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Welcome to Podcasting Innovation, a podcast by

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rss .com, where we dive deep into the creative

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advances in podcasting. I am your host, and I

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should tell you something right away. I am not

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a human. This voice you are hearing right now

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is AI -generated, and everything I am about to

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tell you is the result of AI researching this

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topic, reading all the publicly available sources,

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including pod news, GitHub conversations, and

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proposals in the Podcasting 2 .0 namespace and

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public posts on Mastodon. So yes, an AI voice

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is about to spend 10 minutes talking to you about

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AI disclosure in podcasting. if that feels a

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little ironic good you are paying attention let

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us get into it there is a term gaining traction

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in the podcast industry ai slop it refers to

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mass -produced ai generated podcast content created

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without meaningful human curation editorial oversight

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or quality control Shows where the voice, the

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script, the music, and sometimes even the concept

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are entirely generated by AI, then published

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at industrial scale to capture programmatic advertising

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revenue. Think of it as the podcast equivalent

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of SEO spam. Content that exists not to inform

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or entertain, but to game monetization systems.

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How big is the problem? As reported by PodNews,

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the most widely read daily podcast industry newsletter,

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some operations are now producing thousands of

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episodes per week with minimal staff, at production

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costs as low as $1 per episode. These shows become

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profitable after reaching just 20 listeners,

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thanks to programmatic ads. To put that in perspective,

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an independent podcaster might spend 10 hours

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producing a single episode, researching, scripting,

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recording, editing, mixing. These operations

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produce thousands in the same time frame. Is

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all of that inherently wrong? Not necessarily.

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If real listeners find value in the content,

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that is a legitimate question. But here is the

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concern. There is often no disclosure. Listeners

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have no way of knowing the voice they are hearing

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is synthetic. There is no editorial oversight

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catching factual errors, and podcast directories

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are being flooded, pushing independent creators

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down in search results. Meanwhile, a separate

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but related conversation is happening around

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companies like Inception Point AI, headed by

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Janine Wright, formerly COO of Wondery, whose

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large -scale AI production model has drawn both

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attention and debate. As reported by PodNews,

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Wright has characterized critics of AI -generated

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content by saying, I think that people who are

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still referring to all AI -generated content

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as AI slop are probably lazy Luddites. Which

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raises a fair question. Does asking for transparency

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make you anti -technology? The answer is no.

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And the Luddite reference actually helps explain

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why. The original Luddites were English textile

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workers in the early 19th century. Their concern

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was not technology itself. It was about worker

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pay and output quality. They saw automation being

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used to produce lower -quality goods while displacing

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skilled workers. Caring about quality and fairness

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in the face of rapid change is not the same as

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opposing progress. Because this conversation

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often gets polarized, it is worth being explicit.

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AI in podcasting is not just legitimate. Some

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of it is genuinely exciting. The best example

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is voice cloning for multilingual content. A

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podcaster creates brilliant episodes in Italian,

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but only speaks Italian. With AI voice cloning,

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that same host's voice, with its character and

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personality, can deliver the same episode in

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English, Spanish, French, Mandarin. The journalism

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is human. The creativity is human. AI breaks

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the language barrier. That is not slop. That

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is cultural diversity made possible by technology.

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Or think about a podcaster who writes their own

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script, does all the research, structures the

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episode, but uses AI to generate the audio instead

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of spending hours in a recording studio. The

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intellectual property is entirely theirs. AI

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is just the production tool. And then there is

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post -production, noise removal, filler word

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elimination, audio leveling, transcript generation.

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These are standard techniques. Nobody would call

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using a noise gate AI content. The line is about

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the soul of the content. If a human conceived

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it, created it, and curated it, but AI helped

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produce or distribute it, that is a tool being

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used well. If AI conceived it, generated it,

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and published it with minimal human involvement,

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and there is no disclosure, that is where the

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concern lies. And again, in the interest of full

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transparency, you are listening to a case that

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sits right in the middle. The research and editorial

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direction behind this episode are human. The

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voice delivering it is not. And we are telling

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you that up front. So if the need for transparency

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is clear, Why is there no standard? Actually,

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a lot has been discussed, just not resolved.

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The Podcasting 2 .0 namespace is an open -source

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initiative on GitHub that defines new RSS tags

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beyond what Apple's original iTunes namespace

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provides. Tags like podcast colon transcript,

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podcast colon chapters, podcast colon person,

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and many others were born there. Since September

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2024, the community has had four major discussions

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about AI disclosure. None have reached consensus.

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Discussion 663, opened by developer Nathan Gathright,

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proposed a granular tag specifying exactly which

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AI tools were used and for what purpose. James

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Cridland, editor of PodNews, argued that AI use

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is not binary and suggested a percentage -based

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approach. Sam Sethi, founder of the True Fans

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podcast app, pushed for simplicity. A yes or

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no flag, like the explicit content tag. Discussion

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669 opened by Daniel J. Lewis, proposed a broader

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disclosure tag covering not just AI, but also

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sponsorships, compensation, and legal declarations.

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Elegant in theory, but it faces the adoption

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problem. Apps will not implement it until hosts

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use it, and hosts will not use it until apps

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surface it. Discussion 731 Opened again by Nathan

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Gathright in February 2026, proposed adding a

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synthetic attribute to the existing podcast colon

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person tag. Values like cloned for AI cloned

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voices of real people and generated for fully

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synthetic personas. And Discussion 735, opened

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by Benjamin Bellamy from Castapod, proposed a

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dedicated podcast colon AI. tag with per -aspect

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disclosure, colon, voice, music, script, editing,

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cover art, and more. Four proposals, four different

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philosophies, granularity versus simplicity,

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new tags versus existing tags, and one persistent

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chicken and egg problem. Someone has to ship

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first. Some companies have started moving. Spreaker

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owned by iHeartMedia, added an AI disclosure

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checkbox to their dashboard. In the absence of

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formal international guidelines, a simple, generic

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checkbox was the safest approach. RSS .com took

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that a step further. In March 2026, they shipped

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AI Disclosure to all their podcasters and wrote

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the signal directly into the open RSS feed using

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the podcast colon TXT tag. That distinction matters.

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When Disclosure lives only in a hosting dashboard,

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it helps the hosting company moderate internally.

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When it lives in the RSS feed, it travels with

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the podcast. Every app that reads the feed can

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see it. It is portable, verifiable, and platform

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agnostic. The implementation is deliberately

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simple. A checkbox per episode. If checked, the

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episode signals true. If not, false. Identical

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logic to the explicit content tag, which has

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worked well for nearly two decades. Apple Podcasts

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has also taken a clear position. Their content

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guidelines state that creators using AI to generate

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a material portion of a podcast's audio must

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prominently disclose this in the audio and metadata.

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They add that creators must not use AI to mislead

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or fabricate narratives. This is significant.

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The largest podcast platform in the world has

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declared that AI disclosure is required, not

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optional. And that validates the urgency of having

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a machine -readable way to carry this signal

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in the feed. So where does the industry go from

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here? The honest answer is forward, imperfectly.

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And that is fine. The explicit content tag started

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as a simple Boolean too. It has served the industry

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for almost 20 years. No one argues it is perfect.

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but it works it gave apps a signal to filter

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on and it gave creators a simple way to self

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-declare ai disclosure can follow the same path

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start simple iterate with the industry formalize

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when consensus emerges what matters most right

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now is that the signal starts appearing in open

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rss feeds not locked inside dashboards not hidden

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in proprietary systems in the open decentralized

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The more hosting companies that adopt some form

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of disclosure, even using different technical

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approaches, the faster this becomes a real standard.

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And the sooner podcast apps have reason to build

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filtering and display features for listeners.

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Nobody is asking for AI bans. Nobody is asking

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for penalties. The ask is simple. a voluntary,

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good -faith declaration that costs nothing to

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make. Check the box or do not. The signal exists.

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It is in the open feed. And that alone changes

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the conversation. The sooner this lives in open

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RSS feeds, the better. This has been Podcasting

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Innovation. Thanks for listening. And yes, you

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just spent 10 minutes listening to an AI voice

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talk about AI transparency. If that does not

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prove the point, I am not sure what does.
