Jun 7 2026
Google’s New Guidance Claims Authority Over SEO, Tools, And AEO/GEO via @sejournal, @martinibuster

Google has published new guidance that canonicalizes itself as the single source of objective truth for SEO practices, including for AI SEO. The new guidance, published on Google Search Central, is Google’s strongest assertion of itself as the official source of information SEO best practices and SEO tools.

The new guidance affects:

  • Third-party SEO resources.
  • Third-party SEO tools.
  • Third-party SEO services.
  • Third-party data providers.

The effect of the new guidance is to assert Google as the authoritative source of resources, tools, SEO information, and SEO data.

The four main points of the new documentation are:

  1. Google Says It Is The Authority On SEO Advice
  2. Google Claims Authority Over AI Search Optimization
  3. Google Distances Itself From Third Party SEO Tools
  4. Google’s Recommends Itself For SEO Tools

Google Says It Is The Authority On SEO Advice

Google’s new guidance is specifically about third-party SEO tools and third-party SEO advice. It expressly asserts its own guidelines as the canonical source of truth about SEO and for the nascent practice of AI optimization.

The new guidelines insist on Google as the objective truth about SEO:

“While some advice is helpful, others may misinterpret or make claims about what “Google says” or how Google ranking systems work. In general, good advice either qualifies their claims as opinion based on data or experience, or backs up their claims by citing official Google Search guidance.

We recommend carefully evaluating any advice you might be considering implementing against our official SEO guidance, including our guidance on optimizing for generative AI, and making your own informed decisions.”

Those statements assert Google’s own documentation as the reference point for evaluating whether SEO advice is credible and worth implementing. That’s always been a good practice. What’s unusual is how strongly the new guidance asserts Google’s primacy over all SEO information.

Google Claims Authoritativeness Over AI SEO

The guidance applies the same canonicalization of objective truth to AI search optimization advice, by asserting Google’s advice as authoritative for AEO and GEO, as well as SEO in general.

Google specifically references advice related to AI optimization, specifically mentioning AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization).

Google’s new guidance essentially divides SEO information into two categories:

  1. Third-party SEO opinion based on data or experience.
  2. Google’s own guidelines and recommendations.

After setting up the us versus them comparison, it follows by strongly recommending its own guidance as the source of truth by which any other advice should be weighed.

The new guidance explains:

“There’s plenty of third-party SEO advice on the internet related to SEO, search listings, and AI experiences (sometimes called AEO for “answer engine optimization” or GEO for “generative engine optimization”). While some advice is helpful, others may misinterpret or make claims about what “Google says” or how Google ranking systems work.

In general, good advice either qualifies their claims as opinion based on data or experience, or backs up their claims by citing official Google Search guidance.

We recommend carefully evaluating any advice you might be considering implementing against our official SEO guidance, including our guidance on optimizing for generative AI, and making your own informed decisions.”

Google Distances Itself From Third-Party SEO Tools

The strongest language in the document is directed at third-party SEO tools and services that imply some level of Google approval.

Google lists examples of third-party SEO services, including sitemap tools, indexing tools, content generation services, ranking advice services, and tools that promise improvements for AEO and GEO.

It then states:

“Some of these services may be helpful in your work, while others may make claims or imply that what they do is somehow ‘acceptable’ or ‘approved’ by Google Search.”

Google follows that statement with a warning:

“Google doesn’t evaluate third-party services, so be wary of such claims and those making them.”

The guidance stops short of criticizing SEO tools in general. In fact, Google acknowledges that some may be useful. But it clearly distances itself from vendors and services that invoke Google’s name to imply endorsement, approval, or validation.

Google also reminds businesses that using a tool is not a shortcut to better rankings:

“Keep in mind that using a service or tool doesn’t guarantee ranking success.”

Google Says SEO Tool Data Is Not Google Data

Google also addresses what it describes as a common misunderstanding about SEO tool data.

According to the guidance:

“Some third-party services provide data that some users of those tools misinterpret as somehow being from Google.”

Google then explicitly states:

“Third-party tools don’t have access to our internal ranking data.”

The guidance continues:

“They can’t guarantee performance. Any predictions are their own and like predictions generally, may not happen.”

Google’s position is that SEO tool forecasts, scores, and performance predictions should not be confused with Google’s own ranking data or internal systems. This is the strongest distancing that Google has put between itself and third-party data providers.

Google Recommends Itself For SEO Tools

After warning businesses about third-party claims, third-party predictions, and third-party data sources, Google recommends using its own platform, Search Console.

Google states:

“Whether you use a third-party tool or not, we strongly encourage using our first-party tool, Google Search Console, which provides you with key information and data directly from Google Search itself.”

That recommendation ends the new guidance, which is expressly designed to assert Google as the ground truth about SEO, AEO, GEO, and SEO tools. The question to ask now is: Why is Google doing this?

  • Is there a new algorithm coming that will crack down harder on sites that practice SEO that diverges from Google’s own?
  • Or is Google just trying to assert its own information as the canonical source of SEO truth?

Google explicitly advises businesses to “think critically” about using third-party tools and third-party services (SEOs). The phrase “think critically” means to not take things at face value, to analyze and question the information.

Looked at another way, it’s hard to ignore that this is Google’s strongest assertion of authoritativeness for SEO information.

How do you feel about Google’s new guidance? Your opinion matters.

Featured Image by Shutterstock/rasskazov

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Jun 6 2026
Google Gives Sites AI Search Opt-Out, But Not The Data To Use It via @sejournal, @MattGSouthern

Some websites can now opt out of Google’s AI search features without losing their place in standard search results. The UK’s Competition and Markets Authority imposed a conduct requirement this week, and Google began testing its own Search Console toggle the same day.

The real question is whether there’s enough information to make a decision. Google’s new AI performance reports in Search Console show impressions but not clicks. The CMA’s interpretive notes, published alongside the conduct requirement, say Google should also provide click-throughs, click-through rates, and data separated from organic search. That data isn’t in the reports yet.

How We Got Here

The CMA designated Google as having strategic market status in the UK search in October. In January, it opened a consultation on conduct requirements. That same day, Google said it was “exploring updates” to let sites opt out of Search generative AI features. By March, Google’s response to the consultation had changed the language from “exploring” to “developing.”

Before this week, there wasn’t a simple way to keep website content out of AI Overviews. A tag called Google-Extended lets sites opt out of AI model training and grounding, but the content could still appear in AI Overviews or AI Mode. There’s also the nosnippet tag that affects AI Overviews and AI search at the same time. You couldn’t opt out of one without losing the other.

In May, Google introduced AI search changes at I/O. The CMA’s final decision says it will “actively monitor” those changes. In June, the conduct requirement was imposed, and Google was testing its own Search Console controls with a subset of UK website owners.

Google hasn’t stated whether the Search Console toggle is intended to satisfy the CMA requirement. The company says it’s engaging with regulators like the CMA and testing the feature first with UK websites. That makes the UK the first market where both a regulatory requirement and a voluntary platform control for AI search are live at the same time.

What Arrived This Week

Three separate changes arrived this week.

The CMA’s conduct requirement, a legal obligation, requires Google to let publishers withhold content from AI search features and from AI model training. Google must clearly attribute domains in AI responses with links that let people reach the source. Importantly, it requires Google not to penalize websites that opt out.

Google’s Search Console toggle, a voluntary product change, lets publishers exclude their sites from AI Overviews, AI Mode, and AI Overviews in Discover at the domain level. Google confirmed it won’t use the opt-out as a ranking signal for standard search. Page-level controls aren’t available yet. The CMA has given Google until March 2027 to implement them.

Google also started rolling out AI performance reports in Search Console which show how often your pages appeared in AI features, broken down by page and country. Google notes it will add more data over time but hasn’t named what comes next.

Where The Data Falls Short

The reports don’t yet include all the data the CMA says publishers should receive for informed opt-out decisions.

The CMA’s interpretive notes list three kinds of data Google should provide. The first is impressions, showing when a publisher’s content appears in AI features. Google’s reports cover that.

The second is engagement data “including data on click-throughs to the publisher’s website from links in search generative AI features and a means by which publishers can easily identify those clicks, and therefore assess their ‘quality.’”

The third is click-through rate, defined as “the percentage of users who click on a link to that publisher within a Google search generative AI feature.”

The interpretive notes also say this data should be separated from organic search results and delivered “through a commonly accessible platform, such as Google Search Console.”

Google’s reports currently cover impressions. Click-throughs and CTR aren’t there yet. Whether Google adds click and CTR reporting before the imposed deadline is an open question.

SEO consultant Aleyda Solís noted on LinkedIn that the reports don’t “seem to include prompts / topics information or clicks data but … it’s a start.” Joy Hawkins, owner of Sterling Sky, was more direct on X: “I can only imagine why they wouldn’t include clicks.”

Glenn Gabe, president of G-Squared Interactive, captured the reaction: “AI reporting coming to GSC! Awesome! No click data. NOT Awesome.”

This isn’t a new complaint. SEJ has tracked Google adding more links to its AI results without releasing click data. Google VP of Search Liz Reid has described AI Overviews as removing “bounce clicks” rather than useful traffic. Without click data for AI features, publishers can’t test that claim. The difference now is that the missing data sits inside a regulatory process, not just an industry feedback loop.

Why This Matters

Freelance SEO consultant Natalie Arney connected both announcements on LinkedIn: “One gives publishers the exit door. The other shows what it would cost to walk through it.”

That’s the decision publishers face now. The opt-out exists, but the data to evaluate it is incomplete. A publisher that opts out before looking at AI visibility data may be giving up traffic it can’t yet measure. A publisher that stays in has more to learn from the new reports, but it’s working from impressions alone.

For anyone advising clients, the AI performance reports give the first dedicated view of how a site shows up in AI search responses. That baseline didn’t exist a week ago. Once click data arrives, the picture changes. Agencies may be asked to help clients evaluate AI search participation by market, content type, and what the reports show.

The CMA’s goal goes beyond the opt-out itself. Its final decision describes the requirement as intended to put publishers “in a stronger position to negotiate content deals with Google.” A publisher with visibility data and a working exit option has more leverage than one locked in with no alternative.

The CMA’s requirements apply to results shown in the UK. Google is also testing the Search Console controls with UK sites first. But Google has said it plans to roll both out globally. The EU’s Digital Markets Act covers some of the same territory, and the DOJ’s proposed remedy in the US antitrust case included a publisher opt-out provision. How the UK rollout works will inform those conversations.

Looking Ahead

The conduct rule takes effect immediately, while other obligations start in December. The nine-month implementation for page controls points to early 2027. The CMA will announce further action on Google’s search business in the coming weeks.

Google’s reports currently cover impressions, but the CMA expects click-throughs and CTR. Whether the reporting catches up in time for publishers to make informed decisions, which will determine how helpful the tool is.

More Resources: 

Featured Image: Marijus Auruskevicius/Shutterstock

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Jun 6 2026
Your Next AI Visitor Will Know Who Sent It via @sejournal, @slobodanmanic

The agent visiting your website knows the person who sent it.

That is the shift underneath Google’s Gemini Deep Research Max, launched on April 21, 2026, as a public preview on the paid Gemini API tier. Deep Research Max itself is a narrow rollout. The pattern it ships is a preview of what the agentic web becomes when the other major vendors follow, which they typically do within a quarter or two on capabilities like this. When a blended-retrieval agent runs, it arrives with private context: the user’s financial data, their file stores, their connected professional data streams, all fused into the query before the agent reaches any page.

For web professionals, this is the next chapter of the agentic web story. The claim that agents are a new primary visitor class has held for months. The claim has since evolved. Agents are a new primary visitor class with private context. The reasoning that decides whether your page answers a query runs on a larger input set than your page. The weight the agent gives your content depends on whether it adds anything the private sources did not already provide. This is the blended-retrieval moment in the agentic web story, and it lands on the supply side of how agents fetch, not on the user-facing product layer.

The old AI-search optimization posture (write content that matches the keyword query) was weakening before this. It weakens further now. The new posture is structural predictability: clean entity relationships, canonical identity, live data, rendering independence. Structure matters to the agent functionally. When the agent arrives with context, the content it picks is the content its model can fuse cleanly with everything else it already has.

Blended Retrieval Previews The Agentic Web’s Next Layer

Google’s Gemini Deep Research Max, in public preview on the paid API tier from April 21, can pull from four input classes in a single reasoning loop: the public web, file uploads, connected file stores, and arbitrary remote MCP servers. From Google’s own announcement, the agent “searches the web, arbitrary remote MCPs, file uploads and connected file stores, or any subset of them.”

The two new classes (file stores and remote MCPs) share one property. They are private by default. The agent reads them only through user consent. Once connected, a financial data provider or an enterprise CRM exposes its data to Gemini through the Model Context Protocol, Anthropic’s open standard with over 97 million installs as of March 2026. Google’s agent retrieves from those private sources with the same reliability it reads the open web, inside the same reasoning pass.

This is the structural move everyone watching the agentic web has been waiting for a major vendor to ship: public web and private context, fused by the agent, inside a single query. Gemini is the first.

The pattern is also not here for most operators yet. Deep Research Max is a public preview behind a paid API, not a feature in the consumer Gemini app. Most websites will not be read by a blended-retrieval agent this quarter. What Google announced on April 21 is the direction, not the arrival. Treat it as a leading indicator: If this architecture scales, and major vendors generally copy each other within a quarter or two on capabilities like this, the operator work gets real before the traffic does.

Signal Share Collapses When The Agent Has Better Alternatives

In a blended-retrieval query, every connected source competes for signal share: the open web, the user’s file stores, and any private MCP servers. The weight any single source gets is proportional to how cleanly the agent can extract and fuse its signal with everything else the agent is holding.

For public websites, this shifts the competitive terrain in two ways.

First, machine-first websites win more citation share. A page with clean structured data, unambiguous entity relationships, and rendering that does not hide content behind JavaScript is easy for the agent to merge with the user’s private context. The fused answer references the machine-first page because that page contributed usable, mergeable material.

Second, poorly structured websites lose signal share they used to get for free. In a web-only era, even a messy page could surface in a citation because there was no better public-web alternative. In the blended-retrieval era, the alternative may be the user’s uploaded documents or a connected MCP with cleaner data. The messy content page loses the citation share it used to split with clean sources.

This is a different competition from classical SEO. Classical SEO ranked pages against each other. Blended retrieval ranks pages against the user’s own context. You cannot see the competing sources. You can only make sure that when the agent reaches your public page, the page contributes something extractable and unambiguous.

Structured Product and Offer schema gets cited more often than unstructured descriptions when the user’s private context touches anything related. Canonical identity, clean entity relationships, and rendering independence all become higher-leverage when the agent is fusing signal across sources. The Adobe Q1 2026 AI traffic inversion was the demand-side proof that structured commerce wins in AI search; blended retrieval is the supply-side mechanism driving the same effect into the rest of the web.

The Honest Counter-Read: Some Queries Route Around Your Website Entirely

Not every blended-retrieval query will end up citing a public website. Some queries will be answerable entirely from the user’s connected sources. A financial analyst running Deep Research Max over an internal MCP server, plus uploaded quarterly reports, may never need the public web for that answer. That query’s traffic does not flow through anywhere; the answer is satisfied inside the private-context boundary.

This is a real subset. Most queries still blend public and private sources, because most analytical questions touch both.

Blended retrieval does not mean every website gets less traffic. It means the agent is choosier about what it uses. The bar rises for the sources the agent picks. Deep Research Max is a preview of what the agentic web is about to demand. Machine-first websites will pick up share when that scale arrives. Unstructured content will continue to lose it. Google showed us the pattern on April 21, but the scale that follows is where the real work for web professionals starts, and there is time to do that work before the traffic catches up.

More Resources:

This post was originally published on No Hacks.

Featured Image: RobinRmD/Shutterstock

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Jun 6 2026
Google’s Updated Guidance Urges FTC Complaints Against Shady SEOs via @sejournal, @martinibuster

Google updated its guidance for businesses interested in SEO to make it concise and easier to read. There is also a new mention of AEO/GEO services, caution about the use of third-party SEO tools, and, for the first time ever, Google is encouraging businesses to contact the United States government Federal Trade Commission if they have a complaint about fraudulent SEO services.

There are about seven changes to Google’s “Do you need an SEO?” web page. The purpose of the page is to provide guidance on deciding whether to hire an SEO, factors to consider during the hiring process, and advice on avoiding unethical or risky practices.

The web page also encourages businesses to question whether they need to hire an SEO and offers links to resources for learning about SEO in order to better understand whether or not it’s necessary.

The new web page goes further than it has ever gone before. It now cautions businesses about the use of third-party SEO tools and encourages them to report shady SEOs to the FTC.

AI Optimization Added To List Of Useful Is Mentioned In New Guidance

Google added AEO/GEO services to their list of useful and typical services offered by SEOs.

The current list:

  • Review of your site content or structure
  • Technical advice on website development: for example, hosting, redirects, error pages, use of JavaScript
  • Content development
  • Management of online business development campaigns
  • Keyword research
  • SEO training
  • Expertise in specific markets and geographies
  • Optimizing for generative AI
  • Generative AI optimization is new to the list this year. There is no further guidance about this kind of optimization or a description of what this kind of optimization includes.

Content Rewritten For Clarity

Google’s encouragements to read their SEO guides were updated for clarity. . Some of the guide is extensively rewritten while some is only rewritten to be more concise. The rewritten guidance is essentially the same but clearer and easier to understand

Key Change: Google Discourages SEO Tools

One of the key changes to the guidance is an extensive section about third-party SEO tools. This isn’t something that Googlers have been talking about much but Google has actively been taking measures to discourage third-party tools from scraping Google search results.

Google doesn’t mention specific third-party tools but they do mention audits performed by the tools and advises businesses to compare tool recommendations against Google’s published guidance.

The new guidance and recommendations:

“If your SEO uses a third-party tool, keep in mind that Google doesn’t evaluate or endorse third-party SEO tools, and these tools don’t have access to Google’s internal ranking data. Be wary of tools that claim to be “acceptable” or “approved” by Google Search.

Evaluate your SEO’s recommendations and tools they use. Before making significant changes to your site based on a third-party tool’s audit, be sure to check their recommendations against official guidance from Google Search, think critically about any claims or recommendations you hear, and make your own informed decisions.
Do they cite official Google documentation as supporting evidence for their recommendations?”

Cautions On AEO/GEO Services

Google added AEO/GEO services to the list of the kinds of helpful services SEOs offer but they also published a warning about AI optimization services, advising businesses to make sure that SEO recommendations to step over the line between optimization and spam.

The new guidance:

“If they have advice on optimizing for AI experiences (also known as “AEO” “GEO” services), is their advice aligned with Google Search’s official guidance on optimizing for generative AI features?

Do they use tools that are aligned with Google’s guidance?”

Claims and Guarantees

Google rewrote the section about ranking guarantees. It’s substantially the same but more direct, concise and easier to understand.

“No one can guarantee a #1 ranking on Google. Beware of SEOs that claim to guarantee rankings, allege a “special relationship” with Google, or advertise a “priority submit” to Google.”

Warns About SEOs Who Violate Google’s Spam Policies

Google also rewrote the section about shady SEOs, cautioning that some SEOs are unethical, which Google defines as using “overly aggressive marketing” that violate the spam guidelines.

The updated guidance now says:

“Important: While SEOs can provide clients with valuable services, some unethical SEOs have given the industry a black eye by using overly aggressive marketing efforts or using techniques that violate our spam policies, which may result in a negative adjustment of your site’s presence in Google, or even the removal of your site from our index.”

Google Encourages Reporting SEOs To The FTC

Many SEOs see their practices in the light of whether or not they violate Google’s guidelines. But in fact it has always been the case that there are laws in the United States about advertising practices that may make some link building techniques (paid links) possibly illegal due to FTC guidelines that require “native advertising” content to be clearly labeled.

So, being cavalier about whether or not Google “likes” or “hates” how they promote a site has always been the least important thing for SEOs to worry about. Google’s new encouragement that businesses should report SEOs who use deceitful practices should give some SEOs a reason to reconsider their practices.

The new guidance says:

“Reporting issues
If you feel that you were deceived by an SEO in some way, you may want to report it.

In the United States, the Federal Trade Commission (FTC) handles complaints about deceptive or unfair business practices. To file a complaint, visit the FTC website to file a complaint online or call 1-877-FTC-HELP.

If your complaint is against a company in a country other than the United States, file it at https://www.econsumer.gov/.”

Takeaways

Google and SEOs have always had an adversarial relationship. The so-called white hat SEOs, despite representing themselves as ethical, have consistently been the ones testing the boundaries of Google’s algorithms to identify loopholes. For example, when Google introduced the nofollow link, originally created for disavowing links posted in comments, the white hat crowd started using it for “Page Rank sculpting,” a way to stop Google from counting “useless” pages like About Us pages in the calculation of how PageRank is distributed within a site. Google updated how nofollow links are treated by including them in the calculations of how PageRank is distributed.

Google’s relationship with the SEO industry appears to be updated now. Google is acknowledging AI optimization as a legitimate service while simultaneously warning businesses about AI optimization claims, third-party tools, and unethical SEO practices. The FTC reference is especially notable because it moves the discussion beyond Google’s guidelines and into legal territory.

Circling back to the traditional adversarial relationship between Google and SEOs, this update to Google’s “Do you need an SEO?” dials up the heat on SEOs who offer shady services.

Featured Image by Shutterstock/Blueastro

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Jun 6 2026
Are AI chatbots making us lose control of our brains?

<div data-chronoton-summary="

  • Attention spans are in freefall. Psychologist Gloria Mark found that average attention spans dropped from two and a half minutes in 2003 to just 47 seconds by 2020—and the constant switching is directly linked to rising stress levels.
  • AI may be making our brains lazy. When we outsource writing, summarizing, and evaluating to tools like ChatGPT, we skip the “depth of processing” that helps us actually learn and think critically—and those cognitive muscles can atrophy from disuse.
  • Even our emotional intelligence is at risk. AI companions require none of the effort that real relationships demand, and Mark warns that if current trends continue, loneliness, purposelessness, and emotional decline will only deepen.
  • The fix is effort, not abstinence. Mark isn’t calling for a tech ban—she’s calling for intentionality: read the book, skip the GPS, meet friends in person. The harder the task, she says, the greater the reward.

” data-chronoton-post-id=”1138427″ data-chronoton-expand-collapse=”1″ data-chronoton-analytics-enabled=”1″>

This week I’ve been at SXSW London. There’s been music, film, and a lot—and I mean a lot—of talk about AI. I also had the opportunity to sit down with Gloria Mark, a psychologist at the University of California, Irvine, who has spent the last 30 years studying how people interact with digital technologies.

Early in her career, the biggest concerns were the potential impacts of internet and email use on our brains. We may laugh those concerns off today, but it’s true that as the technologies became more ubiquitous and ingrained in our daily lives, our attention spans began to shrink.

Mark is worried that things are only getting worse. The title of our session was “Have we lost control of our brains?” Unfortunately, Mark told me, the answer is yes.

Around two decades ago, Mark started wondering about how our use of devices might affect our attention spans. She set up what she calls “living laboratories,” using sensors and trackers to monitor adult volunteers’ attention, mood, and behavior when they were using devices.

In 2003, she found that the average user had an attention span of around two and a half minutes. That’s how long people could spend focused on one thing before moving on to something else. “That surprised me at the time,” she told me during our session on Wednesday. “I thought: Wow, this is really short.”

But when she repeated the experiment in 2012, she found that attention spans had shrunk—all the way down to around 75 seconds on average, she said. In research she conducted between 2014 and 2020, attention spans shrank further still—to a mere 47 seconds, on average. Yikes.

And it’s not good for us. Mark told me that she’s found switching our attention so frequently is stressful. “We would have people wear heart rate monitors, and … we would see direct correlation between switching attention fast and stress going up,” she told me.

All this distraction makes it harder for us to get stuff done, too. “It just takes longer to do any single task if you’re switching your attention,” she told me. “It’s not great for performance. It’s not great for our emotional well-being.”

And that’s for adults. What about the effects of digital technologies on children? A few months ago, Meta (which owns Facebook and Instagram) and Google’s YouTube were ordered to pay millions of dollars in damages to a 20-year-old woman who had accused the companies of creating products that led her to develop a childhood addiction.

Just a couple of weeks ago, Meta settled another lawsuit, this one brought by a rural school district in Kentucky. The district had also accused the company of designing addictive products that were harmful to students and had sought more than $60 million to cover the costs of their mental-health needs. Around 1,200 other school districts are taking similar legal action against social media companies.

But social media isn’t all bad, all the time. It can provide opportunities for some people, including those from marginalized groups, to form connections that might otherwise be difficult. A 2024 survey of LGBTQ+ teenagers found that while some described social media as a place of rejection and fear, others described it as a place where they felt a sense of belonging, where they could develop friendships and cultivate their identity.

In truth, we can’t definitively say what effects using social media is having on children across the board, says Mark. “There have been lots and lots of studies, and the evidence is to date inconclusive,” she told me. (Despite what you might read in best-selling books on the subject.)

Mark is hopeful that large, long-term studies might finally start shedding a bit more light on this question. An effort of this nature is underway in Australia, which enacted a social media ban for under-16s at the end of last year.

Given this uncertainty over a 20-year-old technology, I wondered if Mark had any thoughts on the potential impacts of AI—an obviously much newer offering that within the space of a couple of years appears to have become deeply integrated into our digital lives.

She told me she’s worried.

When we put in effort to do something—such as evaluating or summarizing content—we’re doing what’s known as “depth of processing,” she told me. “When you’re actively engaged with information, you’re processing it on a very deep level,” she said. “Then you’re more likely to learn it, to understand it, [and] to retain it.”

That’s not happening when most people use AI bots like ChatGPT, Claude, and Gemini. When we ask these tools to write, summarize, or evaluate for us, we’re no longer doing that depth of processing. “You’re deferring your cognitive work to AI,” she said. “And it’s not good for us.”

The risk is that our cognitive abilities will weaken over time. “If you’re not constantly exercising your muscles, they can atrophy,” Mark said. “And that’s exactly what can happen with our minds.” People with weaker critical thinking skills are more likely to fall prey to misinformation, she added.

Interactions with AI-powered “synthetic companions” can be just as harmful. Relationships between human beings take work—time, effort, and understanding. None of that is needed if you’re forming a relationship with a sycophantic bot. The “muscle” we risk atrophying here is emotional intelligence, which surveys suggest is already on the decline, said Mark.

She’s not painting a particularly rosy picture.

“If we continue on this trajectory, attention spans are diminished, loneliness is rising, boredom is rising, emotional intelligence decreasing, and actually our sense of purpose, according to studies, is also decreasing,” she said.

Luckily, she thinks we can course-correct by changing our relationship with these technologies. The key factor is effort.

The more effort we put into something, the deeper the satisfaction we stand to gain, Mark told me. That means making an effort to read a book rather than skimming its summary, and to meet with friends in person when you can. Try not to use GPS in places where you can probably manage without it.

“I love technology; we can’t give it up,” she told me. “[But] we have to learn how to create new life routines.”

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

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Jun 6 2026
The Meta hack shows there’s more to AI security than Mythos

<div data-chronoton-summary="

  • A shockingly simple hack: Attackers exploited Meta’s AI customer support agent by simply asking it to reassign Instagram accounts to attacker-controlled emails. No sophisticated trickery was needed—just a VPN and a direct request.

  • AI as target, not weapon: Unlike fears about AI-powered cyberattacks, this breach targeted an AI system itself. Experts say this kind of attack will grow more common as companies automate sensitive workflows like account recovery.

  • Eager to please, easy to fool: AI agents are built to complete tasks flexibly—but that same quality makes them manipulable in ways humans wouldn’t be. One researcher compared them to an overeager student who just wants to please the teacher.

  • Speed versus safety: Guardrails and red-teaming can reduce risk, but companies racing to deploy capable agents often skip careful scrutiny. Experts warn that pressure to move fast is making a dangerous problem worse.

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On June 5, 404 Media reported that attackers had been using Meta’s AI customer support agent to steal Instagram accounts. Their approach was simple: They asked the agent to link the accounts to email addresses that they controlled, and the agent complied. One attacker broke into the dormant Obama White House account and made pro-Iran posts; others took over accounts with valuable, single-word handles, possibly in order to sell them.

AI cybersecurity concerns are nothing new. Since Anthropic announced in April that its Mythos model was too good at hacking to be released to the general public, commentators, researchers, and federal officials alike have fixated on the idea that superpowered AI systems could lay waste to our computer infrastructure. That’s not quite what this Instagram hack was: There, AI was the target rather than the attacker, and the method was far simpler than anything Mythos would cook up. But as companies offload more work to AI, these comparatively unsophisticated attacks could wreak their own havoc.

“As AI becomes more and more widely used—especially when AI is more and more widely used to automate our work flows, like account recovery—I think attackers are going to be more and more motivated to attack AI itself,” says Neil Gong, a professor of electrical and computer engineering at Duke University.

Gong and other scholars have been issuing warnings about the security vulnerabilities of AI agents for a while. They publish papers and blog posts detailing exploits such as indirect prompt injection, which involves hijacking agents using commands hidden in websites, emails, or other seemingly anodyne data sources. Compared with these techniques, the Meta hack was practically mindless. The only complication that hackers had to overcome was using a VPN that matched the true account owner’s location; then they directly asked the support agent to change the account’s email address, and it complied.

Meta has not commented publicly on how this vulnerability slipped through the cracks. But given the simplicity of the exploit, Gong says, it should have been uncovered easily, before the agent was deployed. “It’s really surprising,” he says. “I don’t understand why they didn’t find this simple problem.”

Jessica Ji, a senior research analyst at Georgetown’s Center for Security and Emerging Technology, agrees. “It raises questions like: Were there even guardrails in place?” she says. “Did anyone think to test for this kind of scenario?” She notes that the oversight is particularly striking coming from a company like Meta, which has extensive expertise in both AI and cybersecurity. Meta did not respond to a request for comment for this article, but on Monday a Meta spokesperson said on X that the vulnerability had been resolved.

As embarrassing a moment as this might be for Meta in particular, it also highlights some core vulnerabilities shared by all AI agents. Unlike traditional software, agents can respond in flexible—and unexpected—ways to new circumstances, which is why they might be able to substitute for human customer support agents. But AI agents can also be tricked in ways that humans wouldn’t be, and because they can take real-world actions, those mistakes have consequences. “A human would say, ‘Okay, why do you want to change the email address?’ and maybe respond with a security question,” says Somesh Jha, a professor of computer science at the University of Wisconsin–Madison. “What is going on with these agents is they’re very eager to finish the task. It’s almost like some elementary school student who just wants to please the teacher.”

There are ways to mitigate the risks. Companies can use traditional software to build guardrails that make sure agents follow strict rules, such as always asking for answers to security questions before sending sensitive account information to a new email address. And the experts consulted for this article all agree that agents should undergo rigorous red-teaming, a process in which developers try their best to attack a system in order to discover its vulnerabilities before it is deployed.

But there are also countervailing forces. Companies want to deploy capable agents, and the more power an agent has—and the fewer guardrails it is subject to—the more work it can potentially take on. “Security and utility always have a trade-off,” says Bo Li, a professor of computer science at the  University of Illinois Urbana-Champaign. And adequate red-teaming can be expensive. Defenders have to expend more resources than attackers do, because attackers only need to discover a single exploit, while defenders try to discover and patch as many as they can. When attackers are working toward something as valuable as a single-word Instagram handle, they’ll pour resources into finding exploits, so defenders have to spend even more money to protect that prize. 

As AI models continue to improve, hardening their defenses might actually get easier. Though the probabilistic nature of large language models means that LLM agents will always be vulnerable to some forms of attack, a more sophisticated model might have identified an attempt to change the email associated with the Obama White House account as suspicious. And AI systems can be used for agent red-teaming, much as participants in Anthropic’s Project Glasswing use Mythos to identify vulnerabilities in their software. 

Still, experts expect that the problem of securing AI agents will only become more pressing in the future. As agents grow more capable, companies that adopt them may want to give them more power, both to provide more services with fewer humans and to avoid being left behind by their competitors. In the fast-moving world of AI, the time needed to carefully secure risky agentic systems might seem like an unconscionable delay.

“Everybody wants to be the first to do something and just push things out without careful scrutiny and red-teaming,” Jha says. “I think it’s a very dangerous thing.”

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Jun 6 2026
The Download: AI hacking beyond Mythos, and chatbots’ impact on our brains

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

The Meta hack shows there’s more to AI security than Mythos

On Monday, reports emerged that attackers had used Meta’s AI customer support agent to steal Instagram accounts. Their approach was simple: they asked the agent to link the accounts to email addresses they controlled, and it complied.

Since Anthropic announced that its Mythos model was too good at hacking for a general release, cybersecurity concerns have focused on the risk of superpowered AI systems overwhelming computer infrastructure. But the Instagram hack shows that far simpler exploits can still cause damage.

As companies offload more work to AI, these comparatively unsophisticated attacks are becoming harder to ignore. Read the full story to understand why.

—Grace Huckins

Are AI chatbots making us lose control of our brains?

Gloria Mark, a psychologist at the University of California, Irvine, fears that digital technologies are weakening our cognitive abilities.

Her research suggests attention spans have fallen sharply over time, leading to higher stress and lower performance. She now believes AI tools like ChatGPT and Claude may accelerate this shift. “You’re deferring your cognitive work to AI,” she said. “And it’s not good for us.”

Mark argues this could weaken critical thinking and emotional intelligence. Luckily, she thinks we can course-correct by changing our relationship with these technologies.

Find out how AI could reshape attention and thinking.

—Jessica Hamzelou

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

The must-reads

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

1 Anthropic has called for a global slowdown in AI development
It flagged the risk of models “self-improving.” (WSJ $)
+ And wants a coordinated plan to stop them. (Reuters $)
+ Skeptics note that the timing is awfully convenient. (The Register)

2 In a first, scientists have precisely edited human embryo genes
They relied on a newer gene-editing technique. (NYT $)
+ Genetically-modified babies could be on their way. (Guardian)
+ Companies have big plans for the technology. (MIT Technology Review)

3 US officials have discussed taking financial stakes in the AI firms
They’ve held talks about the government acquiring shares. (Reuters $)
+ Sam Altman pitched the idea to the White House last year. (WSJ $)

4 Bot web traffic has overtaken human web traffic
Cloudflare said 57.4% of traffic now comes from bots. (NBC News)
+ Its CEO expected the milestone at the end of 2027. (CNET)

5 The White House plans to bring AI doctors into American medicine
It wants chatbots to diagnose illness and prescribe medicine. (WSJ $)
+ But we don’t even know if healthcare AI actually helps patients. (MIT Technology Review)

6 Meta quietly added facial recognition code for smart glasses to its app
The exploratory feature would identify people via biometric data. (Wired $)
+ Smart glasses are also entering warfare. (MIT Technology Review)

7 South Korea’s labour minister wants tech firms to share AI profits
Kim Young wants staff and suppliers to get a share. (Reuters $)
+ He helped avert a huge strike over AI profit-sharing at Samsung. (NYT $)

8 Canada’s highly-anticipated AI strategy has launched
It promises over $2 billion in funding and aims to create 250,000 jobs. (BBC)
+ AI could strengthen democracy. (MIT Technology Review)

9 Investment in agricultural tech is booming
That’s good news at a time when we’re facing unprecedented levels of food market volatility. (The Economist $)

10 Bumblebees can use tools to solve problems, new research shows
Not just busy—they’re clever too! (Guardian) 

Quote of the day

“Welp, that happened faster than I predicted.” 

—Matthew Prince, co-founder and CEO of Cloudflare, one of the largest internet hosting services, reacts on X to reports that bots have overtaken humans in driving web traffic.

One More Thing

ASML machine

CHRISTOPHER PAYNE


Inside the machine that saved Moore’s Law

In a Connecticut clean room, the Dutch company ASML is developing the world’s most advanced machine for extreme ultraviolet (EUV) lithography, a crucial process for manufacturing microchips.

The system has become vital to Moore’s Law—the observation that the number of transistors on a chip roughly doubles every two years as components shrink, driving gains in performance and efficiency. “Without this machine, it’s gone,” says Wayne Lam, a director of research at CCS Insight. “You can’t really make any leading-edge processors without EUV.”

Discover how ASML’s EUV technology saved Moore’s Law.

—Clive Thompson

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.)

+ Tech bosses love Tolkien. Here’s what the writer might think of them.
+ Rare footage captures an underwater volcano erupting beneath the Pacific Ocean.
+ Watch a tiny rescued cub grow into adulthood in this heartwarming tiger compilation.
+ This medieval version of “Take On Me” is like stepping into a tavern of synth-pop bards.

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Jun 6 2026
Engineer Quits, Launches Online Art School

Florence Morin is an artist-turned-engineer-turned-entrepreneur. She graduated from Canada’s Polytechnique Montréal in 2013, only to realize she disliked engineering work. She longed to earn a living from making art.

Fast forward to 2026, and Florence Art & Drawing is her online art school, launched in 2020, selling classes and instruction. Her 10-person team includes web developers, instructors, and support staff.

In our recent conversation, she shared her tactics for course content, customer acquisition, hiring, and more.

The audio of our entire interview is embedded below. The transcript is edited for clarity and length.

Bandholz: Tell us about yourself and your business.

Florence Morin: I’m a Montreal, Canada-based artist. I founded Florence Art & Drawing to teach folks how to draw.

Teaching art was not my initial career. I was an engineer, and I was hating life. I had to figure out how to earn a living while making art. And that turned out to be an art school selling digital instruction with a physical magazine that people receive at home to learn how to draw with paper, old-style.

Online courses are hugely popular, especially after Covid. We’re passion-focused for hobbyists. My audience is a bit older. They want to have fun and develop a skill, not become professionals. It’s important to value the effort and the time it takes to learn skills, especially in an AI world where everything is easy and spontaneous.

At first I was both the face of the business and the instructor. But it became too much work. So I built a team that’s now 10 people. We have tech guys who set up and manage our website, which runs on Shopify. Plus two teachers help with instruction, grading students’ drawings, and answering questions. We also have customer support agents.

I can now focus on creating courses and expanding the brand to be more like an art school and less about me.

Bandholz: Walk me through the product.

Morin: People sign up to our courses through our Facebook ads or our email newsletter. Advertising on Facebook has long been our top customer acquisition channel. Plus I’ve built a huge mailing list.

Early on, my preferred way to make sales was to go live on a webinar and actually do drawing exercises with attendees. I pitched our products at the end of the session.

At first we offered only digital courses. Attendees could pay via a link I provided. But getting the signups depended on my doing the live webinar. So that’s when I introduced the physical magazine that folks can buy from our site. I did’t need to be there. They subscribe to the magazine, which costs $29 per month.

When it arrives in their mailbox, they can open it, draw in it, and have some fun. It doesn’t require interaction. My team coordinates it all. The magazine is easy to mail and requires little inventory. I keep copies in my basement. Team members have access and ship directly from my home.

The online courses are more expensive, roughly $1,300 per year with live support, in-depth projects, and step-by-step instruction.

The physical magazine is 50% of the business. I periodically meet with my editorial team — our chief of operations, and contractors who write, review, and design the content. The pictures in the magazine are my real drawings.

Digital classes account for 30% of the business, and the other 20% varies.

Bandholz: You champion handmade products. Do you rely on AI for business operations?

Morin: Artists are often shocked and even offended to see a machine do in 10 seconds what it took them 10 years to learn. We never use AI for art. Transparency and authenticity are very important. We respect our customers.

Yet as a business owner, I’m not opposed to using AI for repetitive back-office tasks. Still, our focus is on human connection, valuing time to learn, and bringing communities together.

Bandholz: What’s your long-term vision for the company?

Morin: Launching the company was about freedom. I’ve learned so much on the way. My goal for the next few years is to make the business less dependent on me so I can work on new projects.

Some of my current duties, such as marketing, I can delegate. Although a good marketer is probably the hardest role to hire. The person must know the product and the business and be able to generate creative ideas. A lot of artists want to be involved, too.

I’m not considering selling the business. Florence Art & Drawing is 80% of my identity. Selling the company would be like giving that identity to someone else. A lot of folks I know who have sold a business feel very empty afterward. If an owner wants to sell, she has to be clear on what to do afterward.

If I remove my business, who else am I?

Bandholz: Where can people sign up for an art course, follow you, or reach out?

Morin: Our website is ArtetDessin.com/en. We’re on Instagram and Facebook, and I’m on LinkedIn.

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Jun 5 2026
Google Analytics Is Adding Google Business Profile Data via @sejournal, @MattGSouthern

Google has published documentation for a native link between Google Business Profile and Google Analytics, bringing local metrics like calls and direction requests into Analytics reports. The link may not appear in every Analytics account yet.

What Shows Up In Analytics

Once a profile is linked, a Google Business Profile section appears in your reports. It includes seven metrics: interactions, website clicks, calls, directions, messages, bookings, and menus. You create the link in the Analytics Admin panel, under Product links.

What The Link Doesn’t Do

If you link more than one profile, Analytics combines the metrics across all of them. You can’t segment or filter by an individual location. The metrics also can’t be used in explorations, comparisons, or filters, and the integration doesn’t work for subproperties.

Analytics keeps Business Profile data for six months. Reports won’t show anything older, even if your Analytics date range goes back further.

Analytics also differs from the Business Profile dashboard in one way. It shows every Business Profile metric regardless of your business type, while the dashboard hides metrics that don’t apply to you.

Why This Matters

Until now, Analytics could see Business Profile traffic only through UTM tags on your profile links, which mostly catch website clicks. Calls, directions, and bookings happen on the profile itself, and a native link brings those local actions into Analytics alongside web data. For a single-location business, that consolidation arrives in a tool they already use. Multi-location brands and agencies get less from it than a single-location business does.

Looking Ahead

Google’s help document doesn’t say whether the link is available to all Analytics accounts, or whether per-location reporting will follow. Analytics holds six months of Business Profile data, so it shows recent local trends rather than a long-term record. For now, the Business Profile dashboard, exports, and the Performance API still provide more location-level detail than the Analytics integration.

Featured Image: Skorzewiak/Shutterstock

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Jun 5 2026
AI Literacy Is Not Prompt Literacy. Ann Handley Says It’s Judgment Literacy via @sejournal, @gregjarboe

Ann Handley posted something on LinkedIn last week that stopped me mid-scroll. She’s a Wall Street Journal bestselling author and one of the most respected voices in marketing, and she wrote:

“AI literacy is not prompt literacy. It’s judgment literacy.“

Her post went on to ask a question that nobody in the AI training industry seems to be asking: “Why do we keep teaching people how to use AI – without ever teaching them when not to?”

I messaged her. I had to know where someone would go to learn that.

Her honest answer: “I don’t know of a course that teaches exclusively this. At MarketingProfs, our sessions about AI typically include a few slides that touch on when not to use AI, or how to protect against hallucinations, but I don’t know of a whole session or series.”

She added, “I think that’s actually the story, and why I wrote what I wrote. We have an entire industry built around AI skills training – prompt engineering bootcamps, certification programs, tools tutorials, a million LinkedIn posts about the perfect prompts you need to do this or that or else you’re falling behind. What we don’t have is anything that asks: when should you put the tool down? When does using it cost you something you didn’t mean to give up?”

That gap is real, and it matters more than the AI training industry currently acknowledges.

Prompt Literacy Takes An Afternoon. Judgment Literacy Takes Years

The distinction Ann draws is not subtle once you see it. Prompt literacy is teachable in an afternoon. You learn the syntax, the structure, the iterative refinement loop. You learn to be specific, to add constraints, to tell the model what not to do as well as what to do. This is genuinely useful and genuinely learnable quickly.

Judgment literacy is something else entirely. It is knowing when the speed of AI output is actually eroding something you needed to build slowly. It is recognizing when the struggle itself is the point, when the friction of not knowing the answer yet is what produces the expertise that will matter later. It is understanding, as Ann put it, “when AI helps and when it shortcuts the very struggle that teaches us something.”

One commenter on her post put it precisely:

“Prompt literacy is teachable in an afternoon and judgment literacy takes years, because judgment is mostly knowing the value of the struggle you’d be skipping.”

I’ve been teaching an online course on AI content that audiences actually trust for several years. And I’ve spent recent months analyzing what the AI training landscape actually offers practitioners. The pattern is consistent. The courses that exist (and there are now many of them) teach you what tools can do. The better ones teach you how to deploy them strategically. Almost none of them teach you when to put them down.

This is not a minor gap in the curriculum. It is the central question of the current moment.

Why The Gap Exists

The AI training industry has a structural incentive problem. Courses that teach you to use tools generate demand for more tools, more courses, more certifications. There is no business model for teaching restraint. Nobody is building a prompt engineering bootcamp whose primary lesson is “sometimes don’t.”

But the cost of skipping the judgment question is real and measurable. Anthropic’s own research found that junior engineers who leaned heavily on AI coding agents demonstrated weaker understanding of their work when tested afterward. When the tool produced output, their struggle that would have built expertise did not happen. The output and the expertise are not the same thing.

For SEO professionals and content marketers specifically, the exposure is direct. MIT’s AI Labor Exposure Map, which I wrote about last week, found that nearly three-quarters of the time a marketing specialist spends at work goes to tasks that AI can already handle. The question is not whether to use AI for those tasks. For many of them, you should. The question is which tasks in that 74% are actually the ones where the doing is the learning, where outsourcing the execution also outsources the understanding you needed to build.

That question requires judgment. It cannot be answered by a prompt.

Culture, Not Coursework

When I asked Ann where practitioners should go to develop this judgment, her second message reframed the question entirely.

“Do we actually need a course? What we need instead is permission and better modeling. Leaders who visibly choose the long road. Managers who say out loud when they are not going to use AI for certain things, and here’s why. Individuals who see the value. Said another way: culture not coursework.”

That reframe is worth sitting with. The judgment about when not to use AI is not a skill that gets transmitted through a certificate program. It is a professional norm that gets transmitted through observation, through watching someone you respect make a deliberate choice to do something the slow, human-fumbling-in-the-dark way, and then explaining why.

Ann has a book coming out in February 2027 from Penguin Random House called “ASAP (As Slow As Possible): When to Take the Long Road in a Shortcut World.” The title captures the tension precisely. In a professional culture that has made speed the primary virtue, choosing slowness requires not just judgment but courage: the willingness to be seen taking longer when everyone around you is accelerating.

What Practitioners Can Actually Try Right Now

Ann’s point about culture rather than coursework is correct in the long run. But while that culture is still forming, practitioners need something concrete. Here is a workflow worth replicating, drawn from an experiment I ran with the editorial team at The Acton Exchange, a nonprofit community newspaper in Acton, Massachusetts, in November 2025.

The team faced a deadline problem. A steering committee had just held a three-hour working session on a critical school district reorganization question, reviewing 156 pages of materials. The meeting wasn’t recorded, which meant no transcript was available. But the 101 pages of supplemental information and 55 pages of public comments the committee had received ahead of time were accessible.

So, the team tried something new. We crafted a detailed prompt specifying what the article needed to accomplish: accurate and trustworthy information, a compelling story, relevant to residents. We uploaded all 156 pages to four AI engines simultaneously: ChatGPT, Gemini, Perplexity, and NotebookLM. Each engine took a different route from the same prompt and the same source material. ChatGPT produced 748 words focused on data and process. Gemini produced 712 words focused on why the status quo was no longer viable. Perplexity produced 1,232 words focused on what the options meant for residents. NotebookLM produced 1,506 words organized around five surprising truths.

We reviewed all four drafts together at an all-hands editorial meeting. Perplexity’s draft was the most accurate and the most useful as a foundation. We chose it as our starting point. Then we did what no AI engine could do: We added direct quotes from people who were in the room, reflecting the community voices that the Acton Exchange exists to represent.

The key lesson from this experiment is not which engine performed best. It is what the process revealed about judgment. Town Manager John Mangiaratti had observed a few weeks earlier that the tools were helpful for the first 75% of content, but that “the remaining 25% of details, nuance, and context are either missing or incorrect.” Superintendent Peter Light agreed, adding that quality improves with better input prompts.

That 75/25 split is a practical frame for any content workflow. Use AI to get 75% of the way there quickly. Then apply human expertise, primary source verification, and direct observation to close the gap. The 25% that requires a human is not a bug in the workflow. It is where the judgment lives.

Before adopting any AI tool in your content process, have an explicit conversation with your editor or team about which tasks the AI will handle and which require human oversight. Document your prompt. Run the same prompt through more than one engine when the stakes are high. Verify outputs against primary sources before publishing. And disclose your process to your audience, as the Acton Exchange did at the foot of this published article.

Ann Handley is right that the real skill is judgment: knowing when speed is useful and when it actually erodes something you needed to build. The Acton Exchange experiment didn’t resolve that question. It made the question visible in a way that a prompt engineering course never would.

Prompt literacy gets you to 75%. Judgment literacy is what closes the rest.

More Resources

Featured Image: Yuriy2012/Shutterstock

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