The polite request era is over

For years, Patreon relied on a simple digital handshake: a robots.txt file that politely asked AI crawlers not to scrape creator content. That didn’t work.

On Thursday, the membership platform announced it’s now actively blocking AI training bots — not just asking them to behave. The shift comes via an expanded partnership with Cloudflare, using the company’s AI Crawl Control technology to physically shut the door on unauthorized scrapers.

“Consent shouldn’t depend on whether a scraper chooses to behave,” Patreon wrote in a blog post announcing the change. The subtext is clear: many AI bots were ignoring the rules anyway.

Why robots.txt wasn’t cutting it

Patreon first put measures in place to deter AI crawlers back in 2023. But AI scraping has gotten more sophisticated since then. The company’s internal testing revealed something alarming: individual AI training crawlers were attempting to access Patreon thousands of times per week — despite the robots.txt file telling them not to.

After implementing active blocking, those attempts dropped to zero. That’s a strong signal that many AI companies were simply ignoring the standard web protocol and scraping creator content anyway.

Patreon’s paywall had historically kept much of its content out of reach of crawlers. But the company recently introduced new discovery tools — a redesigned Home Feed and tweet-like Quips — which potentially exposed more creator work to automated scraping. The timing of the crackdown isn’t accidental.

Cloudflare’s evolving anti-bot toolkit

Cloudflare has been steadily building out tools to give publishers more control over AI bots. The company now offers a marketplace that lets websites charge AI companies for scraping — a model it calls Pay Per Crawl. Earlier this month, Cloudflare changed its policies so that “mixed-use” crawlers (bots that both index content for search and train AI models) are blocked by default on any pages that host ads.

Patreon is extending its existing work with Cloudflare to use these updated enforcement tools. The key difference: instead of a passive request in a text file, Patreon is now actively blocking AI training bots at the network level.

“As AI agents become increasingly powerful and popular, creators deserve a meaningful say in how their work is used by AI companies,” said Patreon’s product chief Drew Rowny in the announcement. “On most of the Internet, creators have to accept AI training on their work just to reach and grow an audience. Patreon has a different vision.”

What this means for creators

The change is significant for the millions of creators who rely on Patreon for their income. Many have watched with unease as AI companies trained models on their work without permission or compensation.

Patreon’s new stance doesn’t block all bots. The company says it will still allow bots that index pages and organize information that can be used to send users back to Patreon. That means search engine crawlers and other legitimate indexing services can still operate. What’s blocked is the specific subset of bots that scrape content solely for AI training purposes.

Key details for creators

• Active blocking replaces passive requests via robots.txt
• Individual AI crawler attempts dropped from thousands to zero in testing
• Legitimate indexing bots are still allowed through
• New discovery tools (Home Feed, Quips) triggered the need for stronger protection

For creators who also use other platforms, similar protections are available through Cloudflare’s tools — though most individual creators won’t have direct access to enterprise-level infrastructure. Patreon’s move effectively extends enterprise-grade protection to its entire creator base.

The bigger picture: publishers vs. AI scrapers

Patreon’s move is part of a broader trend. More online publishers and content creators are grappling with how AI companies ingest their work to make models smarter. The legal landscape remains murky — courts are still sorting out whether training AI on publicly available content constitutes fair use or copyright infringement.

In the meantime, technical solutions like Cloudflare’s AI Crawl Control offer a practical stopgap. Patreon’s approach — actively blocking rather than politely requesting — may become the new normal across the web.

The company’s message is clear: creators should be able to grow their audience and control how their work is used. On most of the internet, those two goals are in tension. Patreon is betting that its platform can deliver both.

Meta is testing a wearable that never stops listening or watching

Meta’s next big idea in wearables is a doozy — and it’s already inside its test labs. According to a Financial Times report, the company is working on “super-sensing” AI glasses. The pitch? These frames would record every visual and audio cue around you. Constantly. Always.

That’s not a typo. The concept is an always-hearing, always-seeing device that never takes a break. And yes, it sounds like a privacy nightmare waiting to happen.

Meta’s logic goes like this: all that raw data — every conversation, every street scene, every coffee shop chat — would feed into a personal AI agent. Mark Zuckerberg has floated the idea of an assistant that’s with you 24/7, ready to answer anything. Not a generic chatbot that relies on static training data, but something that knows your real-world context because it’s been recording it.

But the road to a helpful AI runs straight through some very dangerous territory.

Privacy protections? Not if the LED stays off

Right now, Meta’s existing smart glasses — the Ray-Ban Meta frames — have a white ring light that activates whenever the camera is recording. It’s a deliberate tell, a way for bystanders to know they’re on film.

But those safeguards have already been cracked. Shortly after launch, reports emerged showing people could physically block or tamper with the LED indicator, recording video without anyone noticing. Meta responded by announcing a software update: if the LED is covered or disabled, the camera shuts down entirely.

So here’s the obvious question: if Meta builds super-sensing glasses that record nonstop, will that LED be glowing permanently? The answer, according to the FT report, is unsettling. Executives are currently planning not to activate the LED when super-sensing features are in use. Multiple people familiar with the matter confirmed that approach. The idea is to keep the recording discreet — which, they acknowledge, would make it much harder for bystanders to know they’re being captured.

Plans could change, the sources added. But the fact that this is even on the table is enough to raise alarms.

The tech is already ready — and it could come as a software update

Here’s the kicker: the super-sensing capability is reportedly ready to deploy today. It wouldn’t require new hardware. A simple software update could activate it on Meta’s existing line of smart glasses.

That means millions of people already wearing Ray-Ban Meta frames could, in theory, have their devices turned into always-on recording machines with zero new hardware purchases.

What Meta plans to do with your data

Meta knows this sounds bad. So it’s floating a compromise. According to the report, the company is considering a system where:

• Recorded audio and video data is not available for users to download
• Meta itself would not receive the raw recordings
• Instead, metadata extracted from those clips and images would be fed into Meta AI to answer your questions

Sounds cleaner, right? Until you read the next line: Meta is also mulling the possibility of using that recorded data to train its own AI models. That’s a whole other layer of privacy concern — your daily life, your conversations, your private moments, all potentially becoming training fodder for a corporate AI.

Why this is different from your phone’s always-on mic

You might argue: my phone already listens for “Hey Siri” or “OK Google” all day. What’s the difference?

Scale and form factor. Your phone sits in your pocket or on a table. These glasses sit on your face, pointed at everything you see, pointed at everyone you talk to. The social contract changes when a wearable is involved. People don’t expect a pair of glasses to be recording them. They expect a phone to be a phone.

There’s also the tampering issue. Even if Meta ships these with a strict no-LED-tampering policy, the cat is already out of the bag: people have proven they can defeat the indicator. An always-on recording mode with a potentially disabled LED is a recipe for abuse — both by bad actors and by well-meaning users who don’t realize they’re violating someone’s privacy.

What comes next for Meta’s smart glasses

Meta is betting big on wearables as the next computing platform. The Ray-Ban Meta smart glasses have already sold better than expected, and the company sees AI as the killer app. A personal agent that knows your life because it’s been recording it — that’s the vision.

But the company is walking a tightrope. On one side, you have the utility of a truly context-aware assistant. On the other, you have the very real risk of turning a fashion accessory into a surveillance device.

The super-sensing glasses aren’t a product yet. They’re a prototype, a concept being tested behind closed doors. But the fact that Meta is actively planning to keep the LED off during recording suggests the company knows exactly how controversial this would be — and is willing to push ahead anyway.

For now, the LED on your Ray-Ban Meta glasses still lights up when you record. For now. Whether it stays that way depends on how much pushback Meta gets before the super-sensing update goes live.

Why public health agencies are turning to generative AI

A new initiative called PULSE will let 10 US public health jurisdictions trial generative AI tools from OpenAI and Anthropic. The goal? Figure out what works — and what doesn’t — before the technology spreads further.

The program, formally named the Public Health Use Case and Learning Scaling Engine, is backed by the Coalition for Health AI (CHAI), Accenture, and the two AI companies. It will run across state, local, tribal, and territorial health agencies.

OpenAI and Anthropic have each donated 10 enterprise licenses, giving up to 2,000 public health practitioners access to their commercial AI products. Accenture will handle participant onboarding and help build playbooks from the trial results.

“Every major technological transformation succeeds or fails based on trust, governance and execution,” said Dr. David Lakey, former Texas health commissioner, in a statement. “PULSE will support agencies in this endeavour, and is specifically designed for practical implementation.”

Five focus areas for the pilot

CHAI’s leadership council will pick the participating jurisdictions. Practitioners will then be grouped into communities tackling five specific use cases:

• Biosurveillance and drug-wave prediction — spotting disease outbreaks and tracking illicit drug trends.
• Social determinants of health (SDoH) mapping — using AI to identify how housing, income, and environment affect community health.
• Operations and community-feedback analysis — automating the review of public comments and internal workflows.
• Public communications and multilingual translation — generating health messages in multiple languages.
• Automated clinical-data retrieval and FHIR query engine — pulling electronic health records using the FHIR standard.

Notably, CHAI hasn’t specified which OpenAI or Anthropic products will be used, nor the model versions or configurations. The announcement also leaves unclear how the two providers will be assigned across the pilots.

What about FHIR and human oversight?

FHIR — an HL7 standard for exchanging healthcare data electronically — features in the clinical-data retrieval use case. But the announcement doesn’t define exactly how generative AI fits into that workflow. Will the models write queries, fetch records, summarize results, or do all three?

It also doesn’t say whether staff will check for incorrect queries, incomplete retrievals, or unsupported summaries before using the information. That’s a critical gap, especially for applications that could involve demographic, geographic, clinical, or population-health data.

CHAI hasn’t disclosed whether the pilots will use identifiable records, de-identified information, synthetic data, or aggregated datasets. That distinction matters for compliance with the US Health Insurance Portability and Accountability Act (HIPAA).

HIPAA and data protection: what’s missing

The US Department of Health and Human Services requires organizations covered by HIPAA to protect electronic health information. Its cloud-computing guidance says regulated entities and service providers must meet HIPAA rules when cloud systems create, receive, maintain, or transmit electronic protected health information.

But HIPAA won’t apply to every PULSE participant or workflow — it depends on the agency, the data involved, and the function being performed. The announcement doesn’t set out retention periods, access controls, audit arrangements, or rules for submitting protected health information.

OpenAI says inputs and outputs from its business services — including ChatGPT Enterprise and its API — are not used to train or improve its models by default. Anthropic makes a similar claim. However, those policies don’t define how the PULSE deployments will be configured in practice.

“We believe AI should be useful, safe and accessible to the people tackling society’s most important challenges,” said Felipe Millon, OpenAI’s head of government go-to-market. He added that the donated licenses were designed to help public health organizations evaluate the tools through a structured process.

Governance and evaluation remain vague

The pilots are scheduled to begin in autumn 2026. CHAI expects to release the resulting playbooks in 2027, which other public health agencies can use as reference material.

But CHAI hasn’t published the measures it will use to assess the pilots. It hasn’t explained whether each use case will be evaluated under separate technical, operational, privacy, and safety criteria. The announcement also doesn’t detail how model outputs will be reviewed — whether staff must approve generated public communications, verify translations, validate retrieved clinical information, or check biosurveillance outputs before use.

The US National Institute of Standards and Technology (NIST) recommends identifying which AI functions need human oversight and training users to understand system performance and limitations. Its generative AI guidance also covers testing, validation, monitoring, documentation, privacy, and management oversight.

“Public health teams are being asked to do more with less, and AI can help — as long as it’s brought in with care and the right guardrails,” said Elizabeth Kelly, Anthropic’s head of beneficial deployments. She said PULSE would let practitioners test the tools in their own environments with privacy, governance, and responsible-use measures built in from the start.

Who can participate — and what’s still unknown

Eligible participants include state and territorial health departments, county and municipal agencies, tribal authorities, Indian health organizations, and large city health departments. But CHAI hasn’t specified minimum staffing, infrastructure, interoperability, or cybersecurity requirements for participating jurisdictions.

Data from the National Association of County and City Health Officials, cited by CHAI, shows nearly 40% of local health departments aren’t using AI at all. The coalition said some departments are interested in revising workflows and improving operational efficiency.

PULSE plans to convert findings from 10 jurisdictions into guidance for wider use. Yet the announcement doesn’t explain how the playbooks will account for differences in agency size, technical systems, legal responsibilities, staffing, or procurement arrangements.

It also doesn’t say whether outputs from biosurveillance, drug-wave prediction, or clinical-data retrieval will be used only for testing, presented to staff for review, or incorporated into operational workflows.

“We know AI is going to reshape how we deliver public health — the question is whether we do it thoughtfully or not,” said Dr. Ashish Jha, a former White House COVID-19 response coordinator. He said the program would test which applications work and document the findings for other agencies.

Broader context: CHAI’s governance work

PULSE is part of CHAI’s larger effort on governance standards for healthcare AI. In May, the organization announced plans to develop guidance covering eight governance areas through workshops and working groups involving more than 150 healthcare AI representatives. It has since started publishing playbooks on organizational AI policies, governance structures, and internal resources.

Separately, CHAI has worked with the Joint Commission on governance playbooks aligned with its voluntary Responsible Use of AI in Healthcare certification. The PULSE announcement doesn’t state that participating public health agencies will be assessed under that certification.

Dr. Brian Anderson, chief executive of CHAI, said public health agencies entered the COVID-19 pandemic after years of limited investment in technology. He said PULSE was intended to give agencies practical experience with AI before wider implementation.

For more on how AI is being applied in healthcare settings, read our coverage of Bunkerhill’s $55M raise for agentic AI across health systems. And if you’re interested in the broader AI landscape, check out our analysis of AI and big data trends in healthcare.