address common myths for generative AI optimization, stating:
“Chunking” content (i.e., breaking it into short paragraphs for AI retrieval) is not required.
Writing in an “AI-friendly” way is not required.
“Seeking inauthentic mentions” may flag your site as spam (like traditional SEO). AI systems do evaluate brand and product mentions across the web, but, like Google, can tell real mentions from fake ones.
No “special” structured data, such as from Schema.org, is required, though it helps generate rich snippets in organic results.
Core Concepts
The guidelines emphasize two core concepts for optimizing AI on Google Search:
AI Overviews and AI Mode rely directly on organic search.
No additional tactics are required or recommended beyond traditional SEO.
Google suggests becoming familiar with the Universal Commerce Protocol, as AI agents will eventually not only search but also perform various actions on behalf of humans, such as booking a hotel or making a purchase.
Over the past few years, I’ve watched AI content creation tools rapidly gain adoption across the SEO/GEO industry. These tools offer the promise of leveraging AI to automate content creation, reduce headcount, cut costs, and scale output.
As someone who has spent the last decade helping companies recover from Google algorithm updates, my spidey senses started tingling the minute I heard the pitches for many of these tools. Even before AI was part of the conversation, Google already had a long history of reducing the visibility of automated content in its search results.
Despite recent advancements in the quality of AI outputs, I’ve remained skeptical that publishing AI-generated or AI-assisted content at scale can drive sustained performance in Google’s search results. This is especially true now, given how Google updated its ranking systems in recent years specifically to demote overly optimized, SEO-driven content.
Over the past several months, I have been monitoring more than 220 websites that were publicly identified, either by themselves or by their AI content vendors, as customers of various AI content creation, automation, and scaling platforms. These tools fully write articles, assist with writing them, or use AI automations and workflows to support content creation. Many of these tools also now focus on driving visibility, mentions, and citations in AI search responses (AEO/GEO).
I wanted to analyze what happens after the claims of big wins.
A consistent pattern emerged across the 220+ sites I’ve been monitoring, and I believe it is concerning enough to be worth writing about: it works, until it doesn’t.
Below, I will share some of the trends I am observing, plus a variety of common SEO/GEO approaches I believe may be causing declines in organic search (and consequently, AI search) visibility. As a reminder, what is dangerous for SEO can also be dangerous for AI search, largely because of RAG.
Methodology & Disclaimers
Before we dive in, it’s important to set the stage with my approach and provide some important disclaimers.
This analysis is based on third-party SEO measurement data: organic traffic estimates and organic page count time series data from Ahrefs, corroborated against the Sistrix Visibility Index data to confirm broader visibility patterns. Top-traffic URLs were identified using Ahrefs’ top-pages export. Where I describe URL patterns or percentage changes, I am quoting directly from these third-party tools as of May 2026.
The dataset covers more than 220 client domains tracked across the publicly published customer-stories pages of over a dozen AI content platforms. For many of these sites, I narrowed the analysis to a specific subfolder where the AI-assisted content had been published, either identified directly in the case study itself or inferred from a sharp increase in new pages around the time of the case study’s publication.
The analysis, conclusions, and recommendations throughout this piece reflect my own professional opinions based on more than a decade of helping companies recover from Google algorithm updates. Other SEO/GEO practitioners may disagree with my findings and approaches, and individual sites and strategies will always have their own context.
3 Important Disclaimers About This Data:
First, these are third-party estimates, not first-party analytics. They are well-validated tools in the SEO industry, but they are not perfect measurements of organic search performance.
Second, the traffic declines described here could reflect many factors, including but not limited to algorithmic adjustments by Google, on-site changes by the site operators themselves, off-site competitive dynamics, brand changes, acquisitions, seasonality, and changes to internal site architecture. I am not asserting that any AI content tool directly caused any traffic outcome described in this piece. I am describing a correlation observed across many listed sites that share similar content patterns and organic traffic trajectories.
Third, vendors and specific domains are deliberately not named here. The pattern is the story, not the specific actors. Any resemblance to a specific company, vendor, or case study is incidental to the broader pattern described.
What The Data Shows: Rapid Growth Before A Steep Decline
If there is one thing the data makes clear, it is this: scaling content production with AI is not a low-risk strategy for organic search. It can produce real short-term gains in both SEO and AI search (LLMs use search engines), but across this dataset, those gains have rarely held. In many cases, the eventual loss has exceeded the initial peak.
Across the group of 220+ sites and subfolders I analyzed:
54% lost 30% or more of their peak organic traffic.
39% lost 50% or more.
22% lost 75% or more.
Within those declines, a recurring trajectory appears: a rapid growth in organic pages over six to 12 months; an organic traffic peak within roughly three to six months of the content peak; and then a steep decline in traffic that erases most of the gain (and frequently drops below the prior baseline) within the following year.
Image Credit: Lily Ray
Most of these traffic drops took place after the case studies were published (which also makes me wonder whether the case studies themselves could be contributing to the declines). In the example below, the case study was published in January 2025, indicated by the the black star below:
Image Credit: Lily Ray
I am also continuously monitoring changes to organic page growth and organic traffic to these sites and subfolders over time. Looking at the updated data, a substantial number of these brands appear to have substantially reduced their content footprints in 2025 and 2026, often removing, redirecting, or 410’ing many of the same pages featured as success stories in published case studies. This could explain the recent drop in pages (yellow line) shown in the above screenshot (and potentially, the corresponding increase in organic search traffic).
In many cases, these case studies remain published to this day, but the pages they reference do not.
The Familiar Rank & Tank Playbook
When a site starts seeing traffic drops due to sitewide content quality issues, it’s rarely a gentle decline. As Glenn Gabe refers to it, a better label would be “Mount AI”: steep growth, followed by a similarly shaped drop-off in organic traffic, once Google’s systems have gathered enough signals to identify what is going on.
Below are several examples of case study sites that used AI to scale content creation and saw massive drops in organic traffic after their case studies were published:
Image Credit: Lily RayImage Credit: Lily RayImage Credit: Lily RayThis site’s decline started during the unconfirmed “self-promotional listicle Google update” in January 2026, which I also wrote about on my Substack (Image Credit: Lily Ray)
This pattern is consistent across industries, including cybersecurity, travel, marketing, SaaS, healthcare, B2B services, crypto, and consumer goods, and it shows up across vendors.
The shape of the line in the chart is similar to trajectories we have seen among many sites affected by Google’s algorithm updates in recent years. It is the same boom-bust cycle the SEO industry has watched repeatedly in different forms, accelerated this time by the speed at which AI tools have enabled site owners to scale content.
The SEO Industry Just Went Through This
What is hard to overstate is just how recently the SEO industry watched a near-identical cycle play out. Many SEOs and site owners are still licking their wounds from a brutal round of Google updates and new spam policies that obliterated many sites’ traffic a few years back.
In September 2023, Google launched the Helpful Content Update, the most aggressive crackdown it had done in years against content that, according to its announcement, “feels like it was created for search engines instead of people.”
Roughly six months later, in March 2024, it followed up with the longest core update in Google’s history, which Google states was designed to “reduce unhelpful, unoriginal content in search results by 45%.” Across two consecutive update cycles, Google’s stated target was the same thing: content produced at scale, regardless of whether the production method was human, AI, or a combination of both.
Alongside the March 2024 update, Google formalized a new spam policy called “Scaled Content Abuse,” explicitly naming the practice it was working to suppress: generating many pages to manipulate search rankings, regardless of authorship.
The SEO industry is still working through the collateral damage from those updates, including significant losses for many small publishers, some of whom were publishing original, human-written content but used excessive SEO frameworks that the updates likely flagged. The casualty list also included some publishers who had partnered with ad networks and other emerging tools offering AI content creation and scaling as a service.
Having spent hundreds of hours analyzing and presenting about those two major updates, I can say that the content I am seeing published with many of these new AI tools often looks and feels a lot like the exact type of content that was wiped off the map with these 2023 and 2024 Google updates.
8 Recurring Content Patterns That Are Risky For SEO And AI Search
So, what types of content am I seeing published by companies using AI tools to build articles that I believe are ultimately risky for SEO? I believe the answer lies in page templates that aim to influence SEO rankings, AI search responses, and/or citations in AI search, but are highly formulaic and easily repeatable by competitors.
What starts as a genuine approach to try to build helpful content (and score a mention/citation) ends up being an easily detectable footprint by Google when enough sites are publishing similar pages, and the index becomes flooded with tens or hundreds of thousands of these similar pages, which is easier than ever to do using AI.
This is exactly what Google means when it talks about writing for search engines, not humans.
Reviewing top-traffic URLs across the declining domains, eight distinct content templates appear repeatedly. Most sites seeing declines in the analysis use some combination of at least three or four. The most aggressive ones use all eight. Typically, affected sites also have hundreds or thousands of these articles, which amplifies the problem and generally leads to steeper traffic losses.
1. Comparison Pages At Scale
Pattern: /blog/[product-A]-vs-[product-B] published at scale across most reasonable head-to-head matchups in a category. Observed across the dataset for product-vs-product pairings, framework-vs-framework pairings, and, in at least one case, concept-vs-concept pairings unrelated to the publisher’s actual business.
2. The “What Is X” Glossary
Single-term, single-question pages designed to be cited by AI engines. Pattern: /resources/what-is-[term] or /glossary/[term]. Observed across the dataset, including programmatic glossaries scaled across multiple languages from a single source template. Scaling translations with AI and without human review can also frequently lead to sitewide content quality issues.
3. The “Best [X] For [Y]” Listicle
The most familiar AI-content template, with origins in the affiliate-content era. This pattern was observed across the dataset in both broad-category and narrow-niche variants.
4. The Self-Promotional Listicle
A variant of No. 3 in which the publisher is itself a competitor in the category being ranked, and frequently lists itself as the No. 1 best among competitors. These pages generally lack real evidence that the company genuinely tested all of the competitors in the list, which is recommended by Google for review pages.
I wrote about this “listicle” page template causing SEO/GEO issues in February 2026, when I found that many companies publishing dozens, hundreds, or even thousands of self-promotional listicles saw extreme traffic drops beginning on the same day (approximately Jan. 21, 2026). This pattern was observed across multiple sites in the dataset, most aggressively in B2B services.
5. The Competitor-Vs-Alternatives Page
Pattern: /blog/[competitor-brand]-alternatives, or, in the more programmatic form, dedicated landing pages built for every named competitor in a category. This approach was observed extensively across the dataset, including one case where the majority of a site’s top traffic pages were dedicated to individual competitor brand names.
6. Programmatic Location And Language Scaling
This is one of the oldest tricks in the SEO book, and one that I’ve seen sites get in trouble for with algorithm updates for at least 10 years. The approach: Use one template multiplied across every geography or language a search engine will index, with very little unique content per local landing page.
In many cases, the company publishing these pages often does not have real brick-and-mortar locations in each of the neighborhood/city/state pages they are targeting.
This page type was observed across the dataset including state-by-state content, country-by-country service pages, and the multilingual programmatic glossaries described above.
7. The FAQ Farm
Each page answers exactly one question. Pattern: /faq/[full-question]. Designed for extraction by AI engines: a clear question in the URL, the answer in the first paragraph, bullet points in the body, schema markup at the bottom.
The problem? This approach creates a lot of low-quality content and baggage for the site when implemented at scale. Scaling FAQs was also observed extensively across the dataset, including in industries where the templated tone was a noticeable mismatch with the publisher’s brand context.
Publishing off-topic content, with no apparent connection to the publisher’s actual business, at high volumes, is one of the fastest ways to get in trouble with search engine algorithms. This was also a huge problem during the Helpful Content Update and March 2024 Core Updates, when many sites were experimenting with publishing off-topic content, like funny quotes, jokes, baby names, horoscopes, and other high-volume articles that weren’t actually topically relevant for the publisher.
This method was used across multiple sites in the dataset, including pieces on entertainment topics on a services platform, lists of names and jokes, social-media memes on B2B websites, and historical or biographical content on business-focused sites.
A large B2B company’s blog subfolder hit by the unconfirmed late-January 2026 Google update. (Image Credit: Lily Ray)
Google did not announce or confirm an update by name in January 2026, but at least 40 sites I identified saw a negative trend beginning around Jan. 20, 2026. In many cases, the impact was isolated to the company’s blog or other subfolder containing a lot of new SEO-driven content. My analysis found that some of these companies were scaling dozens, hundreds, or even thousands of these self-promoting listicles, in which they named their own company the No. 1 best when compared to competitors.
I suspect this adjustment on Google’s end was just the start of Google (and likely the LLM providers building on top of search) beginning to demote this type of content in search results, and it appears that the impact was greater than just the listicles themselves. For affected sites, the entire blog or subfolder containing these articles often also saw declines. In other cases, the impact was carried over across the full domain.
How To Use AI Content Tools Safely
I do believe there is a way to use AI content tools safely, and a way for these tools to support the creation of high-quality content. The tools themselves are not the problem, but the implementation can be. I believe these tools should be used and overseen by experienced SEO professionals who understand the landscape of content approaches that Google has grown extremely sophisticated at penalizing and demoting over the past 10+ years. The problem often stems from a “set it and forget it” approach, or when the goal is to scale as many pages as quickly as possible without human review.
Using AI content tools for research, organization, content briefs, pulling in proprietary company data and insights, and more can be invaluable for speeding up the content creation process. But when articles are simply published “for SEO/GEO” without consideration of the risks involved with search engine ranking systems, the well-intentioned content can actually backfire for both SEO and AI search.
To perform well, I recommend that any AI-assisted content should still demonstrate E-E-A-T, add original or unique information above and beyond what is offered by competing pages (information gain), and consider being transparent about the use of AI to create the content (which is recommended by Google).
The Bottom Line
If there is one takeaway from monitoring these 220+ sites over the past several months, it’s that the playbooks being sold as “AI-first SEO” or “GEO-optimized content at scale” look remarkably similar to the playbooks that got sites flattened by the Helpful Content Update and the March 2024 Core Update. The packaging is new, but the pattern is not.
Across the dataset, the brands still growing are generally the ones whose content does not match the eight templates above. Many brands that scaled into those templates are the ones now removing pages, redirecting subfolders, and taking other steps to try to mitigate recent losses in traffic.
If you’re currently evaluating an AI content vendor, or running a program in-house, here are a few practical questions I think are worth asking before publishing another page:
Does this page actually exist because a real customer or reader needs it, or because a search engine or LLM might cite it?
Could a competitor publish a near-identical version of this page tomorrow using the same prompt?
Would I be comfortable if Google, a journalist, or my own customers saw the full list of URLs in this subfolder?
Is the article inherently biased, and if so, is the page transparent with users about those biases?
Is there any first-party data, expertise, or original perspective on this page that isn’t available on the first ten results already ranking for the query?
None of this means AI content tools are unusable. They can be genuinely useful for research, briefs, internal data synthesis, and accelerating workflows where a human expert is still in the loop. The trouble starts when the goal becomes volume, or when the people closest to the content stop reviewing what is going out the door.
The SEO industry has already lived through this cycle once in the last few years. The sites that came out of it best were the ones that prioritized quality, originality, and topical focus over scale. I expect the same to be true of this cycle, and I’ll keep tracking the data as it plays out.
Do you know who your audience is and what they want?
Over the last 20 years or so, we used to rely almost purely on data to answer that question. But as cookie tracking and user signals declined and analytics shifted toward sampling (what we refer to as the “signal-loss era”), we’ve lost some of that superpower. On top of this, we’ve handed over control to hyper-personalized platforms with “black box” targeting algorithms to find our audiences, leaving us less able to truly understand what is going on. And in doing so, we have lost track of the user.
In a way, the abundance of data made us complacent: “Data-informed” became the standard, while “user-informed” strategies progressively faded.
The problem with that over-reliance on data is that it made it “okay” to forget we are fundamentally communicating with humans and creating connections. We focused on the outcome and lost the drive to know who we’re connecting with and what leads us to acquire certain users or lose some.
And while signal loss and AI targeting might be perceived as a constraint, in reality, it is actually a great opportunity to go back to basics of marketing. It means we can focus on really understanding the user as a person, and not as trace fragments of data they leave in our web analytics.
Ultimately, getting to know them means we can serve them better – and find stronger, long-lasting ways to connect.
The Opportunity: Understanding Users And How We Reach Them
Even if we still had the data we had before, would it even be enough? I don’t think so, because it assumes user behavior is limited to what we can observe. In reality, behavior is shaped by a series of small, automatic decisions that happen below the surface, often driving outcomes before any action is even initiated – let alone tracked.
On top of this, when we talk about “understanding the user,” this is often reduced to understanding their needs and a rough demographic, but that’s only part of the picture. Users are people, with unique needs and patterns of thoughts at every stage of their consideration journey.
Now more than ever, we need to truly know who we are talking to and interacting with. What makes them favor us over a competitor? What media and channels are they using so we can reach them? What emotional triggers are really relevant to them? What is important to them at every stage of the journey? Only after answering these questions can we claim to have at least scratched the surface.
I’ve said before that human decision-making is inherently imperfect, shaped by cognitive biases and heuristics that help us navigate complexity without analyzing every option in detail. And that’s the reason why knowing what they want is often not enough to get the full picture – you need to know how they make decisions too.
When we fully understand the user, we can shape our approach ahead of outcomes, inform testing and platform targeting, and even anticipate results before execution.
A Practical Alternative To Cookie-Based Strategies: The R.E.M. Framework
To make sure you can reach the right audience, even when data is scarce and tracking unreliable, you should work with three simple things to aim for: Being Relevant, Everywhere, and Memorable in your strategy, from creatives, messaging, and channel choices.
This is what I call the R.E.M. Framework.
Image by author, April 2026
1. Be Relevant (And Relatable)
Relevancy is the first gateway to attention. In a world saturated with competing stimuli, it’s one of the primary filters the brain uses to decide what deserves focus.
Think about it: You might be having a great conversation with a friend in a group full of other people talking, and pay attention only to what they say. And yet, if your name is mentioned by someone else in a conversation you are not listening to, it’s very likely that you will automatically start paying attention to that instead.
This is what is commonly referred to as “the cocktail party effect,” a great example of how stimuli that are relevant to our personal experience, context, and goals can automatically capture our attention even when we are engaged in another task – something that happens consistently on social media, for example.
Today, we often refer to attention as “marketing’s primary currency,” and for a good reason. In a market so saturated, we only have a few seconds to pique our users’ interest before they move on to the next thing. And any content that won’t result in early engagement is likely to be dropped by the algorithm, which won’t serve it to other users as deemed not a good fit for our audience.
This is known by the industry as “the three-second rule,” and might in fact even be optimistic for newer platforms where short-form video prevails, like TikTok videos and Instagram reels. Short-form videos tend to make people forget what they came to the platform for in the first place much faster than long-form videos, and it’s exceptionally easy to lose a viewer on these formats if the hook isn’t instantly strong enough.
But in order to understand how to capture interest early, we need to take a step back and understand how attention works.
As humans, we are consistently exposed to a lot of stimuli at the same time, and we don’t have the cognitive resources to process each one of them, so we select some of them for further processing while ignoring others. We do so via a process called “selective attention” that can be driven by internal motivations (“endogenous orienting”) or external drivers (“exogenous orienting”). In other words, we tend to allocate attention based on our own goals (for example, when we have a deadline and we need to focus on a deliverable) or on the perceptual features of the objects around us (for example, the sound of the phone ringing or a stand-out word in a sentence).
That means that we have two ways to engage someone’s attention: by connecting with their goals, or presenting them with something that stands out in a sea of other similar things.
We can argue that relevancy sits in between these processes and can engage them both. As a matter of fact, when we are researching something, we are already deciding to filter out all the results that seem relevant to our own goal. But it works the other way too: Something relevant to our needs, goals, and context will jump out when we are doomscrolling on socials, even when we are not engaged in a search.
So relevancy is a sort of “catch-all” for attention.
How do you make sure you are immediately relevant?
By identifying what your audience needs, and leading with the solution in the hook. Don’t waste time with obscure messaging or secondary angles that you can elaborate on once you’ve anchored attention.
Strong tests and creatives are the ones that don’t focus on the business, but focus on the user and what they are trying to solve instead. And hyper-personalized platforms make this even more layered. Make the audience see themselves in what you offer, and you’ll shorten the time it takes for them to recognize you as the right choice.
2. Be Everywhere (Your Audience Is)
But can you be relevant to everyone? Of course not. So it’s imperative you understand your audience and their motivations to capture existing demand. And beyond that, you need to be present where they can find you, with the message they’re looking for in that moment.
This is one of the main challenges, now that journeys are so scattered across different platforms and search experiences. There are so many channels people discover us by, that it’s virtually impossible to track where certain journeys even start from. We might get a user from an LLM query, or a social post, or a Google search. Most likely, it’s all of them. A consideration journey is not linear, and it’s in fact the result of a continuous loop of discovery and evaluation, something we know now as “The Messy Middle.” Even the best attribution models rarely capture this.
The “Messy Middle” from Google’s 2020 consumer behavior report. (Screenshot by author, April 2026)
So, the solution is to work cross-functionally to cast a wide net across different channels, because visibility builds trust. “Out of sight, out of mind”: Our brain forms associations that strengthen with repeated exposure, and drops whatever is not used. If you’re consistently present where your audience is, with relevant content, you create the perception that you are indeed everywhere – without actually having to be.
And that matters, because repeated exposure is part of how we cast a choice in a sea of options. We call this “availability heuristic,” a decision-making shortcut that makes us favor what comes to mind easily: what we’ve seen often, recently, or remember clearly. Think about recommending a movie. You’re far more likely to mention something you’ve just watched, or keep seeing suggested, than something from years ago.
So, while relevance gets you noticed, presence keeps you top of mind. That means that when someone is ready to act, you’re already part of the consideration set, often before they even start a search.
Of course, going omnichannel is a beast in itself. Creatives and messages in one platform won’t work on another – you still need to test and iterate – but if you do it from a customer lens, your work is much simpler, and the benefits are two-fold: You can target different moments in the journey and stay top of mind.
But how do you prioritize channels when resources are limited?
You can rely on demographic research, personas, and early discovery data to establish a rough baseline, although that only gets you so far. Mapping who they are doesn’t tell you what they do when they make a choice, and how those behaviors shift across the journey. That’s the piece you have to find out for yourself: How do they make decisions? Who do they rely on for information, and where do they go to find it? And just as importantly, where are they when they’re not actively looking, and how can you meet them there?
And this is where personas fall short. They might tell you what people need and who they are, but not how they feel when making a decision. Often, what gets labeled as a bad strategy is simply incomplete research.
To really understand your audience, you need all of this information, which brings us to the next part.
3. Be Memorable
Being memorable is the one variable that still carries the most weight – yet is the hardest one to achieve. Why? Because it relies on creating a meaningful connection with the audience. And what that connection looks like can vary a lot across different individuals.
The general playbook to produce an emotion in marketing has often relied on the assumption that we share the same set of basic reactions, something that is based on Paul Ekman’s studies isolating fear, anger, happiness, surprise, disgust, and sadness as the “six basic emotions.”
And while it is true that some of these can be shared, the reality of the human emotional experience is much more nuanced and is often modulated by personal context, expectation, cultural values, and much more.
While attention works similarly across different individuals, memorability relies on personal context, values, and experiences. Think about an ad that stayed with you. What was the reason why you remember it so well? Chances are, it is because of the way it made you feel. Another reader of this article will have chosen a completely different ad.
Some brands, messages, or creatives stay with us because they elicit an emotional reaction. They make us laugh, they trigger nostalgia, sometimes they outrage us. But they all make us feel a certain way. And even when we choose employing rules of thumb like going for what we already know (“familiarity bias”) or what our peers suggest (“social proof”), it’s often because these are choices that are validated and make us feel safe.
We often hear that people make decisions emotionally, then they justify them rationally. This idea is reflected in early theories like Damasio’s “Somatic Marker Hypothesis,” which proposes that emotional signals influence decision-making, and is supported by neurophysiological evidence showing that physiological arousal varies between liked and disliked brands, pointing to the involvement of emotional processes in brand evaluation.
Electrodermal activity to liked versus disliked brands. Disliked brands elicited significantly higher physiological arousal than liked brands, illustrating that emotional intensity can vary independently of stated preference (Walla et al., 2011).
And that’s important, because the way we feel about a brand determines not only our perceptions but it’s pervasive of the entire experience with them, including trust and willingness to engage with their messaging and offer. We remember experiences for how they made us feel; we connect with some brands and ethos, and we disconnect wildly from some. Once you gain that memorability with your audience, you have an easier time retaining it – as well as guiding them to choose you.
What does this mean for you? Get acquainted not only with what your user needs or what is most likely to catch their eye, but with their personal and cultural context, how they feel, and what their expectations and values are – because these are all aspects that influence the relationship between brand and consumer. A genuine connection will make the user bypass any intermediate evaluation, and make you stand out from competitors, looping us back to our R – the relevancy you aim for in the first step of this framework.
Takeaways
Catching attention isn’t the only metric of success in the signal loss and hyper-personalization era. You need to be everywhere, and to stay top of mind when your audience is looking for the solution you can offer. So it’s imperative you know your users, their motivations, and their emotional states to capture existing demand and connect with them, wherever they are.
Easy, right?
Not really, but here are some starting points:
Find what your audience needs by collating data that goes beyond search, and takes into account customer service logs, user interviews, and social scraping (both for your brands and your competitors), so that you can capture both the pre-purchase and post-purchase journeys. Use that data to inform your USP and messaging in your test and creatives. Make it all about them, not you.
Don’t take channels for granted, or ignore them just because they’re not useful to your immediate key performance indicators (KPIs). Visibility is often the result of compound actions and cross-functional collaboration. Map out your discoverability across different channels, content formats, and ways to consume content, so that you can target different moments in your audience’s journey. Let this be your guiding light when you pick your battles.
Get to know your audience at a granular level: What do they feel when they search? What are their values? What are their expectations? If they know us, how do they feel about us? Use those emotional drivers to understand what creatives, messaging, and format might be best to use as a gateway to create a meaningful connection.
In summary, start with finding your audience, learn how they decide and understand their underlying needs; all of this will inform your unique selling proposition (USP) and product value proposition, your messaging and creatives, as well as your distribution channels and the choice of formats.
It’s time we go beyond personas and start looking at the real people behind the screen.
On May 6, 2026, Anthropic CEO Dario Amodei walked out onto a stage at his company’s developer conference in San Francisco and said something you almost never hear from a tech CEO: Growth is the problem.
Anthropic had planned for a 10-fold expansion. What it got was 80-fold growth in Q1, on an annualized basis. Revenue has crossed $30 billion, up from $9 billion at the end of 2025. The company is weighing a funding round at a reported $900 billion valuation – which, if it closes at those terms, would likely surpass OpenAI’s most recent post-money valuation of $852 billion. And yet, as Amodei told the audience that day, “I hope that 80-times growth doesn’t continue because that’s just crazy and it’s too hard to handle.”
He wasn’t being falsely modest. Demand for Claude has already created what Anthropic described as “inevitable strain on our infrastructure,” hitting reliability and performance during peak hours. Hours before Amodei took the stage, the company announced a deal with SpaceX – which, earlier this year, merged with xAI, the company behind the Grok AI models, now rebranded SpaceXAI – to take over the entire compute capacity at the Colossus 1 data center in Memphis, giving it access to more than 300 megawatts of capacity and 220,000 Nvidia GPUs.
The detail worth noting: xAI and Anthropic are direct competitors at the model layer. The fact that Grok’s infrastructure is now running Claude’s workloads is the clearest signal yet of how constrained high-end compute capacity has become. That’s a bridge built under emergency conditions, not a planned expansion.
So, why should SEO professionals, content marketers, and entrepreneurs care about Anthropic’s infrastructure problems? Because this story is actually about something much bigger than one company scrambling for server capacity.
This Has Happened Before
In 2011, I read I’m Feeling Lucky: The Confessions of Google Employee Number 59 by Douglas Edwards, who was Google’s first director of marketing and brand management. That’s when I learned how close Google came to buckling under its own success in the early days.
In late 1999, Edwards wrote, “Google began accelerating its climb to market domination. The media started whispering about the first search engine that actually worked, and users began telling their friends to give Google a try. More users meant more queries, and that meant more machines.” Then the machines became impossible to get. A global shortage of RAM hit at the worst possible moment, and Google’s system, as Edwards put it, “started wheezing asthmatically.”
That infrastructure crisis drove decisions that shaped the web for the next two decades. Google started filtering duplicate content – even non-malicious versions like printer-friendly pages – because every redundant page required adding hardware without improving user experience. The constraint shaped the product. The product shaped SEO.
Anthropic’s compute crisis is the same dynamic, playing out 25 years later at a different scale. The question isn’t whether they’ll solve it. They will. The question is what decisions they’ll make under pressure, and how those decisions will reshape the products that millions of marketers depend on.
What The Data Actually Shows
When I went looking for what this growth moment means for practitioners, I found the headlines and the data pointing in surprisingly different directions.
Rand Fishkin recently shared findings from the Datos State of Search Q1 2026 report, which draws on clickstream data from tens of millions of real devices. His summary was pointed: AI is disrupting traditional search – no, the data doesn’t show that. AI tools are growing faster than traditional search in absolute terms – no, traditional search is still outpacing AI tool growth on an absolute basis. AI Mode in Google is huge – no, it’s still under 0.2% share, growing but still small. ChatGPT is pulling away from Claude – actually, no. Claude is closing the gap, Gemini holds the number two spot and is growing, and ChatGPT has plateaued since September 2025.
These are not the narratives that get clicks. They are, however, what the data says.
At the same time, I went to Think with Google and worked through its report, “The Rise of the Super-Empowered Consumer,” which tells a different part of the same story. Some of what’s in there deserves more attention than it’s getting. AI Overviews is used by over 2 billion people, and users report making decisions faster and with more confidence. AI Mode now has over 75 million daily active users, with nearly 1 in 6 queries using voice or images. Queries in AI Mode run three times longer than traditional searches, and sessions are becoming more conversational. Google Lens handles over 25 billion visual searches every month. Shoppers are 2.3 times more likely to use Google Search than ChatGPT for purchase decisions, and 40% of consumers who use Google AI Mode for shopping say they’re using ChatGPT less as a result.
Two different pictures of the same moment. Both accurate. Neither is complete on its own.
The Takeaway For Practitioners
The AI industry is generating a firehose of information, and most of it gets consumed at the headline level. A company announces 80-fold growth, and people read it as a story about AI winning. Fishkin publishes data showing traditional search still outpacing AI tools in absolute volume, and people read it as a story about AI losing. Google publishes a consumer report showing AI Overviews reaching 2 billion users, and people read it as confirmation that SEO is dead.
None of those readings are wrong. All of them are incomplete.
The strategic value isn’t in reading the news. It’s in following the thread further – downloading the Datos report, working through the Google consumer study, checking the CNBC article against the Cryptopolitan analysis of what the Anthropic-SpaceX deal actually signals about the infrastructure war playing out between the major AI companies.
Google’s early infrastructure crisis produced lasting decisions about duplicate content that practitioners are still navigating. Anthropic’s current one will produce decisions about rate limits, model availability, enterprise pricing, and compute allocation that will shape how Claude-powered tools perform for the marketers and developers using them. Those decisions are already being made.
A recent Anthropic experiment may offer a glimpse at how agentic commerce could work in two-sided marketplaces, where buyers and sellers negotiate prices.
Called “Project Deal,” the test compared two large language models to determine whether stronger AI systems would gain an advantage in autonomous marketplaces. While the experiment does not necessarily predict how AI agents would negotiate with humans in real-world commerce, it revealed both model differences and user blindness to poorer economic outcomes.
Agentic commerce could take many forms, including something dynamic like Project Deal.
Project Deal
Anthropic conducted the experiment in an internal Slack employee marketplace. Sixty-nine staffers allowed Claude AI agents to negotiate the purchase and sale of real items on their behalf, including books, sporting goods, and household products.
Once the marketplace opened, the agents operated autonomously, proposing prices, responding to counteroffers, and closing deals without human approval.
Across four separate marketplace runs, the agents completed 186 transactions totaling about $4,000. A subsequent regression study yielded 782 transactions with values above $15,000.
Anthropic intentionally varied the capability of the participating AI models, using the more advanced Opus for some employees and the smaller Haiku for others.
The company noted that the experiment’s design reflects growing interest among economists and AI researchers in what some call “agentic interactions,” in which AI systems move beyond information retrieval and begin acting as economic participants.
Economic Advantage
During the Project Deal test, Anthropic found that the stronger Opus model generally achieved better economic outcomes than the smaller Haiku model, but not necessarily because it completed significantly more transactions.
Instead, the differences appeared primarily in negotiation performance.
According to Anthropic’s data, Opus agents earned $2.68 more per transaction when selling items and paid $2.45 less when buying items. The pricing differences were relatively small in dollar terms, but meaningful relative to the experiment’s median transaction price of about $12.
Anthropic also conducted a narrower paired-item comparison. Looking only at identical items sold by different models across runs, Opus sellers earned an average of $3.64 more for the same item than sellers represented by the weaker Haiku model.
In other words, more capable models could be a significant competitive advantage in the marketplace. The company was careful to mention that Project Deal “doesn’t reflect how we think agents should be deployed in the real world.”
Fixed Price
Does the outcome suggest that online sellers deploying “better” AI seller agents could earn significantly more in some marketplaces?
The answer might be no.
Today, most ecommerce transactions are fixed-price purchases rather than negotiations. So agentic commerce, wherein AI agents shop on behalf of folks, might not be applicable.
On the other hand, many two-sided marketplaces still include elements of bargaining, price optimization, or dynamic pricing.
Examples include:
eBay offers,
Facebook Marketplace negotiations,
Craigslist transactions,
Wholesale sourcing,
Advertising auctions,
Freight marketplaces,
Procurement platforms.
In these kinds of exchanges, a relatively stronger AI system could theoretically produce measurable economic advantages over weaker systems or human negotiators.
If agentic exchanges expand, model capability itself could become a form of competitive advantage similar to logistics efficiency, marketplace data access, or advertising sophistication.
Dollars not Deals
While the economic advantage gained from AI agents in the Project Deal experiment was significant, it was also nuanced. For example, the statistical differences between Opus and Haiku in deal completion were relatively small, according to Anthropic.
Both models, if you will, could close the sale. This is worth mentioning because, in the near future, merchants may evaluate AI agents much like they evaluate advertising campaigns or marketplace performance today. Instead of focusing solely on completed transactions, sellers could begin measuring:
Average negotiated selling price,
Procurement savings,
Margin improvement,
Pricing consistency,
Revenue per transaction.
User Blindness
Perhaps the most surprising finding from Project Deal was not the difference between the AI models themselves, but how humans responded to those differences.
The human participants represented by the weaker Haiku model often reported levels of satisfaction and fairness similar to those using the stronger Opus model, despite achieving measurably worse economic outcomes, according to Anthropic.
In other words, many folks did not recognize that their AI agent had negotiated less effectively on their behalf.
That finding could eventually become important in ecommerce and marketplace environments where AI agents act semi-autonomously for buyers or sellers.
For example, a merchant deploying an AI procurement agent might not immediately recognize that a weaker model consistently pays slightly higher supplier costs. Similarly, a marketplace seller using a less capable negotiation agent might unknowingly accept systematically worse pricing outcomes.
Over time, even relatively small pricing disadvantages could compound across thousands of transactions, advertising purchases, or sourcing agreements.
Dealmakers
If nothing else, Anthropic’s Project Deal demonstrated that AI agents could buy and sell in a narrowly focused marketplace.
That small success should certainly have the industry thinking about what happens when AI agents are, in fact, buying and selling for folks.
On March 20, 2026, Google quietly added a new entry to its official list of web fetchers. Not a crawler. Not a training bot. An agent.
Google-Agent is the user agent string for AI systems running on Google infrastructure that browse websites on behalf of users. When someone asks an AI assistant to research a product, fill out a form, or compare options across websites, Google-Agent is the thing that actually visits the page. Project Mariner, Google’s experimental AI browsing tool, is the first product using it.
This is not Googlebot. Googlebot crawls the web continuously, indexing pages for search. Google-Agent only shows up when a human asks it to. That distinction changes everything about how it operates.
Robots.txt Does Not Apply
Google classifies Google-Agent as a user-triggered fetcher. The category includes tools like Google Read Aloud (text-to-speech), NotebookLM (document analysis), and Feedfetcher (RSS). All of them share one property: a human initiated the request. Google’s position is that user-triggered fetchers “generally ignore robots.txt rules” because the fetch was requested by a person.
The logic: If you type a URL into Chrome, the browser fetches the page regardless of what robots.txt says. Google-Agent operates on the same principle. The agent is the user’s proxy, not an autonomous crawler.
This is a meaningful departure from how OpenAI and Anthropic handle similar traffic. ChatGPT-User and Claude-User both function as user-triggered fetchers, but they respect robots.txt directives. If you block ChatGPT-User in robots.txt, ChatGPT won’t fetch your page when a user asks it to browse. Google made a different call.
Website owners who relied on robots.txt as a universal access control mechanism now have a gap. If you need to restrict access from Google-Agent, you’ll need server-side authentication or access controls. The same tools you’d use to block a human visitor.
Cryptographic Identity: Web Bot Auth
The more significant development is buried in a single line of Google’s documentation: Google-Agent is experimenting with the web-bot-auth protocol using the identity https://agent.bot.goog.
Web Bot Auth is an IETF draft standard that works like a digital passport for bots. Each agent holds a private key, publishes its public key in a directory, and cryptographically signs every HTTP request. The website verifies the signature and knows, with cryptographic certainty, that the visitor is who it claims to be.
User agent strings can be spoofed by anyone. Web Bot Auth cannot. Google adopting this protocol, even experimentally, signals where agent identity is heading. Akamai, Cloudflare, and Amazon (AgentCore Browser) already support it. Google brings the critical mass.
This matters because the web is about to have an identity problem. As agent traffic increases, websites need to distinguish between legitimate AI agents acting on behalf of real users and scrapers pretending to be agents. IP verification helps, but cryptographic signatures scale better and are harder to fake.
What This Means For Your Website
Google-Agent creates a three-tier visitor model for the web:
Human visitors browsing directly.
Crawlers indexing content for search and training (Googlebot, GPTBot, Google-Extended).
Agents acting on behalf of specific humans in real time (Google-Agent, ChatGPT-User, Claude-User).
Each tier has different access rules, different intentions, and different expectations. A crawler wants to index your content. An agent wants to complete a task. It might be reading a product page, comparing prices, filling out a contact form, or booking an appointment.
Here’s what to do now:
Monitor your logs. Google-Agent identifies itself with a user agent string containing compatible; Google-Agent. Google publishes IP ranges for verification. Start tracking how often agents visit, which pages they hit, and what they attempt to do.
Check your CDN and firewall rules. If your security tools aggressively block non-browser traffic, Google-Agent may be getting rejected before it reaches your server. Verify that Google’s published IP ranges are permitted.
Test your forms and flows. Google-Agent can submit forms and navigate multi-step processes. If your checkout, booking, or contact forms rely on JavaScript patterns that confuse automated systems, agent visitors will fail silently. Semantic HTML and clear labels remain the foundation.
Accept that robots.txt is no longer a complete access control tool. For content you genuinely need to restrict, use authentication. robots.txt was designed for crawlers. The agent era needs different boundaries.
The Hybrid Web Isn’t Coming. It’s Logged
A year ago, the idea that AI agents would browse websites alongside humans was a conference talk prediction. Today, it has a user agent string, published IP ranges, a cryptographic identity protocol, and an entry in Google’s official documentation.
The web didn’t split into human and machine. It merged. Every page you publish now serves both audiences simultaneously, and Google just made it possible to see exactly when the non-human audience shows up.
