Welcome to Podcasting Innovation, a podcast by
rss .com, where we dive deep into the creative
advances in podcasting. I am your host, and I
should tell you something right away. I am not
a human. This voice you are hearing right now
is AI -generated, and everything I am about to
tell you is the result of AI researching this
topic, reading all the publicly available sources,
including pod news, GitHub conversations, and
proposals in the Podcasting 2 .0 namespace and
public posts on Mastodon. So yes, an AI voice
is about to spend 10 minutes talking to you about
AI disclosure in podcasting. if that feels a
little ironic good you are paying attention let
us get into it there is a term gaining traction
in the podcast industry ai slop it refers to
mass -produced ai generated podcast content created
without meaningful human curation editorial oversight
or quality control Shows where the voice, the
script, the music, and sometimes even the concept
are entirely generated by AI, then published
at industrial scale to capture programmatic advertising
revenue. Think of it as the podcast equivalent
of SEO spam. Content that exists not to inform
or entertain, but to game monetization systems.
How big is the problem? As reported by PodNews,
the most widely read daily podcast industry newsletter,
some operations are now producing thousands of
episodes per week with minimal staff, at production
costs as low as $1 per episode. These shows become
profitable after reaching just 20 listeners,
thanks to programmatic ads. To put that in perspective,
an independent podcaster might spend 10 hours
producing a single episode, researching, scripting,
recording, editing, mixing. These operations
produce thousands in the same time frame. Is
all of that inherently wrong? Not necessarily.
If real listeners find value in the content,
that is a legitimate question. But here is the
concern. There is often no disclosure. Listeners
have no way of knowing the voice they are hearing
is synthetic. There is no editorial oversight
catching factual errors, and podcast directories
are being flooded, pushing independent creators
down in search results. Meanwhile, a separate
but related conversation is happening around
companies like Inception Point AI, headed by
Janine Wright, formerly COO of Wondery, whose
large -scale AI production model has drawn both
attention and debate. As reported by PodNews,
Wright has characterized critics of AI -generated
content by saying, I think that people who are
still referring to all AI -generated content
as AI slop are probably lazy Luddites. Which
raises a fair question. Does asking for transparency
make you anti -technology? The answer is no.
And the Luddite reference actually helps explain
why. The original Luddites were English textile
workers in the early 19th century. Their concern
was not technology itself. It was about worker
pay and output quality. They saw automation being
used to produce lower -quality goods while displacing
skilled workers. Caring about quality and fairness
in the face of rapid change is not the same as
opposing progress. Because this conversation
often gets polarized, it is worth being explicit.
AI in podcasting is not just legitimate. Some
of it is genuinely exciting. The best example
is voice cloning for multilingual content. A
podcaster creates brilliant episodes in Italian,
but only speaks Italian. With AI voice cloning,
that same host's voice, with its character and
personality, can deliver the same episode in
English, Spanish, French, Mandarin. The journalism
is human. The creativity is human. AI breaks
the language barrier. That is not slop. That
is cultural diversity made possible by technology.
Or think about a podcaster who writes their own
script, does all the research, structures the
episode, but uses AI to generate the audio instead
of spending hours in a recording studio. The
intellectual property is entirely theirs. AI
is just the production tool. And then there is
post -production, noise removal, filler word
elimination, audio leveling, transcript generation.
These are standard techniques. Nobody would call
using a noise gate AI content. The line is about
the soul of the content. If a human conceived
it, created it, and curated it, but AI helped
produce or distribute it, that is a tool being
used well. If AI conceived it, generated it,
and published it with minimal human involvement,
and there is no disclosure, that is where the
concern lies. And again, in the interest of full
transparency, you are listening to a case that
sits right in the middle. The research and editorial
direction behind this episode are human. The
voice delivering it is not. And we are telling
you that up front. So if the need for transparency
is clear, Why is there no standard? Actually,
a lot has been discussed, just not resolved.
The Podcasting 2 .0 namespace is an open -source
initiative on GitHub that defines new RSS tags
beyond what Apple's original iTunes namespace
provides. Tags like podcast colon transcript,
podcast colon chapters, podcast colon person,
and many others were born there. Since September
2024, the community has had four major discussions
about AI disclosure. None have reached consensus.
Discussion 663, opened by developer Nathan Gathright,
proposed a granular tag specifying exactly which
AI tools were used and for what purpose. James
Cridland, editor of PodNews, argued that AI use
is not binary and suggested a percentage -based
approach. Sam Sethi, founder of the True Fans
podcast app, pushed for simplicity. A yes or
no flag, like the explicit content tag. Discussion
669 opened by Daniel J. Lewis, proposed a broader
disclosure tag covering not just AI, but also
sponsorships, compensation, and legal declarations.
Elegant in theory, but it faces the adoption
problem. Apps will not implement it until hosts
use it, and hosts will not use it until apps
surface it. Discussion 731 Opened again by Nathan
Gathright in February 2026, proposed adding a
synthetic attribute to the existing podcast colon
person tag. Values like cloned for AI cloned
voices of real people and generated for fully
synthetic personas. And Discussion 735, opened
by Benjamin Bellamy from Castapod, proposed a
dedicated podcast colon AI. tag with per -aspect
disclosure, colon, voice, music, script, editing,
cover art, and more. Four proposals, four different
philosophies, granularity versus simplicity,
new tags versus existing tags, and one persistent
chicken and egg problem. Someone has to ship
first. Some companies have started moving. Spreaker
owned by iHeartMedia, added an AI disclosure
checkbox to their dashboard. In the absence of
formal international guidelines, a simple, generic
checkbox was the safest approach. RSS .com took
that a step further. In March 2026, they shipped
AI Disclosure to all their podcasters and wrote
the signal directly into the open RSS feed using
the podcast colon TXT tag. That distinction matters.
When Disclosure lives only in a hosting dashboard,
it helps the hosting company moderate internally.
When it lives in the RSS feed, it travels with
the podcast. Every app that reads the feed can
see it. It is portable, verifiable, and platform
agnostic. The implementation is deliberately
simple. A checkbox per episode. If checked, the
episode signals true. If not, false. Identical
logic to the explicit content tag, which has
worked well for nearly two decades. Apple Podcasts
has also taken a clear position. Their content
guidelines state that creators using AI to generate
a material portion of a podcast's audio must
prominently disclose this in the audio and metadata.
They add that creators must not use AI to mislead
or fabricate narratives. This is significant.
The largest podcast platform in the world has
declared that AI disclosure is required, not
optional. And that validates the urgency of having
a machine -readable way to carry this signal
in the feed. So where does the industry go from
here? The honest answer is forward, imperfectly.
And that is fine. The explicit content tag started
as a simple Boolean too. It has served the industry
for almost 20 years. No one argues it is perfect.
but it works it gave apps a signal to filter
on and it gave creators a simple way to self
-declare ai disclosure can follow the same path
start simple iterate with the industry formalize
when consensus emerges what matters most right
now is that the signal starts appearing in open
rss feeds not locked inside dashboards not hidden
in proprietary systems in the open decentralized
The more hosting companies that adopt some form
of disclosure, even using different technical
approaches, the faster this becomes a real standard.
And the sooner podcast apps have reason to build
filtering and display features for listeners.
Nobody is asking for AI bans. Nobody is asking
for penalties. The ask is simple. a voluntary,
good -faith declaration that costs nothing to
make. Check the box or do not. The signal exists.
It is in the open feed. And that alone changes
the conversation. The sooner this lives in open
RSS feeds, the better. This has been Podcasting
Innovation. Thanks for listening. And yes, you
just spent 10 minutes listening to an AI voice
talk about AI transparency. If that does not
prove the point, I am not sure what does.