Google Rolls Out AI Mode Information Agents To Ultra Subscribers via @sejournal, @MattGSouthern

Google has launched information agents in Search for AI Ultra subscribers, covering all AI Mode languages and markets.

Robby Stein, VP of Product for Google Search, announced the availability in a post on X and said access will expand to more people this summer.

The launch comes roughly three weeks after Google announced the feature at I/O. The agents monitor topics in the background and send updates with links to the web.

How Information Agents Work

Users ask AI Mode to keep them updated on a topic, and the agent watches for new information.

Stein described the feature in his announcement:

“Just ask AI Mode to keep you updated on any topic, and your agent will work around the clock on your behalf to send detailed updates and links to the web the moment new info is available.”

At I/O, Google said the agents look across the web, including blogs, news sites, and social posts. They also tap the company’s real-time data on finance, shopping, and sports.

How The Launch Compares To The I/O Plan

When Google announced information agents in May, the company said they would launch first for AI Pro and Ultra subscribers this summer.

Today’s availability covers Ultra subscribers only, and Stein’s post doesn’t say when Pro subscribers will get access.

He called the Ultra rollout a first group:

“Excited for this first group to try agents in Search! We’ll expand to more people this summer.”

Why This Matters

Information agents change when your content can reach searchers. Instead of running the same query each week, a person gets an update when something new appears.

Because the updates include links to the web, agent notifications could still bring traffic. Stein’s post doesn’t say how agents choose which sources to include in an update.

The Ultra requirement keeps the initial audience small. If access expands as planned this summer, more recurring queries could move from active searches to background monitoring.

Looking Ahead

Stein’s post doesn’t say whether the feature will eventually reach free users.

At I/O, Google also said agentic booking capabilities will roll out to everyone in the U.S. this summer. Custom experiences with Antigravity in Search are planned for the coming months, starting with Google AI Pro and Ultra subscribers in the U.S.

Google Extends Dynamic Search Ads Migration Deadline via @sejournal, @brookeosmundson

Google is giving advertisers more time to prepare for the transition from Dynamic Search Ads (DSA) to AI Max for Search.

In a LinkedIn post, Google Ads Liaison Ginny Marvin announced that the automatic transition deadline has moved from September 2026 to February 2027.

According to Marvin, the change comes in response to advertiser feedback. Google said the extension will give advertisers more time to prepare and avoid major account changes during the busy Q4 season.

What’s Changing

When Google first announced its plans for DSA earlier this year, the company said existing DSA campaigns and ad groups would automatically move to AI Max beginning in September 2026.

Now, that timeline has changed.

Advertisers that have not manually upgraded their DSA campaigns will now have until February 2027 before Google automatically transitions them to AI Max.

Google continues to recommend using the manual upgrade tools rather than waiting for the automatic transition. According to Marvin, those tools will begin appearing in advertiser accounts over the next few weeks.

The company says manual upgrades provide more oversight and control during the process.

However, not every part of the rollout is being delayed.

Google confirmed that Automatically Created Assets (ACA) and campaign-level broad match settings will still transition to AI Max as planned in September 2026.

Additional Reporting Updates Coming

Alongside the deadline extension, Google also shared new details about reporting improvements for Final URL Expansion (FUE).

According to Marvin, advertisers can expect:

  • Account-level Final URL Expansion reporting
  • Additional performance metrics for FUE assets
  • Bulk asset removal capabilities directly from reporting tables

Google did not provide a specific launch date, but said those updates are coming soon.

The announcement follows ongoing advertiser requests for greater visibility into how Final URL Expansion selects and serves landing pages.

AI Max Is Now the Default for New Search Campaigns

Google also revealed that AI Max is now enabled by default when creating new Search campaigns.

According to Marvin, Google observed faster first conversions during testing, particularly within the first two weeks after launch.

Advertisers can still disable AI Max settings if they prefer a more traditional campaign setup.

The change gives new advertisers immediate access to AI Max without requiring additional setup during campaign creation.

Looking Ahead and What To Do Next

The deadline may have moved, but Google’s recommendation has not changed.

Advertisers that still rely on DSA campaigns should begin evaluating AI Max before the automatic transition arrives. The additional time allows teams to test controls, review reporting, and better understand how Final URL Expansion behaves within their accounts.

For now, the most immediate action is to watch for the manual upgrade tools as they become available. Google continues to position those tools as the preferred path for moving from DSA to AI Max.

Featured image: El editorial / Shutterstock.com

Reddit Climbs, Clicks Drop, GBP Comes To GA4 – SEO Pulse via @sejournal, @MattGSouthern

Welcome to the week’s Pulse: updates affect how you read post-update rankings, what new click data says about Google traffic, and where your local reporting lives.

Here’s what matters for you and your work.

Reddit Gained Top Positions In Every Niche After The May Core Update

SE Ranking analyzed 100,000 keywords and found Reddit grew its top 3 presence in all 20 niches it tracks.

Key facts: Reddit ranked first for 13,872 keywords after May, up 54% from 8,993 in March. Gains were strongest in experience-led niches, with Reddit holding 18% of top 3 in pets. YMYL categories saw less change, healthcare rising from 0.93% to 1.33%. Two-thirds of domains dropped in March didn’t recover in May.

Why This Matters

The niche-level split changes how you interpret “Reddit is growing” headlines. An 18% top 3 share in pets is a different competitive picture than 1.33% in healthcare.

The direction also reversed from March, when Amsive’s analysis found Reddit and similar platforms losing visibility. Core updates have moved the same platforms in different directions, which makes it risky to draw conclusions from a single update.

Most domains that lost visibility after the March update didn’t gain it back after the May update. For websites waiting on a rebound, the data shows another core update doesn’t guarantee one.

Read our full coverage: Reddit Gained Top Positions In Every Niche After May Core Update

SparkToro Data Shows 68% Of Google Searches End Without A Click

SparkToro co-founder Rand Fishkin published new U.S. zero-click data drawn from Similarweb’s clickstream panel, covering January through April.

Key facts: In the panel, 68% of Google searches ended without a click. For every 1,000 searches, 232 clicks reached the open web. Of all clicks, 66% went to the open web, 27% to Alphabet properties, and 6% to ads.

Why This Matters

When stakeholders expect click rates from a few years ago, this gives you something concrete to point to.

The measurement angle matters as much as the traffic angle. Google’s new AI performance reports in Search Console show impressions, and independent data keeps showing fewer clicks. Visibility tracking now means watching where you appear, not just what arrives in your analytics.

What SEO Professionals Are Saying

In a comment on Fishkin’s LinkedIn post, Darren Shaw, founder of Whitespark, connected the data to Google’s new Search Console reports:

“I shared a post recently talking about how Google will be showing impression data from Ai overviews and AI mode in search console. Lots of people complaining in the comments that there was no click data. I thought, “you don’t get it”.”

Andre Alpar, board member and advisor at Alpar Ventures, questioned whether searches that lead to follow-up searches should count in another comment:

“A portion of the 29% that do “another search” do a click afterwards on thair next search. So they are not 100% “zero click” imho.”

The reactions split between accepting the click decline and debating how to count it.

Read our full coverage: Google Search Sends 23% Of Queries To The Open Web

Google Updates Its SEO Documentation

Google published a new Search Central page covering third-party SEO tools, services, and advice. It also updated its “Do you need an SEO?” page with about seven changes.

Key facts: The new page advises businesses to check SEO advice, including AEO and GEO, against Google’s documentation. The updated hiring guidance warns about third-party tools, mentions AEO and GEO services, and now encourages business owners to contact the FTC about fraudulent SEO services, a first for this page.

Why This Matters

The guidance splits SEO information into two categories. One is third-party opinion based on data or experience. The other is Google’s documentation, which the page recommends for weighing everything else.

Roger Montti’s analysis reads the wording as aimed at agencies and people selling SEO services, which puts you on the receiving end.

The “Do you need an SEO?” page is what business owners find when they look into getting help. It tells them to weigh your recommendations, and your tools, against Google’s documentation.

The AEO and GEO mentions give the terminology debate an official anchor. When a client asks whether they need a separate AEO strategy, Google’s answer is now on the record.

Read our full coverage: Google’s New Guidance Claims Authority Over SEO, Tools, And AEO/GEO and Google’s Updated Guidance Urges FTC Complaints Against Shady SEOs

Google Business Profile Data Connects To Analytics & Gemini

Two updates put Google Business Profile data in new places. Google documented a native Business Profile link in Google Analytics and announced a Business Profile connection for the Gemini app.

Key facts: The Analytics link brings seven Business Profile metrics into reports, including calls, directions, and bookings. Once connected, Gemini can draft review replies, edit profiles, and answer performance questions. The Gemini features begin rolling out this month, the Business Profile connection follows in the coming weeks.

Why This Matters

Local reporting has lived in separate places for years. Website data sits in Analytics, while calls and direction requests sit in the Business Profile dashboard. The Analytics link closes part of that gap.

Whether it helps you depends on your setup. Analytics combines metrics across linked profiles, so multi-location reporting still needs the Business Profile dashboard or the API.

What Local SEO Professionals Are Saying

Darren Shaw welcomed the Analytics link in a LinkedIn post:

“Google Business Profile data is coming to Google Analytics.

You’ll soon be able to connect your Google Business Profile directly to GA4 and see local performance data inside Google Analytics. This means you’ll be able to report on things like:

  • Calls
  • Bookings
  • Direction requests
  • Website clicks
  • Total interactions

And this is great because local SEO reporting has always been messy.

Your website data is in one place, your GBP data is somewhere else, and you have to piece it all together manually.

Now you’ll be able to see more of that data in one place and get a clearer picture of how your Business Profile is helping people find you, contact you, and visit your website.”

In a comment on Shaw’s post, Kaycie Mandour-Smith, managing director at Infinity Dental Web, a digital marketing firm for dentists, described a restriction she ran into:

“Was so excited for this and then…. if you have your listings in a manager acct group, you can’t connect them. Have to ungroup them in order to be able to link. Hoping newer iterations will allow you to select a group and then a profile.”

Read our full coverage: Google Analytics Is Adding Google Business Profile Data and Google Is Adding Business Profile Tools To The Gemini App

Theme Of The Week: Search Work Keeps Moving Onto The Big Platforms

Each story this week shows more of the search workflow moving onto large platforms.

SparkToro’s data measures fewer clicks reaching independent websites. SE Ranking’s numbers show top positions collecting on Reddit across every niche it tracks. The Business Profile connections move local reporting and management into Google Analytics and Gemini.

This week is less about any single feature and more about where the work gets done. The data, reporting, tools, and advice all sit closer to the big platforms than they did a month ago.

Top Stories Of The Week:

More Resources:


Featured Image: Shutterstock

New: Yoast releases performance optimizations for larger websites

With our latest 27.8 release, we introduced performance optimizations that should reduce loading times throughout the plugin’s functionalities, especially noticeable in large sites with lots of posts and users. 

Note: This post contains technical content and implementation details.

Offering well-tuned software with minimal overhead in servers and fast loading times is always at the forefront of everything Yoast developers do. However, Yoast SEO is installed in millions of websites so the variance of setups that we must be well-tuned for is big. This means we should be continuously going back to search for windows in optimizing the performance of the plugin. We’ve been known to do that consistently in the past, like when we improved our database system

The 27.8 release is the outcome of one of those targeted reviews. We deliberately picked features whose behavior at scale offered the most headroom and reworked them to be leaner and faster. From modifying queries to make pages faster for sites with many users and shaving heavy operations in the admin for sites with many posts, to reducing rounds trips to the database for multiple features and generally applying performance best practices, this is a release meant to improve the user and developer experience in the Yoast SEO plugin. 

We would also like to offer a technical summary of the improvements in this release here, focusing on their nitty-gritty details because it’s always nice to raise awareness about performance best practice (not to mention that it’s always fun to talk about code). 

Significantly reduce loading times of the root sitemap on sites with many users 

For context, for Yoast SEO to calculate the Last Modified value of the author sitemap, when it outputs the root sitemap, it uses the usermeta of the all the users that are eligible to be included in the author sitemap.  

Calculating the eligible users was traditionally done by checking user capabilities. This was done by adding the ‘capability’ => [ ‘edit_posts’ ] argument in the get_users() call that was used. As a result, a very heavy query with multiple joins and no use of the indexes of the database was triggered.  

Specifically, the resulting query added a clause like this: 

AND ((((mt1.meta_key = 'wp_capabilities'
        AND mt1.meta_value LIKE '%"edit_posts"%')
       OR (mt1.meta_key = 'wp_capabilities'
           AND mt1.meta_value LIKE '%"administrator"%')
       OR (mt1.meta_key = 'wp_capabilities'
           AND mt1.meta_value LIKE '%"editor"%')
       OR (mt1.meta_key = 'wp_capabilities'
           AND mt1.meta_value LIKE '%"author"%')
       OR (mt1.meta_key = 'wp_capabilities'
           AND mt1.meta_value LIKE '%"contributor"%')
       OR (mt1.meta_key = 'wp_capabilities'
           AND mt1.meta_value LIKE '%"wpseo_manager"%')
       OR (mt1.meta_key = 'wp_capabilities'
           AND mt1.meta_value LIKE '%"wpseo_editor"%'))))

Since LIKE ‘%…%’ cannot use any B-tree index, MySQL must read each matching wp_capabilities row in full and do seven substring scans of the serialized PHP meta_value per row. 

By modifying that calculation from using the capability check to looking for users with published posts (via using the ‘ has_published_posts ‘ => true argument), we instantly turned the resulting query to be one that uses indexes and that performs way better in sites with many users.  

In fact, on one of our tests, on a site with around 2 million users, the time it took to complete each query (so approximately the time that took the root sitemap to render), went from over 300 seconds down to just 25 milliseconds! This means that the change has the potential for drastic improvements in loading times of root sitemaps in similar sites.  

Finally, considering that the ‘ has_published_posts ‘ => true argument was already used in a later stage of the sitemap generation, the change itself should have little to no negative impact on the actual functionality of the feature. 

Reduce loading times of the author sitemap on sites with many users 

For Yoast SEO to render author sitemaps, it needs to calculate the eligible users. On sites with many users, this can be a very heavy operation. Aside from the above optimization, we noticed that while Yoast SEO was calculating eligible users, it also added a meta query to check whether the user_level of each user was over 0.  

It turned out that this was a remnant from old times, because the user_level framework had been deprecated by WP core since version 3.0. While this didn’t break things in our sitemap feature, it unnecessarily added an INNER JOIN in the resulting query without much purpose and in sites with very big user and usermeta tables that was degrading performance. So we went and removed the unnecessary JOIN: 

INNER JOIN wp_usermeta AS mt1 ON wp_users.ID = mt1.user_id 

... 

AND ( mt1.meta_key = 'wp_user_level' AND mt1.meta_value != '0' )

Since the user_level framework was deprecated a long time ago, we made the deliberate call to drop support for it, especially since doing so would make our feature smoother. In fact, we are comfortable shipping this optimization and expect minimal disruption as a result, exactly because of how old that deprecation is. 

Prevent unnecessary expensive database queries in admin pages 

In order to timely notify admins that they need to perform the necessary actions for their site data to be indexed optimally in our internal storage, Yoast SEO used to run a database query daily while admins navigated throughout the backend. For big sites, that database query had the potential to run for several seconds, slowing the rendering of admin pages periodically. 

Specifically, the Limited_Indexing_Action_Interface::get_limited_unindexed_count() function that can run complex queries like below, was running periodically in admin pages slowing the speed of bigger sites’ rendering.  

SELECT Count(P.id) 

FROM   wp_posts AS P 

WHERE  P.post_type IN ( 'post', 'page' ) 

       AND P.post_status NOT IN ( 'auto-draft' ) 

       AND P.id NOT IN (SELECT I.object_id 

                        FROM   wp_yoast_indexable AS I 

                        WHERE  I.object_type = 'post' 

                               AND I.version = 2)

We managed to re-arrange the logic of the code responsible for the notification that told admins about pending actions in such a way that those heavy queries now run only once, at the moment it’s first detected that such a notification should be created.  

That way, we effectively cache the results of the Limited_Indexing_Action_Interface::get_limited_unindexed_count() and rely on cache invalidation that existed before our changes but weren’t properly utilized. As a result, a potentially very heavy database query went from being triggered daily (and in cases of very busy sites, with lots of concurrent users, once per 15 minutes) to being triggered only once, in most sites. 

Optimize expensive database queries in admin pages  

Related to the above query-preventing change, not only did we manage to avoid running that aforementioned heavy database query more than once per site, but we also managed to optimize the query itself. An added benefit from that is that we made the SEO optimization tool much faster in sites with lots of posts. 

Specifically, we went from: 

AND P.ID NOT IN ( 

    SELECT I.object_id FROM wp_yoast_indexable AS I 

    WHERE I.object_type = 'post' 

)

To:  

AND NOT EXISTS ( 

    SELECT 1 FROM wp_yoast_indexable AS I 

    WHERE I.object_id = P.ID 

      AND I.object_type = 'post' 

)

Since NOT IN (subquery) builds the entire list of object_ids, while the second query short-circuits the moment one row matches, the query runs considerable faster in sites with multiple thousands of posts. 

Reduce roundtrips to the database 

As a rule of thumb, roundtrips to the database are considered to be expensive operations that should be reduced to a minimum whenever possible. Our reviews discovered instances where we were retrieving data for multiple posts in sequential SELECT queries where we could have done a single batched SELECT query to gather data for all posts at once. 

For example, a piece of code that looked like this: 

$indexables = []; 

foreach ( $post_ids as $post_id ) { 

	$indexables[] = $this->repository->find_by_id_and_type( (int) $post_id, 'post' ); 

}

was refactored into something that looked like this: 

$ indexables = $this->repository->find_by_multiple_ids_and_type( 

	array_map( 'intval', $post_ids ), 

	'post', 

);

That meant that for a chunk of 1000 posts, instead of performing 1000 SELECT queries that yielded a maximum of one row, we now perform a single SELECT query that yields a maximum of 1000 rows. Naturally, we made sure that the posts that will be requested each time do not exceed a certain threshold, to avoid reaching MySQL usage limits. 

As a result, sites with e.g. 1000 posts would save 960 roundtrips to the database for certain operations like part of their SEO optimization or part of the output of the schema aggregation feature

Improve post editor performance by preventing unnecessary re-renders 

The WordPress editor re-renders Yoast’s sidebar panels whenever the data they pull from the store appears to have changed. Unfortunately, “appears to have changed” is decided by reference equality (JavaScript’s ===) not by comparing values. A selector that returns { items: [‘foo’] } looks identical to a human, but if it’s a fresh object literal each time, React treats it as new and re-renders the panel. And if we multiply that by a busy editor that dispatches state updates on every keystroke, the result is panels that re-render constantly for no reason. 

With the 27.8 release, we identified multiple instances where data that weren’t actually changed triggered unnecessary re-renders in the post editor and patched them, making our editor integration much more robust and performant. 

AI Overview Click Data Reveals Unexpected User Behavior Patterns For Marketers via @sejournal, @gregjarboe

Google revealed at I/O 2026 that AI Overviews now has more than 2.5 billion monthly active users. What it did not reveal is how those users actually behave once an Overview appears. New data from GWI, the consumer research firm whose surveys represent 3 billion individuals globally, fills that gap – and the findings challenge some of the assumptions SEO practitioners have been building strategy around.

The most actionable number from GWI is one the industry hasn’t been talking about. Among users who engage with AI-featured search every day, 50% click through to one of the cited sources. Among users who engage only once a week or a few times a month, that figure drops to 28%. Among those who use it less often than that, it falls to 14%.

That gap is not a minor variation. It is a 3.5x difference in click-through behavior between your most frequent visitors and your least frequent ones – and it has direct implications for where content investment pays off.

The Users Most Likely To Click Are Also The Most Actively Evaluating

Chris Beer, Senior Data Analyst at GWI, offered an additional layer that makes the frequency data more useful. When asked whether younger and older users experience AI Overviews differently, Beer noted a pattern that cuts against the assumption that younger users are simply more comfortable with AI-generated answers.

“Younger users are more likely to say AI Overviews have increased their trust of search results, but also more likely to say it’s decreased their trust as well,” Beer said. “The key takeaway is that younger users seem to be more actively evaluating AI’s role in search, whether positively or negatively, while older users are more likely to remain neutral or unaffected.”

Active evaluation – not passive acceptance – is what the high click-through rate among daily users reflects. These are not users who trust AI Overviews so completely that they stop there. They are users who have developed a consistent habit of using the Overview as a starting point and the cited source as the destination. For content strategists, that behavioral pattern is the signal worth optimizing for.

The Broader Search Shift GWI Is Watching

Beer’s response to a question about how GWI will track the next wave of AI search changes offered a reminder that AI Overviews are not the only variable in motion. Social search has grown meaningfully over the past five years, with 35% of Americans now using social platforms to find information online, compared with 30% in 2020.

That five-percentage-point shift is not dramatic on its own. But it is happening alongside the AI Overview rollout, the expansion of AI Mode, and the Gemini-embedded search interface Google announced at I/O – all simultaneously. The practitioners who are building strategy around a single variable, whether that is traditional rankings, AI citation presence, or social discovery, are mapping one road in a city that is rebuilding its entire grid.

Two Steps Practitioners Can Take This Week

The GWI frequency data points toward two specific actions, neither of which requires waiting for the next Google announcement.

The first is to identify which of your pages are already being cited in AI Overviews and run a simple test: Do those pages deliver meaningfully more depth, specificity, or expert perspective than the Overview summary that cited them? If the answer is no – if clicking through from an AI Overview lands a user on content that essentially repeats what the Overview already said – then the 50% of daily users who click citations will find nothing worth their time, and your bounce rate on AI-referred traffic will reflect it. The fix is to add one layer of original content to each cited page that the AI cannot replicate from your existing text: a specific data point from your own measurement, a direct expert quote, a named case study, or a step-by-step process that goes past the conceptual level.

The second step is to stop treating AI citation presence and social search as separate workstreams. The GWI data on social search growth means that a piece of content surfaced in an AI Overview, shared on LinkedIn, and discovered through social search is not three separate distribution events. It is one piece of content reaching an audience through three channels that users are using in parallel. The content that performs across all three tends to share a common characteristic: It answers a specific question with a specific answer, not a general topic with general commentary.

The Daily Audience Is Most Likely To Visit Your Site

The frequency data from GWI makes the stakes of that specificity visible. Daily users click at 50%. Occasional users click at 14%. The difference is not the AI. It is the audience. The daily users are the ones who have already decided AI-augmented search is worth their time. They are also the ones most likely to follow a citation to your site, evaluate what they find there, and form a durable judgment about whether your content is worth returning to.

More Resources:


Featured Image: Prostock-studio/Shutterstock

Google’s Agent-Friendly Checklist Is The Accessibility Audit Restated via @sejournal, @slobodanmanic

Google’s seven rules for building agent-friendly websites are the accessibility playbook restated for AI agents. Same audit, two visitor classes. The practitioners who already did the work for blind and low-vision users are most of the way to passing.

I noticed the page because Matt G. Southern wrote about it on Search Engine Journal on May 1, 2026. The web.dev article itself (web.dev/articles/ai-agent-site-ux, by Kasper Kulikowski and Omkar More) was last updated April 1, almost a month ago. Your TLDR version of it is: Google has shipped a checklist, kind of, and it’s worth running.

I tested nohacks.co against all seven rules. Six passed. One failed on every native element on the website, and the cause is a Tailwind v4 default that ships with no warning. Operators who upgraded from v3 to v4 lost this rule across their entire button population unless they added three lines back to their base styles.

The interesting part is which rule that is, and why.

And of course I fixed it.

The Seven Agent-Friendly Rules Google Published On Web.dev

The full list lives at web.dev/articles/ai-agent-site-ux, introduced by Google with the line, “To help agents navigate your website, consider following:”

  1. Reflect every action in the interface. A button click should produce a visible state change. An action that fires silently is invisible to the agent.
  2. Keep the layout stable. “Add to cart” should sit in the same place across product categories. Agents that take screenshots get confused by elements that shift between pages.
  3. No ghost or transparent overlays. Anything that covers an interactive element gets discarded by visual analysis, even if the cover is fully transparent.
  4. Use semantic HTML. and overstyled

    and . If you must use a non-semantic element, give it role and tabindex.

  5. Set cursor: pointer in CSS. Google calls this “a strong signal for actionability.”
  6. Link to with the for attribute. Lets the agent map the visible label text to the underlying field.
  7. Make interactive elements bigger than 8 square pixels. Visual analysis filters out anything smaller.

Two things Google did not say. First, the verbatim framing is “consider following.” Suggestion-grade, not mandate. Second, nothing in the web.dev article ties these recommendations to ranking, surfacing, or any specific AI-product-side consequence. Worth naming the gap. Google said consider; I’m pushing harder than that, because the source is the dominant search vendor and the list overlaps with two decades of accessibility practice. So the prescription below is mine, not theirs.

The closest thing to a punchline in the article is the second-to-last sentence: “Everything we suggest to make a site ‘agent-ready’ also makes sites better for humans.” That’s not throwaway. Every item on the list was already a WCAG recommendation when web accessibility advocates were the ones documenting it, a decade ago.

I also really like how this pairs with “machine-first, human-always” in Machine-First Architecture.

Six Of Google’s Seven Agent-Friendly Rules Pass On Nohacks.co (1, 2, 3, 4, 6, 7)

I ran the audit by inspecting the live HTML on the homepage and on a representative article page, the agentic browser landscape guide. The method was unglamorous: curl the page, grep for , ,

  • Rule 1 (clear state): Every interactive element uses a transition-all class. Hover states scale the primary CTA. Inputs get a focus ring. State changes are visible. Pass.
  • Rule 2 (stable layout): No layout-shift sources in the HTML. Header, navigation, and footer are consistent across home and article. No content reflow on hover. Pass.
  • Rule 3 (no ghost overlays): The only pointer-events-none element is the back-to-top button itself when it’s hidden. The button is non-interactive AND invisible at that moment, so it isn’t covering anything. Pass.
  • Rule 4 (semantic HTML): Eight elements on the homepage with proper aria-label attributes, plus 45 elements. Zero

    . Zero . Pass.

  • Rule 6 (label-for): The newsletter signup uses . The label text is screen-reader-visible and linked to the input by the for attribute. Pass.
  • Rule 7 (size threshold): Buttons render at 40 pixels by 40 pixels (h-10 w-10 in Tailwind). Inputs are px-5 py-3. All well above 8 square pixels. Pass.

That leaves rule 5.

Rule 5 (Cursor: Pointer) Fails On Every Native Button Under Tailwind V4

Native elements on nohacks.co render with cursor: default, not cursor: pointer. The failure spans the mobile menu open button, the newsletter Subscribe submit, six podcast episode play buttons on the homepage, and the floating back-to-top button. The article page adds two more newsletter Subscribe submits (one above the article, one below). Twelve unique native elements across the two pages. None of them get the cursor signal.

elements are fine. Anchors with href get cursor: pointer from the browser default, and that hasn’t changed. The failure is button-specific.

The cause is a Tailwind v4 change. From the official Tailwind CSS v4 upgrade guide:

“Buttons now use cursor: default instead of cursor: pointer to match the default browser behavior.”

Tailwind v3 included cursor: pointer on as part of its Preflight base styles. Tailwind v4 removed it. Operators upgrading from v3 to v4 don’t notice because the keyboard navigation still works, the screen reader still announces the button, the click still fires. The accessibility tree is unaffected. The thing that breaks is the visual signal that something is clickable, and that signal is rule 5 on Google’s list because it’s specifically what the vision-model side of the agent uses to decide what to interact with.

The fix is the snippet Tailwind ships in the same upgrade guide:

@layer base { button:not(:disabled), [role="button"]:not(:disabled) { cursor: pointer; }
}

Three lines. Restores rule 5 across every native button on the website. The :not(:disabled) clause preserves the existing disabled:cursor-not-allowed pattern that Tailwind users already have on their disabled-state buttons. Drop it into the global stylesheet, and rule 5 passes on every button at once.

Every Rule On Google’s List Maps To An Existing WCAG Recommendation

The accessibility community has been writing the same list for two decades. Five of Google’s seven rules map directly to a WCAG criterion:

  • Rule 2 (stable layout) overlaps with WCAG 2.4.3 (Focus Order) and WCAG 3.2 (Predictable).
  • Rule 4 (semantic HTML over styled

    ) is the foundation of the WAI-ARIA Authoring Practices and WCAG 4.1.2 (Name, Role, Value).

  • Rule 5 (visible cursor signal) maps to WCAG 1.3.3 (Sensory Characteristics).
  • Rule 6 (label-for-input) is WCAG 1.3.1 (Info and Relationships).
  • Rule 7 (minimum interactive size) is WCAG 2.5.5 (Target Size). The human-readable version is 24 by 24 CSS pixels; Google’s threshold is lower because vision models can detect smaller elements than human users can comfortably tap.

The pattern is consistent. Build for assistive technology, build for AI agents. The audit is the same shape, run for two visitor classes simultaneously. The vocabulary is different. The artifact is identical.

Run One Accessibility Audit, Recover Both Visitor Classes

Stop running accessibility audits and AI-agent-readability audits as separate disciplines on separate quarterly cycles. They are the same audit. Web professionals who already invested in WCAG conformance are most of the way to passing Google’s seven. Operators who never did the accessibility work now have the agent-readability work landing on the same checklist with vendor weight behind it.

Concrete move for this week:

  1. Pull the top five highest-traffic pages on your website.
  2. Run them through both Google’s seven rules and a WCAG-AA scan (Lighthouse, axe DevTools, the WAVE extension; whichever you already use). Note the overlap.
  3. Fix once. Recover both visitor classes.

If you are on Tailwind v4, add the three-line @layer base snippet to your global stylesheet first. That single change recovers rule 5 across your entire population. It is the single biggest fix on the list because Tailwind v4 ships everywhere now, the accessibility tooling won’t flag it (the click still works), and nobody is talking about this regression.

Search Interest In “Web Accessibility” Barely Moved When The EAA Took Effect, Then Quadrupled

Worldwide search interest in “web accessibility,” past five years. Source: Google Trends, captured 2026-05-02. (Image Credit: Slobodan Manic)

Search interest was flat for four years. Through 2024. Through most of 2025. Through the European Accessibility Act becoming applicable on 28 June 2025, which is the regulatory event that should have moved this curve more than anything else, and barely did. The bigger climb starts late 2025, accelerates through early 2026 to its peak, and has settled lower since. Worldwide search interest in the term more than quadrupled in 18 months.

I am not claiming the AI-agent coverage is what drove this. The data is correlational. But the shape is interesting: the regulatory event that should have spiked the curve barely did, and the curve started moving when the audience for accessibility-shaped guidance started overlapping with the audience for AI-agent-readability guidance.

The Convergence Is A Decade Old; Google’s Vendor Weight Is What’s New

The last decade of accessibility work was carried by a community that mostly didn’t have the weight to make it the dominant audit discipline. The work was the right work. The audience didn’t show up. Then AI agents arrived with budget, traction, and a vendor-side incentive structure pointing in the same direction. When Google publishes the same checklist as agent-readability guidance, the discipline stops being two audits run by two separate communities. It becomes one input-side discipline that web professionals run because the visitor class spans both.

Six of seven on the audit is a passing grade. The seventh is a CSS rule that was Tailwind’s job to set, and now it’s yours.

Trust … no one? At least, not blindly.

More Resources:


This post was originally published on No Hacks.


Featured Image: beast01/Shutterstock

Job titles of the future: Nature’s drug designer

In 2018, after nearly two decades working in Big Pharma, chemist Tim Cernak was ready to put his skills to a new use. 

For Merck, he’d developed precision therapies for cancer, HIV, and diabetes that could target disease while minimizing harm to healthy cells. But as a lifelong nature lover, he was increasingly concerned about the health of ecosystems and wondered whether his expertise could transfer. Animals, he learned, are often treated with pharmaceuticals formulated for humans, which affect them like old-school cancer drugs: Though intended to kill abnormal cells, they’re indiscriminate in the harm they cause. For instance, the standard of care for frogs infected with a deadly skin infection is itraconazole, an antifungal that is often lethal for the amphibian.

Cernak imagines a world where “the patient was always meant to be a frog in the first place, from the beginning to the end.” Now an associate professor at the University of Michigan, he’s worked on all types of creatures, from a Gila monster with a parasite to bald eagles with avian flu. Here’s what it takes to treat nature’s patients.

Experience with protein-modeling software 

Developing any type of drug is extremely expensive, failure-prone, and slow-going. But AI can speed up the entire drug-­design workflow, says Cernak. Google DeepMind’s AlphaFold model allows him to visualize a mutant protein’s three-­dimensional structure on a screen—rather than growing it on a plate, the traditional methodology—and then quickly generate possible new drugs that would latch onto that structure. The next step is to run a series of reactions and see which potential drugs may be effective; with the help of robots in the lab, he can speed through as many as 1,500 per day. 

Curiosity about creatures of all sizes

Cernak isn’t selective with his patients. For example, he worked on a treatment for loggerhead sea turtles after he was shocked to learn that the iconic species suffered from contagious tumors. He feels especially drawn to creatures that have helped humans, like the Gila monster, whose hormones have informed popular weight-loss drugs like Ozempic. And it’s not just animals; he’s also developing a precision insecticide to treat hemlock trees under attack from invasive species. 

A pioneering spirit

Cernak refers to this new discipline as “conservation chemistry.” It’s a combination of words with a loaded history, from DDT decimating US bald eagle populations in the 1960s, to cow painkillers killing millions of Indian vultures in the ’90s. He recognizes the risks, but Cernak feels that excluding chemists from conservation is a missed opportunity. 

“I’m just sick of looking at the chemical tools that are used in the conservation space, and they’re not cutting-edge,” he says. “It’s like, how do you have this super high-tech engine over here for making human medicines, while we’re living through a mass extinction?” 

Anna Gibbs is a journalist who covers the intersection between science and society.

Inside soccer’s data renaissance

Imagine tuning in to the opening kickoff of a World Cup match and seeing a player intentionally send the ball all the way down the pitch and right out of bounds on the opponent’s end. Casual fans might scratch their heads. Where’s the logic in surrendering possession seconds into a game? If you were Jesse Davis, though, you’d know that this play could be a prime setup to score. 

Davis is a professor of computer science at KU Leuven in Belgium and head of its Sports Analytics Lab, which has been at the vanguard of a data awakening in soccer since its inception more than a decade ago. Though the research group brings machine-­learning models to bear on a variety of sports—including basketball, volleyball, and field hockey—nowhere is its impact felt more than on the soccer pitch. 

Davis and his team of researchers employ advanced data analytics to reveal a range of (beg your pardon) game-changing findings that are shifting pro clubs’ decision-making. “His lab is the most influential sports analytics lab in soccer,” says Hugo Rios-Neto, data recruitment lead for Royal Sporting Club Anderlecht in Belgium. They’ve helped teams better evaluate their rosters, conceived ways to assess how efficient (or not) strategies are, and developed algorithms that uncover hidden tactical patterns.

Like, for instance, the value of kicking the ball out of bounds close to the goal and letting your opponent throw it back into play—a move that’s been popping up in some of the world’s top leagues over the last few years.

To make the statistical argument for this seemingly counterproductive move, Davis’s group built a training data set composed of more than 1.4 million passes and some 60,000 throw-ins—partly from the 2022 World Cup. They used tree ensemble models (essentially a mashup of decision trees) to simulate the tactic. The conclusion, which the researchers presented in a 2024 paper under the apt title “Boot it”: When the ball is in the middle third of the pitch, kicking it out of bounds on your opponents’ side of the field can put you within 10 actions (think passes and dribbles) of a goal. That can be a big deal in a game that has 1,500 or more actions per match and very little scoring. The idea, Davis explains, is that you’re setting yourself up to recover the ball in an advantageous situation.

Beyond providing discrete game-day insights, Davis also occupies a unique niche in the world of sports analytics, where many clubs now hire their own internal data teams to maintain a competitive edge. He makes most of his research freely available via open-source analytics tools, but the academic life also affords him the freedom to tackle more complex problems—like standardizing in-game data, a project that will make it easier to parse game footage and come up with winning strategies. 


Davis, 45, grew up in Wisconsin and spent his childhood enraptured by basketball and (American) football. Soccer was largely a nonentity to him until college, when the 2002 World Cup—in which Brazil famously swept the tournament—reeled him in. But the notion of going on to dissect the sport never crossed his mind. His doctoral studies in computer science at the University of Wisconsin–Madison had him working with radiologists to analyze mammography reports. 

Jesse Davis at Den Dreef stadium, home of the Belgian Jupiler Pro League team OH Leuven.
PETRA ISRAËL

In October 2010, he joined KU Leuven as a computer science professor looking at the intersection of AI and health care, with a focus on monitoring athletic performance. His research team studied, for instance, combining things like heart rate with other metrics to determine whether someone was overtraining. They also dove into the biomechanics of running.

The tactical and technical aspects of sports, and soccer specifically, became the subject of Davis’s professorial work when he hired Jan Van Haaren, an engineering student focused on artificial intelligence and a self-described soccer fanatic. He wondered if data analysis could be used to study things like passing, shooting, and ball progression—metrics the game was only just beginning to digitally crunch at the time. 

Davis realized that machine learning and other artificial-intelligence tools lent themselves well to the complexity, fluidity, and speed of soccer.

You need not be well versed in the moneyball-ization of pro sports to see that it’s relatively easy to apply deep statistical work to baseball or basketball. You can isolate actions like jump shots and assign value to ones taken close or far away. Soon a basketball coach realizes that a player who can’t make a layup, but shoots roughly as well from the three-point line as on mid-range jumpers, might as well go for the shot that gets more points. 

Soccer, by comparison, seemed like a poor candidate for that kind of analysis. “The vast, vast majority of actions really don’t lead to the outcome of a goal or even a shot,” says Rios-Neto. “So it’s hard to elaborate or derive a winning strategy from the data.”

But Van Haaren’s love of the sport, and Davis’s love of sports in general, inspired them to try. Over time, Davis realized that machine learning and other artificial-intelligence tools lent themselves well to the complexity, fluidity, and speed of soccer. In 2014, he officially stood up the Sports Analytics Lab. 

With a stable of about 10 students and postdocs at any one time, the lab began laying what Van Haaren calls the “intellectual foundations of how the game is analyzed today.” The researchers picked apart in-game actions, and suddenly they were valuing ball possession, penalty-kick strategy (aim for the center), and the merits of long shots on goal (take them). “One of the trends that’s been in soccer over the last five to 10 years is that the number of long shots has dramatically increased,” says Davis. “What the data let you do is really quantify what the probabilities of those things are.”


In the years since Davis and his team started untangling individual soccer tactics, their ideas have started to permeate clubs across Europe, like Belgium’s Club Brugge KV, as well as national soccer organizations in the US and Belgium. “The work coming out of the lab is genuinely useful,” Rios-Neto says, “and clubs apply it for a range of purposes.” 

Van Haaren, who’s now the director of football intelligence at Club Brugge, is one of many in-house analysts adapting the lab’s work to the pro game. “Our collaboration with the lab is centered on translating [the team’s] football philosophy into measurable, data-driven outputs,” he says. When a club wants to assess, say, how well a center-back is moving the ball down the field, it aims to tally how many times the ball ended up in the part of the pitch closest to the opposing team’s goal. It does this by combining event data, which records actions on the ball, with tracking data, which records player movement. This shows how well players fulfill their roles, which is useful in development and also when scouting for new recruits. 

Davis’s lab, meanwhile, is continuing to ask questions that apply to the game writ large. To determine if there’s an advantage to taking more long shots, for instance, postdoc Maaike Van Royand colleagues modeled the behavior of English Premier League teams using a Markov decision process—a computational framework in which some actions are under a person’s control while others are random. (That duality is particularly useful for soccer, where movement can feel anything but linear.) The results, presented in 2021 at the MIT Sloan Sports Analytics Conference, showed that Chelsea could gain 1.6 more goals per season by shooting from distance 20% more often.

Despite those kinds of insights from Davis’s lab and similar research groups that have sprung up over the last decade at institutions like MIT and Carnegie Mellon, soccer somewhat lags behind many other pro sports when it comes to collecting the data that analysts need. All teams employ people to watch video and use software to annotate specific in-game tactics—the details of which may make sense only to the most devoted fans. It’s a mostly manual process, one that can take up to six hours per game. “It’s a complete nightmare as a data analyst to work with,” says Davis.

So while the lab plays on, Davis has also joined up with researchers from other institutions in an effort to standardize data across all matches. The group is experimenting with transformers, the neural network architecture that underpins large language models like ChatGPT. If you can bring that to the world of soccer, a human game annotator could tag a tactic—a three-on-two breakaway, say—a few times, and that could train the model on the concept so it could tag subsequent instances on its own. “There’s been a lot of progress,” Davis says. “But it still remains quite hard.”

If we’re keeping score, though, the lab’s work has already made the analytics process easier thanks to open-source tools it’s put out there—some of which clock thousands of downloads a month. One is a framework called VAEP, a model that assesses the effects of all actions on the ball. Another is an xG (expected goals) model, which looks at the quality of a scoring chance. Still another is a package to synchronize event data with tracking data. “Lots of people in industry use our code in their daily workflows,” Davis says.

For him, the practical application of having their code out there is important, but the real (ahem) kick is watching theory become practice. As he says, “I’m really motivated to solve problems that arise in real settings and see my work have an impact.” 

Andrew Zaleski is a contributing writer at Washingtonian magazine. 

Why China is betting on big nuclear reactors

<div data-chronoton-summary="

  • China is catching the West on nuclear: China has nearly doubled its nuclear fleet since 2016 and is on track to surpass both the US and EU in nuclear capacity by 2030.
  • The secret is standardization: China builds reactors in batches of six or more using a uniform design and licensing system—essentially applying the factory-efficiency logic that small reactor advocates champion, but at massive scale.
  • Small reactors are exciting, but still unproven: A California startup just hit a key milestone in a US government pilot program, but its test reactor can’t yet produce electricity.

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It’s a tale of two nuclear industries.

In China, large reactors are coming together at a stunning pace. The country has nearly doubled its nuclear fleet since 2016, reaching nearly 60 gigawatts of total power capacity. The new facilities are nearly all gigawatt-scale pressurized-water reactors.

Meanwhile, the US has built just two reactors in that time—Unit 3 and Unit 4 at Plant Vogtle in Georgia. Smaller reactors are attracting a lot of excitement and investment, though. A microreactor developer just saw its reactor reach criticality in a new Department of Energy pilot program.

The world is racing to meet rising electricity demand, and many countries are interested in energy sources, like nuclear power, that don’t come with greenhouse-gas emissions. The key question: Which of these strategies will really pay off in terms of getting electrons on the grid quickly?  

Today, the US and France are known as leaders in the nuclear industry. The US has the world’s largest fleet, with France coming in second. France is heavily dependent on nuclear for its grid—about two-thirds of the country’s power comes from nuclear reactors.

But they have hardly added any new reactors to their fleets in recent years. The US can point only to Vogtle, and France connected its latest reactor to the grid in December 2024—the first in over 20 years. 

It’s incredibly difficult to build the massive projects that dominate the nuclear industry today. Up-front investment can run well into the billions, so investors need to wait decades to break even. Designs are complex and can often change during the regulatory process, tacking on cost and time. 

Many are hoping that the key to turning things around in these countries could be smaller reactors.

The idea is that shrinking the footprint of a reactor cuts down the initial investment needed to prove out the new technology. The reactors could even be put together in a factory rather than being built on-site, allowing for a lower price over time.

These smaller reactors are the target of tons of interest and investment in the US, including a new Department of Energy pilot program. The department set a goal last year of having three test reactors reach criticality by July 4, 2026, the nation’s 250th anniversary. (Criticality is the point at which a reactor achieves a self-sustaining chain reaction that can release energy.)

Last week, California-based Antares hit the milestone with its Mark-0 reactor. 

The company plans to eventually build microreactors, designed to produce between 100 kilowatts and 1 megawatt of electricity (large reactors on the grid today are at least 1,000 times that size). The core design is a sodium-cooled reactor, and it uses TRISO fuel, self-contained graphite-coated spheres of a more concentrated fuel than what most reactors use today. 

But there is still a long way to go before it can actually produce power—the Mark-0 doesn’t have any power conversion or heat removal systems. The company plans to produce electricity in late 2027 and deploy in the field by 2028, CEO Jordan Bramble told the Associated Press.

The private sector is interested—and invested—too. Big Tech companies are throwing money at new reactors they hope can help power data centers. 

But look to the other side of the globe, and others are sticking with the established blueprint: China is absolutely churning out large nuclear reactors. Construction started on six new reactors there in 2025, and two more got underway in the first five months of 2026. The country is on course to overtake both the US and the European Union in installed nuclear capacity by 2030.

The speed here is staggering. As of 2024, the average time to build a new reactor in China came in at between five and seven years. The global average is about nine years, and the two most recent reactors in the US took about 15 years.

One key to this speed is standardization: China has set up a uniform project management system to design, license, and build new reactors. They’re built in batches of six or more to take advantage of economies of scale.

It’s one of the ideas meant to give the edge to smaller reactors, but China is working to realize the same benefits for larger projects. A huge amount of government investment is certainly helping.

Larger reactors generally provide more electricity to the grid for a lower price, a key consideration in view of China’s steeply increasing electricity demand. While smaller reactors require less up-front investment than larger ones because of their size, they’ll actually be more expensive per unit of electricity produced. 

That’s not to say China is exclusively focused on big reactors: the country is also expected to see its first operational small modular reactor, the Linglong-1, start sending power to the grid this year.

But looking ahead, it’ll be interesting to see if smaller reactors can help the West keep building new nuclear power. At the moment, with China’s quick progress, it’s looking as if bigger might just be better. 

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

Google DeepMind is worried about what happens when millions of agents start to interact

<div data-chronoton-summary="

  • A new class of risk is emerging: As millions of AI agents begin working together online without human oversight, Google DeepMind warns we could hit a tipping point where today’s hypothetical dangers become tomorrow’s real ones.
  • $10 million to build a field from scratch: Google DeepMind has joined forces with Schmidt Sciences, the UK government, and others to fund research into multi-agent safety—a field that, right now, barely exists.
  • Think scams and cyberattacks, but supercharged: The risks aren’t science fiction—they’re turbocharged versions of what already happens online, from prompt injections that turn agents into self-guided malware to coordinated attacks on the digital infrastructure society depends on.
  • The future is arriving faster than expected: Risks that seemed hypothetical just a few years ago are already materializing, and researchers caution that no single lab should be writing the safety rulebook everyone else has to live by.

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Google DeepMind is funding research into the potential dangers of situations where millions of different AI agents interact with each other online.

According to Rohin Shah, who directs the company’s AGI safety and alignment research, the mass-market arrival of agents that can carry out tasks without human oversight and follow instructions given to them by other agents creates a whole new class of risk.

In an effort to address this, Google DeepMind—which made agent-based tools a centerpiece of Google I/O last month—has teamed up with several other organizations to announce a $10 million funding pot for researchers to study the behavior of multi-agent systems and come up with ways to prevent unsafe scenarios. Joining Google DeepMind are Schmidt Sciences, a philanthropic foundation set up by Eric and Wendy Schmidt; ARIA, the UK government’s moonshot agency; the Cooperative AI foundation, a UK-based nonprofit research outfit; and Google’s charitable arm, Google.org.

I asked Shah and James Fox, who leads the Science of Trustworthy AI program at Schmidt Sciences, what they hope to achieve with that $10 million. It’s no small sum, but it’s dwarfed by the budgets commanded by Google DeepMind’s own research teams.

The aim is to kick-start research outside tech companies, says Shah: “The strength of academia is that it can look really quite far into the future and do the kind of work that isn’t top of mind at industry labs.”

“The main issue is that there just isn’t really a field of research for multi-agent safety yet,” he adds. “And we would like there to be.”

The concern is that as more and more AI agents get deployed and begin working together, we could hit a tipping point where imagined scenarios become real. “We see this with humanity, too,” says Shah. “Our institutions can accomplish things that no individual human can.”

Shah thinks we have a few more months to go before agents are deployed throughout the economy in numbers that make potential risks a real concern. He wants to get ahead of that moment.

Risky business

What risks are we talking about, exactly? The possibilities that Shah and Fox have in mind mostly boil down to supercharged versions of bad things that happen on the internet already: scams, prompt injections (where an AI agent is fed malicious instructions, turning it into a self-guiding piece of malware), other forms of cyberattack. We look at what humans do now and ask what the agent version of that would be, says Shah.  

“We’ve got this digital commons that is integral to how society works, and you really want to ensure that this doesn’t descend into just absolute anarchy,” says Fox.

(I asked Shah if they were considering any worst-case scenarios more on the doomer end of the spectrum, such as widespread economic collapse. “Certainly not if we’re talking by the end of the year,” he said. That’s only six months away! He laughed. “Okay, a while after that.”)

Shah and Fox both think that the only way to understand what might happen when large numbers of multi-agent systems interact with each other is to run realistic simulations. They want researchers to drop AI agents into sandboxes and study what they do.

You can’t predict what’s going to happen by studying single agents, or even small groups of agents, in isolation. You can’t assume that AI agents underpinned by LLMs will always act rationally, says Fox. And the complexity comes from having huge numbers of interactions at once.

Some researchers, including a team at Google DeepMind, have argued that artificial general intelligence (if possible at all) could come not from a single super-smart model but from a kind of agent hive mind, where the capabilities of the whole add up to more than the sum of its parts.  

Lack of trust

Google DeepMind is not the only top AI firm warning about the risks of the technology it is building. A couple of weeks ago, Anthropic published guidelines for deploying AI agents based on an approach to cybersecurity known as zero trust, which starts with the assumption that a computer system is vulnerable, an agent is an attacker, and a breach will happen.

Refael Angel, cofounder and CTO of Akeyless, a cybersecurity firm based in Tel Aviv, agrees that understanding the new risks introduced by agent-based systems is crucial.  

Every approach to security in the past has assumed that the machine in question was software written by a human, doing fixed things on fixed paths, says Angel: “An agent breaks all of those assumptions. It reasons, it improvises, and it can be hijacked by a single sentence buried in a document it was asked to read.”

Angel welcomes this new funding. “No single lab should author the safety standards everyone else has to trust,” he says. But he cautions that safety researchers can overlook boring problems that are already here in favor of more exotic hypothetical ones.

And yet, Fox notes, risks that were hypothetical a few years ago are now very real: “The future’s come more quickly than perhaps expected.”