The AI industry pumps out so much hype that you’d be forgiven for simply shrugging at the recent release and sudden withdrawal of Anthropic’s Fable 5 model. Set against the Elon Musk vs. Sam Altman trial and Anthropic locking antlers with the Department of War, the Fable episode could read like just another week in AI.

This one is worth paying attention to. This is really the first time the government has stepped in to regulate a specific model release on the grounds that its capabilities could pose a national security risk in the wrong hands. Whatever happens next, the line has been drawn: a frontier model in general release can be taken off the board because Washington decides it’s too dangerous to leave widely available.

For anyone building AI into their daily work, that shifts the calculation in a real way. The intelligence available to you isn’t only a function of price anymore. It’s also a function of policy, geography, the terms you’re willing to accept on your data, and whether the vendor or the government leaves the model running at all.

The story behind the freeze

For readers who don’t track model releases closely, here’s the short version. Fable 5 is the first generally available model in what the company is calling its “Mythos-class” models, a tier above Opus that Anthropic says has crossed a meaningful risk threshold in cybersecurity and biology. Fable 5 is the consumer-safe version, built on the same underlying Mythos 5 model but wrapped in extra guardrails designed to block or downgrade certain cyber, biology, chemistry, and model-development queries. It also jumps Anthropic’s core model number, signaling a generational step forward from Opus 4.8, Sonnet 4.6, and Haiku 4.5.

Then, on June 12, three days after launch, the government ordered Anthropic to block Fable 5 and Mythos 5 from every foreign national, including foreign-national employees working inside the United States. Anthropic said it could not reliably enforce that distinction and disabled both models globally. The trigger, by most accounts, was a suspected jailbreak that punched through Fable’s cybersecurity guardrails. Anthropic disputed the severity of the finding, saying the demonstration uncovered only minor, previously known vulnerabilities that other public models could also identify.

That fight is still going on. Cybersecurity leaders have urged the government to reverse the order, arguing that defenders need access to the same capabilities and that comparable tools are already available from American and Chinese competitors. Anthropic is working to get Fable back online, and rival labs will almost certainly ship something comparable in short order (some are already claiming to have done so).

The specific dispute may resolve in days or weeks. The precedent will outlast it. A model can be released, integrated into workflows, and then disappear because a government draws a line around who may use it. For anyone building around a single model or vendor (and “building” might simply be leveraging it in crucial, strategic use cases), availability is now part of the risk calculation.

What people saw before the lights went out

Early users got just enough time with Fable 5 to confirm Anthropic’s claims about it. Despite controversies over how Anthropic chose to limit how Fable 5 deals with queries the company deems risky (more on that in a minute), users are seeing the power of the model. Fable 5 is designed for agentic work, meaning it can work autonomously on tasks for a long time, sometimes hours or days, without losing context. The advice that came out of those early sessions was consistent: stop using frontier AI like a fancy autocomplete. The best way to use it, many say, is not to ask it to perform straightforward in-and-out tasks like writing an essay or telling you the best parts of a lengthy report, but to give it broader goals about what you’re trying to achieve, let it build the plan, then execute, however long it takes.

That window was short, but it counted. It showed this level of intelligence is no longer a slide in a research deck. The model was pulled back, but the capability threshold remains crossed.

A big part of what makes Fable work is that it grades its own homework. If you’re a regular user of Anthropic’s models, you’ll notice there’s no “Thinking” mode for Fable 5. That’s because adaptive thinking is always on: The model decides when and how much to reason on every request, and at higher effort levels it can reflect on and validate its own work. Tasks turn into loops. As it works to achieve the goal, it can try things, evaluate the results, change course as needed, and try again. And it can do so autonomously.

For media and marketing teams, the practical shift is in scope. Instead of, say, assigning it to design a specific email campaign, or help format your newsletter, you can zoom out and tell Fable 5 to conceive and build an entire marketing strategy around your newsletter. That might involve reformatting your templates, building new landing pages, adjusting the publishing schedule, building a social campaign, and more. Theoretically, with the right access, it could then build all of that for you. Your job is to grade the output. Over time, less of that grading happens mid-process and more of it happens at the end.

That’s the promise anyway. The danger is that organizations may begin designing around that promise before access, cost, and governance are stable enough to support it.

Fable 5 is the first model that puts real agency on the table. Right now, working with agents, while powerful, involves a lot of management: ensuring the plan the agent builds is correct, clearing up barriers that it encounters as it performs the task, and then guiding it to the best output, usually through multiple iterations on the task itself. In theory, a model strong enough to evaluate its own intermediate work shouldn’t need that hand-holding.

That gap between theory and practice is the real story of the freeze. For a few days, users could test a different relationship with AI; then the capability vanished. We crossed the threshold in the lab and lost it in the market on the same week.

The three walls between you and frontier intelligence

Fable 5 and the models that will follow it stand to change how we work with AI, and arguably how we work, full stop. However, using Fable 5 to its full potential was never just a matter of selecting it in your model picker or calling the API and letting it cook. The pullback put a sharper point on a problem that was already there: the most capable models are also the hardest to actually deploy. I see three walls in the way, with a fourth that just got built.

1. Access and context. For an organization to use Fable 5 to its full potential, it would require a large amount of access to the right context (the org’s information and data). Here, Fable’s strength tripped over itself. Because Anthropic fears the model could be misused, it requires prompts and outputs from Mythos-class models to be retained for at least 30 days for safety monitoring, including in enterprise environments that would otherwise use zero data retention. Anthropic says the data will not be used to train models and that, on some third-party platforms, it remains inside the customer’s cloud environment. But companies cannot use Fable 5 under a true zero-retention arrangement.
   
   That retention requirement, plus the restricted categories where Fable 5 quietly throttles down to Opus 4.8, has set off real friction with enterprise buyers. Many companies will be reluctant to cede control over how their own data is retained and reviewed. Microsoft reportedly limited employee access while its legal teams assessed the implications for confidential and customer data.
   
   And on top of all that sits the new wall. Even if a company accepts the privacy terms, secures the integrations, and builds the right internal controls, the model can still disappear because of a government order or vendor decision. Serious agentic systems will need fallback models, portability across vendors, and a plan for what happens when the most capable model is suddenly unavailable.
2. Compute. Fable 5 is not cheap. Anthropic priced it at $10 per million input tokens and $50 per million output tokens, twice the price of Opus 4.8. I’ve written before about how the agent era is squeezing compute budgets at every layer, and with AI hardening into a political wedge issue, expect compute pressure to stay tight for months and probably years.
   
   The premium price doesn’t automatically kill the math. Some early users argued that it could solve hard tasks in fewer turns than weaker models, potentially lowering the total cost of completing the work. Still, that argument only holds if the work was worth doing with a frontier model in the first place.
   
   If Fable 5 and its peers are going to act as the brains at the top of a company’s AI stack, the deployment question is going to need actual rigor. Organizations will need to be very selective of how to deploy it: which tasks to assign to it, who should have access, and what guidelines, rules, and restrictions there need to be on usage.
   
   And there’s an awkward irony in talking about allocation right now. Intelligence can be technically achievable and commercially valuable while still being unavailable.
3. Task imagination. I became aware of the term “task imagination” through the AI Daily Brief podcast, which references a video by the AI strategist Nate B. Jones. In his take on the Fable 5 release, Jones makes the simple observation that not many knowledge workers think about their work in terms of tasks that might take days to do. It requires a certain level of strategic thinking that may not actually apply to many roles. Put bluntly: a model can run for two days, but most workers have never been asked to define a goal worth two days of machine effort.
   
   For media practitioners, that’s the part worth sitting with. An editor might call on the model to develop more granular editorial guidelines and style guides based on different article types (news, features, evergreen explainers, etc.). Reporters might build investigative agents that don’t just surface data in document troves, but develop research plans based on leads and then execute on them by mining remote databases, filing FOIA requests, and other complex touchpoints that typically require human involvement.
   
   The catch is that most jobs aren’t scoped that way. Many jobs have narrow definitions of what the work is, and there’s little motivation to go beyond that. A model that can do days of work isn’t very useful if the work it’s given is still measured in minutes. That puts pressure on workers to imagine more ambitious tasks or risk being left behind.

The paradox of a pause

The Fable 5 pause comes wrapped in a paradox. A pause gives organizations time to build the governance, data practices, and strategic habits needed to use this level of intelligence responsibly. The trouble is, task imagination only develops with hands on the model. Without access, people cannot discover which long-running assignments are worth the money, where agents fail, or how their own roles could expand around them. The pause buys time while taking away the main way to use that time well.

Step back, and a clearer picture forms. A future where we’re working alongside agents will encounter serious barriers beyond just capability (and political freak-outs over that capability). We restrict access to context so neither the tool nor its creators knows too much. We limit how much we spend on models because we’re unsure of the return we’ll get. And many of us throttle our ambition with AI since our jobs simply don’t have a rich enough canvas for a model like Fable 5 to fill in.

A fourth restriction now sits on top of those three: the model itself may simply not be available.

For media leaders trying to make the ROI case, that’s a problem. The strongest demonstrations depend on giving capable models real work, real context, and enough time to execute. When the most capable models are pricey, hemmed in, or suddenly absent, teams drift to safer pilots that are easier to approve and unlikely to move the underlying economics.

None of these walls fall just because someone ships a smarter model. While advancements in security, infrastructure, and work redefinition will help us get past them, those are inherently slower than the rapid advancement of AI.

We pushed past one threshold and walked straight into several walls. I suspect the story of Fable 5 will be looked back on not primarily as a step up in power, but as the moment where the implications of that power pushed the limits of the systems meant to use it. Agentic AI is clearly where this is going. The systems around it need a beat to catch up.

The pause is useful, but it isn’t free. Experimentation is how organizations learn what this intelligence is actually for. For now, AI leaders are about to discover that running frontier AI at full strength is harder than proving the strength exists. The pure experimentation phase is over. The reality check phase has started, and access, cost, control, and utility now matter every bit as much as raw intelligence does.

A version of this column appears in Fast Company.

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The European Commission has published a voluntary Code of Practice on Transparency of AI-Generated Content, giving AI providers and deployers a concrete path to compliance with the AI Act’s labeling requirements—and a clear reason to sign up.

The code, released June 10, 2026, covers two broad categories of obligations. Section 1 targets providers of generative AI systems, requiring them to mark outputs—audio, image, video, and text—in machine-readable formats and ensure their detection as artificially generated or manipulated. The technical solutions must be effective, interoperable, and reliable “as far as technically feasible,” factoring in content type, implementation costs, and the state of the art. Section 2 targets deployers, requiring them to label deepfakes (audio, image, or video that falsely appears authentic) and disclose AI-generated or manipulated text publications on matters of public interest.

The Commission also released a set of standard icons that deployers can use to label AI-generated content. Nicholas Diakopoulos, a professor at Northwestern University, shared them on LinkedIn:

The code is currently under adequacy assessment by the Commission and the AI Board. Once it clears that review, signatories can rely on its measures to demonstrate compliance with Article 50 of the AI Act, reducing administrative burden and gaining legal predictability across all EU member states. Non-signatories will have to demonstrate adequate compliance individually, assessed case-by-case by national market surveillance authorities.

Signatories also gain access to Signatory Taskforces: working groups set up to share implementation practices and advance marking and detection techniques across the value chain.

The code is described as a “consistent, practical and proportionate” implementation framework, not a replacement for the AI Act or the Commission’s forthcoming guidelines on Article 50’s scope.

The code was developed over three drafting rounds between September 2025 and June 2026, led by an independent chair and vice-chair. Participants included AI system providers, detection developers, industry associations, civil society organizations, academic experts, and organizations with expertise in transparency and very large online platforms. International and European observers also contributed without voting rights. Two dedicated working groups handled the providers and deployers tracks separately.

Key milestones included a first drafting round starting November 5, 2025, a second round in January 2026, a third round in March 2026, and a closing plenary on June 10, 2026—the same day the code was published.

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Look, I’m going to be straight with you. The traditional article is powerful. But it’s only one way to present your reporting.

You spend weeks on an investigation. You publish 3,000 sharp words. But what happens to the data behind it? The full timeline? The quotes that didn’t fit?

What if you could turn that investigation into an interactive experience, complete with clickable timelines, hover-activated charts and tagged insights, in about 20 minutes?

With vibe coding, it’s possible.

What is vibe coding?

The term vibe coding came out of developer culture, but it is no longer just for developers. It’s for anyone who wants to tell a story that harnesses the power of coding.

When you vibe code, you’re building an application with the help of AI by focusing on what you want it to do. Rather than coding with HTML, JavaScript or other technical languages, a builder describes the user experience in plain language to a Large Language Model (or LLM).

You might type a prompt like, “Build me an interactive timeline showing [x, y, z] events.”

Here’s what worked for the journalists I’ve seen succeed with vibe coding:

• Step 1: Pick something simple. Don’t try to rebuild your entire investigation. Start with one article, dataset or interview.
• Step 2: Use a basic prompt structure, like: “Build an interactive [website/dashboard/story] that shows [your content] in [style you want]. Focus on [what matters most].”
• Step 3: After you have something simple, iterate 3-5 times. First pass: structure. Second: visual style. Third: functionality. Fourth: polish.
• Step 4: Share your creation with a colleague. Don’t talk, just watch. See if they click around. If they get stuck, that means you built it for yourself, not your audience. Time to iterate again or start over.

Why vibe coding and journalism make sense

When I taught vibe coding through the Google News Initiative AI Lab, I watched journalists with zero coding experience build interactive financial dashboards, data visualizations and branded microsites, all in about 90 minutes.

“This would have taken our dev team a month,” one person told me. “I did it during our session.”

While you can move quickly to an initial application with vibe coding, you still want to get your product or development support staff on board before launching. The real benefit is that vibe coding lets you prototype faster to see if your idea works before needing to commit resources.

This matters because most newsrooms don’t have a developer on speed dial. At the Adirondack Explorer, a small regional outlet covering New York’s Adirondack Park, journalists are building a civic information product that aggregates town meeting recordings, transcripts and minutes across dozens of municipalities. That kind of project would normally require hiring contractors or a dedicated dev team. Instead, their reporters are building it themselves.

When I worked with VTDigger through the Google News Initiative, they automated campaign emails across four audience segments, work that directly generated $40,000 in donations that likely wouldn’t have happened with manual effort alone. Vibe coding turns “we can’t afford to build that” into “let me show you what I made this morning.”

Think of vibe coding as a creative prototyping partner. Get to 80% quickly. Then decide if you need developer support to get to 100%.

Tips for those who are new to vibe coding

I’m repeating this because it’s important: Start simple. Pick one piece of your big investigation. It could be a dense PDF that needs to be more accessible. It could be data sitting in a spreadsheet. It could even be an interview transcript with great quotes that couldn’t all make it into the article.

When describing what I want to the LLM, I get experimental with my prompts. I’ll type things like, “use the most modern UI and UX interactions and animations to make my charts and graphs more interesting and allow me to parse through the data visually.” Or, “build this in the style of a high-end investigative journalism piece meets Wired magazine’s data viz.”

Then I iterate with edits like, “change the color scheme to match our brand,” or, “pull out more quotes from the sources,” or even, “make [x, y, z] section more prominent.”

Three high-level no-no’s when vibe coding

1. Never use it for final production without verification.
2. Don't use it for anything requiring real-time data without a proper backend.
3. Never publish AI-generated content without independent verification. In high-stakes areas like health, legal, financial or public safety, errors can cause real harm.

A Google AI Overview recently told pancreatic cancer patients to avoid high-fat foods, which is the exact opposite of what oncologists recommend and could jeopardize a patient’s ability to tolerate chemotherapy.

AI can generate beautiful visualizations, but it can also confidently present wrong numbers. For anything where errors could harm your readers, verify everything against primary sources.

Vibe coding tools to try

The main platform I use for vibe coding is Lovable.dev. For a simple interactive graphic such as a timeline, searchable transcript or basic data visualization, you can expect to use roughly 3-8 credits to produce a solid prototype.

More complex builds with multiple views, filtering or light database features can take 15-30 credits depending on how much you iterate. The free tier is typically enough to experiment with small projects, while paid plans make sense if you’re building regularly or refining more advanced applications.

Bolt.new is another tool worth knowing. For a simple interactive project, such as a timeline or basic data visualization, you might use roughly 20,000 to 60,000 tokens depending on how much you iterate. More complex builds with custom logic, multiple components or repeated revisions can exceed 100,000 tokens. The free tier is generally sufficient for small experiments, while larger or ongoing projects may require a paid plan.

Bolt tends to give you more control over the code and works well if you want to edit things directly. Lovable is more beginner-friendly with a cleaner interface for non-technical users.

Both tools let you attach content like article text, CSV files or transcripts, describe what you want in plain language, and get a working prototype you can publish immediately.

You might wonder why you need these tools when you already have ChatGPT or Claude. The difference is output.

When you ask ChatGPT to build you a dashboard, it gives you code snippets you’d need to assemble yourself, often requiring a developer to make sense of it. When you ask Lovable the same thing, you get a working application with a live preview, hosting, and a chat interface to iterate on it.

Lovable is actually powered by Claude, but it wraps the AI in a full-stack builder that handles deployment, databases and design. For journalists without coding experience, that’s the difference between “here’s some code” and “here’s how it looks.”

Go build something new

The article format has served us well. It’s not dead, but it is not the only option.

When thousands of people fly to a conference, share incredible insights and then go home, that knowledge evaporates unless it’s transformed into something people can continue to engage with.

Vibe coding lets us do that better than text-only articles can. Not just for conferences, but for city council meetings, investigative data, community journalism and breaking news.

My hope is you’ll take these vibe coding tips and run. You’ll build interactive story formats your newsroom has never seen. You’ll prototype tools that solve real problems. You’ll make journalism more engaging, more accessible and more honest about its data and sources.

Journalists who can write, build, prototype, ship and transform their own work into new formats will define what news looks like in five years. So, go vibe code something. I can’t wait to see what you build.

This post first appeared in News Media Help Desk.

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The Centre Daily Times in State College, PA, has voted to unionize after months of pushback against its parent company’s AI tool—a move that, according to The NewsGuild-CWA, makes it the first newsroom in the union to cite AI adoption concerns as a primary reason for organizing.

As Nieman Lab reported, the Centre Daily Times staff voted to join The NewsGuild of Greater Philadelphia last month. All eligible editorial staff signed authorization cards, and McClatchy voluntarily recognized the union. The catalyst, reporters told Nieman Lab, was McClatchy’s Content Scaling Agent (CSA) tool—an AI system that repackages existing articles into short-form summaries for publication or video scripts—and a March internal meeting where Kathy Vetter, McClatchy’s chief of staff for local news, told staff the company would use their bylines on AI-generated content unless union contracts prohibited it.

Josh Moyer, a senior reporter at the Centre Daily Times, took that as a signal. “It was essentially like, if you’re not in a union, your byline gets used; if you are in a union, we’ll follow what the union says,” Moyer told Nieman Lab. “If we want to control what happens to our byline, that’s the company telling us that we need to form a union.”

McClatchy introduced the CSA tool at the paper earlier this year. Reporters initially published at least one CSA-assisted story per week under a generic byline noting AI assistance. But in late February, management changed the policy: AI-generated content would now carry the reporter’s actual name. Reporters objected that it misrepresented their work to readers.

“When our names go on a thing, it says that this article or video is from that person, but that is just not true in this case,” said Trebor Maitin, a service reporter. Maitin was the first reporter at the paper to have his byline changed to reflect AI assistance.

The NewsGuild-CWA’s president, Jon Schleuss, said unionized newsrooms have had more success keeping AI content clearly labeled: “Unionized newsrooms are the ones where McClatchy’s AI slop gets a clear label. In non-union newsrooms, the AI slop may be carrying a real human reporter’s byline.”

Multiple McClatchy publications have seen byline strikes over the CSA tool, and some have taken labor actions over the tool and related workplace issues. For the Centre Daily Times, the union opens the door to formal collective bargaining and the ability to join coordinated actions at sister publications.

“Some of us use AI a lot more and are okay with it,” Maitin said. “But there is an overall understanding that we need to be able to have a say in this, and that unionizing at least gives us a seat at the table.”

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Reuters and Time are blocking all AI bots by default and only letting approved crawlers through—a whitelist approach that more publishers are adopting as the volume of unauthorized scraping grows.

As Digiday reports, both publishers moved to block AI bots last month, joining People Inc. and The Atlantic, which adopted similar strategies earlier this year and late last year respectively. The goal is simple: content costs money to produce, and AI companies have been taking it without paying.

“We saw that there was an imbalance between the value that publishers like Reuters provide and the value that Reuters receives in kind, and so instead we went from a default allow-all to a default disallow all,” said Josh London, head of Reuters Professional, which oversees the direct-to-consumer and direct-to-professional businesses. Reuters has since signed AI licensing agreements with Microsoft and Meta, according to the report.

The publishers aren’t relying on any single tool. Reuters uses robots.txt files, a method that is voluntary and non-binding, and one that many AI bots simply ignore. The approach is meant to create friction and signal that access requires negotiation. “If you want this, let’s have a conversation and then we can allow you to access,” said Alphonse Hardel, head of agency at Reuters, who leads the content licensing business.

Time allows roughly 70 bots on its site, ranging from AI lab crawlers and social platforms to its own operational systems. The volume of bot traffic has become significant enough that Time sees it as leverage for a future AI visibility product it’s developing for brand clients.

The economics are also shifting. Blocking bots cuts server costs: Hardel said the expense of the bot-blocking vendor can be nearly offset by the reduction in non-human traffic. At People Inc., the shift from a block list to an allow list meant going from blocking roughly 2,100 user agents to over 30,000, said Lindsay Van Kirk, the company’s SVP of innovation, speaking at an IAB Tech Lab event in May.

“Adding two full seconds of latency to the majority of scrapers when you implement a block-all-bots approach is a really good thing, even if they have to go through,” Van Kirk said. “Every scraper who has to pay a home proxy network in order to get access to the content is margin that you are taking out of their business.”

The IAB Tech Lab has published guidance on bot management, and the SPUR Coalition—a publisher group formed earlier this year with major news organizations—announced significant new membership as it works to create technical standards for AI licensing and content protection.

For Reuters, the change hasn’t reduced site traffic. After monitoring bot activity over an extended period, the company had enough data to identify which bots it could block without hurting revenue. The publisher maintains a public robots.txt file that lists approved bots, a benchmark that also supports enforcement discussions, said Phil Andraos, general manager of Reuters Digital.

“It’s not a set it and forget it approach,” London said. “The value of content is something that we ignore at our own peril, especially as AI scales.”

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A German court has ruled that Google is directly liable for what its AI-generated search overviews say, in a decision that legal observers say could ripple far beyond Germany. As The Decoder reported, the Regional Court of Munich hit Google with a temporary injunction barring it from spreading false claims about two Munich-based publishers through its AI Overviews.

At the center of the ruling is a distinction the court drew sharply: AI Overviews are not search results. They are Google’s own content.

According to the court, Google’s AI Overviews had falsely tied the two publishing companies to scams, subscription traps, and shady business practices for certain search queries. The AI mixed up information about genuinely sketchy companies with the plaintiffs and drew connections that appeared in none of the linked sources. The publishers sent a cease-and-desist letter; Google didn’t respond appropriately, the court found.

The judges classified Google as a direct infringer because the overview “rewrites and judges results in its own words and according to its own structure.” In the case at hand, the AI opened with confident assertions like “Yes, [company] is known for dubious business practices,” then assembled its own summary, red flags, and user tips. Because Google built the AI, offered it, and controls its algorithms, the court ruled, Google owns what it produces.

Crucially, the court found that existing case law shielding search engines doesn’t apply. Germany’s Federal Court of Justice had previously granted traditional search engines limited liability because they merely point to outside websites. But AI Overviews generate “independent, new, and substantive statements,” the Munich court said, and only Google is positioned to check them against the underlying sources.

Google’s defense—that users can check the linked sources themselves and generally know not to blindly trust AI—fell flat. The court ruled that the ability to disprove a statement through further research doesn’t exempt a publisher from liability, drawing a parallel to press law, where outlets are liable for standalone teasers even if readers never click through. The reasoning is bolstered by research showing users almost never click source links in AI Overviews.

The court also weakened free speech protections for AI output, writing that an AI’s opinion is “not the expression of an acquired conviction” but “the result of an algorithm” and largely an expression of Google’s business interests.

Google was ordered to cover 80% of the legal costs, with the plaintiffs paying 10 percent each. The court said the ruling may have international reach.

The decision lands as scrutiny of AI accuracy intensifies. An analysis by AI startup Oumi for The New York Times found Google’s AI Overviews, running the current Gemini 3 model, answered correctly 91% of the time. At Google’s scale, that still means millions of wrong answers every hour—and a legal exposure that could extend to rivals like ChatGPT, Claude, and Perplexity.

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Two years ago, if anyone had told me the most AI-hostile demographic in 2026 would turn out to be Gen Z, I would have laughed. The generation that grew up with screens in its hands seemed like the obvious early adopter, ready to use the tools to look more skilled, more productive, and more employable than everyone else.

Instead, May commencement season turned into an open revolt. At the University of Arizona, students booed Google chairman Eric Schmidt for pitching AI’s world-changing potential. Gloria Caulfield, VP of strategic alliances for the investment firm and real estate developer Tavistock, drew the same reaction at the University of Central Florida when she compared the rise of AI with the Industrial Revolution. At Middle Tennessee State University, students shouted down Big Machine Records CEO Scott Borchetta for the offense of saying the word out loud.

The numbers back up the booing. A recent Gallup poll measuring AI adoption and attitudes among Gen Zers found the share who say they’re excited about AI fell from 36% to 22% in a year. The share who say they feel anger toward AI climbed from 22% to 31%. Older cohorts are skeptical too, but a sentiment swing this sharp inside the youngest part of the workforce is something I’ve never seen for a new technology this early in its lifecycle.

That swing matters because the AI industry has a serious PR problem at the worst possible moment. Anti-AI sentiment is hardening as the midterms approach, and politicians are picking up data centers as a wedge issue, supporting efforts to halt or slow the build-out of the facilities that fuel AI with the computing power it needs to function. If capacity can’t keep up with demand, the cost of compute will keep rising, and that will put hard ceilings on what newsrooms, marketing teams, and comms shops can actually do with AI in the year ahead.

The infrastructure squeeze is already here

It’s already happening. Anyone who runs Claude as part of their daily workflow knows the rhythm of the outages, and Anthropic’s own status dashboard tells the story in red over the past 90 days. The Claude Code boom has driven demand through the roof in 2026, and the company is scrambling to keep up. Anthropic signed a deal to buy computing power from Elon Musk’s SpaceX, and at the same time it closed the loophole that let builders run third-party software on top of their Claude subscriptions. Some of those setups were burning thousands of dollars of compute against a $200-a-month Claude Max plan. Now those teams have to use Anthropic’s platform directly or move to pay-as-you-go.

The angry posts in response were predictable, but the more useful read on the change is that it forced builders to reckon with the actual cost of what they were running. The choices are probably familiar to anyone trying to budget AI spend: switch to a cheaper model, possibly an open-source one, rebuild on Anthropic’s own platform, or shut the project down.

This was always going to happen at some point. As demand grows, free-compute workarounds will keep closing. The industry’s argument is that the squeeze would hurt less if compute were cheap and plentiful, which is the case being made for trillion-dollar infrastructure projects like OpenAI’s Stargate. For AI to deliver on its promises, compute has to flow like water. That means more data centers, and more power plants behind them.

Which loops back to why Gen Z is angry in the first place. Environmental concerns are near the top of their list, and AI’s energy footprint has only gotten harder to ignore since I wrote about it months ago. This isn’t a fight confined to politicians and podcasters anymore. At the companies I advise on AI adoption, employee surveys keep surfacing the same worry, and in some cases it’s starting to shape whether teams use AI at all.

Governance is the new AI strategy

You can argue about whether the pollution and water concerns are overblown. The cost question is harder to wave off. The leaders running the most ambitious AI programs are past the era of handing every employee a ChatGPT seat. They want agentic workflows, automated processes, and rapid prototyping through vibe coding, and they may be telling their engineers to get obsessed with “tokenmaxxing.”

It’s unclear so far how data center politics will play out. What leaders can actually control right now is governance. That’s more than running training sessions on which model does what, although that matters. Real governance is the balance between experimentation and direction. People need room to invent their own workflows, and the organization needs a way to make sure the compute it’s paying for is being put to good use. That doesn’t just mean “keeping costs down” it means accepting that the bill will sometimes be high and being confident the outcome will be worth it.

Through my consulting work with media companies and PR agencies, I’ve watched this play out in practice. One agency I worked with piloted a vibe-coding tool. Usage spiked early as employees tested its limits, and several different teams ended up building near-duplicate prototypes. The thing that saved them was high-bandwidth communication. They ran regular workshops and project reviews, learned from their people, and steered the work as they went. They eventually homed in on the use cases that actually delivered, in their case automating media intelligence, and the experiment surfaced something unexpected. The original platform wasn’t the right one. The agency ended up adopting a different tool and sunsetting the one it started with.

That’s one of many examples, and the lesson behind all of them is the same. If AI agents are going to do real work for your team, they need to run compute-heavy jobs. Compute is going to stay expensive for a while. The way to avoid the kind of top-down restrictions that suffocate innovation is for leaders to define what success looks like up front, get their teams trained on the tools and models, and build systems that surface collaboration and catch waste before it compounds.

That is what good governance actually looks like. The political fight over AI and data centers isn’t going anywhere, but the companies leaning into AI can still find a way through. The goal is to work inside the real cost constraints while shielding the people doing the actual work from feeling them.

A version of this column appears in Fast Company.

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For the first time in the internet’s history, bots account for more web traffic than humans.

Cloudflare CEO Matthew Prince announced the milestone this week, according to Tom’s Hardware, noting that automated traffic has now eclipsed human-generated requests online, months ahead of even his own projections.

“Welp, that happened faster than I predicted,” Prince wrote on X. “Thought it would be end of 2027, then early 2027, but agentic traffic growing so fast that bots have now passed human traffic online for the first time in the Internet’s history.”

According to Cloudflare’s Radar data, bots represented roughly 57% of all HTTP requests as of late April 2026, with humans accounting for the remaining 43%. Bot traffic has held between 53% and 60% in the weeks since. Prince said the actual crossover occurred in the last few months, though the data is messy enough that pinning down an exact date is difficult.

The shift underscores how quickly AI agents have transformed web traffic patterns. Before the generative AI era, bot traffic sat at around 20% of all web activity, with Google’s web crawler serving as the largest single source. Now, AI agents performing tasks on behalf of users are generating requests at a scale that dwarfs human browsing behavior.

Prince illustrated the contrast at SXSW earlier this year: “If a human were doing a task—let’s say you were shopping for a digital camera—you might go to five websites. Your agent or the bot that’s doing that will often go to 1,000 times the number of sites that an actual human would visit. So it might go to 5,000 sites. And that’s real traffic, and that’s real load, which everyone is having to deal with and take into account.”

The reaction to Prince’s announcement was swift. Tech billionaire Elon Musk replied with a single “Wow” to the post.

The full picture is more nuanced. While bots now dominate HTML request traffic—reading pages, scraping content, indexing sites—humans still account for roughly 65% of total web activity when the metric expands to include app usage, video streaming, maps, and social media scrolling. Bots have overtaken humans in the specific act of navigating and reading the web, but not in the broader measure of people actually using the internet.

Cloudflare, which handles approximately one-fifth of all global web traffic, has been tracking the trend closely. The company’s 2026 Threat Intelligence Report also found that bots now account for 94% of all login attempts across its network, meaning only 6% of login attempts come from actual humans.

The crossing point Prince initially forecast for 2027 arrived in 2026. What once required a two-year runway happened in a matter of months.

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Sitecore is acquiring generative engine optimization (GEO) startup Scrunch for around $225 million, according to a Bloomberg report, adding AI answer-engine visibility to an enterprise content platform that has been quietly building toward a machine-readable web strategy.

Neither Sitecore nor Scrunch have confirmed the price. The deal marks one of the larger investments in the emerging GEO market: the practice of optimizing brand content so it surfaces in AI-generated answers rather than traditional search results.

Scrunch’s platform shows brands real-time signals about how they appear across various AI platforms, along with competitive analysis and technical audits. Its Agent Experience Platform, or AXP, is designed to deliver content in formats AI agents can read and use without disrupting the human experience. Notable clients include Lenovo, Skims, Headspace, and Penn State University.

“We’re at a pivotal moment where companies must rethink traditional digital strategies and accept that the internet must be written for machines to understand if we want humans to experience it,” Eric Stine, Sitecore’s CEO, said in a statement.

Scrunch CEO and cofounder Chris Andrew echoed the same urgency in his own statement. “By joining forces, we’re helping companies meet buyers where they are, moving beyond traditional SEO to win inside AI-generated answers,” he said. “That’s where Scrunch’s AXP is a critical advantage, delivering content in a format AI agents can read and use, without disrupting the human experience, allowing brands to become the trusted sources that power those answers.”

The GEO space is becoming increasingly competitive as brands seek visibility in the AI experiences where consumers are spending more time. Scrunch previously raised $26 million, including a $15 million Series A last summer led by Decibel, with participation from Mayfield, Homebrew, and others.

The deal logic is in the numbers. Scrunch told ADWEEK last year that conversion rates in AI search are three to five times higher than in traditional online search, citing its own data. “A visitor coming from AI search is buying faster than a traditional organic visitor,” Andrew said at the time. Independent verification of those figures was not provided.

Third-party research offers some corroboration. In research conducted by Akamai, AXP-enabled webpages saw a 364% lift in brand presence in responses to non-branded AI prompts and a 218% spike in citations appearing in AI responses.

Stine said the combination would allow brands to “show up with greater clarity, authority, and relevance so they can build trust, increase share of voice, and influence decisions early in the buying journey when it matters most.”

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UK publishers can now opt out of appearing in Google’s AI search results—the AI Overviews that appear at the top of many searches—and the regulator that made it happen says the point is to give publishers leverage to negotiate payment for their content.

According to the BBC, the Competition and Markets Authority, the UK’s official competition regulator, announced on Wednesday that websites based in the country can choose not to appear in Google’s AI Overviews, the AI-generated summaries that appear at the top of search results. Sites that opt out will not receive traffic or impressions from those generative AI features. The CMA called it a “world-first requirement” that puts publishers “in a stronger position to negotiate content deals with Google.”

The timing matters. Many publishers have seen significant traffic drops since Google moved traditional links down the results page and replaced them with AI summaries at the top. The opt-out mechanism is both a way to control traffic from AI as well as a negotiating lever. If a publisher removes itself from Google’s free AI distribution, the CMA’s position is that the same publisher can then demand payment to be included in AI results on different terms.

Google controls more than 90% of the online search market in the UK, according to the CMA. For almost three decades, websites and publishers have relied on Google’s search results to drive users to their businesses. That dependency is what the CMA’s requirement is designed to disrupt—at least in the AI layer.

The BBC quotes the regulator’s chief executive, Sarah Cardell, saying the requirement would result in “fair treatment, greater transparency and meaningful choice for businesses and consumers.” The CMA also said Google must properly attribute publishers’ content which appears in AI search results, with clear links back to their sites.

Google has nine months to bring all the changes in, but the CMA says it wants to see “important parts” of the requirements implemented earlier. The CMA has extra powers over Google and other large tech companies designated as having an influential position in the digital market, and it says it will be monitoring developments in Google search with the ability to act further if needed.

In a blog published the same day, Google said it was testing the new opt-out features in the UK first before rolling them out globally. The company said it was engaging with regulators “to ensure website owners have the right tools as user preferences evolve.”

The broader context is a shift in how people find information online. Some users have moved from traditional search engines to AI chatbots that produce answers based on information scraped from existing websites, often without driving traffic back to the source. The CMA’s intervention is an attempt to give publishers a seat at the table in a search landscape that has changed substantially since the last set of regulatory frameworks were designed.

Whether nine months is long enough to change the economic relationship between publishers and AI search platforms depends on how seriously both sides take the negotiating position the CMA is trying to create.

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