Your podcast episode is done. Sixty minutes of conversation, edited and polished. Now you need show notes.

This is where most podcasters hit a wall. You have to listen through the whole thing again, jotting down timestamps and pulling quotes, writing a summary that actually captures what you discussed. For a 45-minute episode, the show notes easily take another 90 minutes to two hours of focused work that has nothing to do with creating content.

AI tools flip this equation. Feed your audio to a transcription service, hand the transcript to an LLM, and get show notes in minutes. The catch is that minutes of AI output still requires your attention because the machine doesn’t know what mattered in your conversation.

The Invisible Podcast Problem

Google cannot listen to your audio. Neither can any other search engine. Your brilliant episode about startup marketing or sourdough techniques or whatever you cover might as well not exist to anyone searching for those topics.

Show notes fix this. They turn your audio into text that search engines can crawl, index, and serve to people looking for exactly what you talked about.

But the benefits go beyond SEO.

“Deaf and hard of hearing people want access to podcasts,” writes accessibility advocate Meryl Evans. “We want to be able to have the same opportunities as hearing people, to learn and grow, to be entertained, to be inspired.”

When Evans surveyed podcast listeners about accessibility barriers, 74.5% said they’d given up on shows because they couldn’t access the content. Transcripts and show notes aren’t just nice to have. They’re how entire audiences experience your work.

Then there’s the practical side. When someone wants to share that one insight from minute 37 of your episode, timestamps and summaries let them find it. Without that, the moment is effectively lost. They won’t scrub through 45 minutes of audio hunting for it.

What Manual Show Notes Actually Require

The traditional process looks something like this. You listen through the episode, often while doing something else so you miss parts, taking notes on key moments. You write timestamps for the sections you remember being good. You draft a summary that captures the episode without spoiling everything. You pull out quotable moments. You write an SEO description. Maybe you write social posts to promote it.

For weekly podcasters, this workflow eats 4-8 hours every month on administrative tasks that feel endless.

Solo podcaster Katie Harbath described the financial reality in a recent workflow breakdown: “I used to spend $100 per episode on editing. That’s not nothing, especially when you’re funding your podcast out of pocket.”

That hundred dollars per episode adds up fast when you’re publishing weekly. And editing is just one piece of the production puzzle.

How AI Changes the Math

The new workflow has fewer steps and takes a fraction of the time.

First, transcription. You upload your audio file to a tool like Descript, Otter, or one of the dozens of Whisper-based services. A 45-minute episode transcribes in 2-3 minutes. The cost is pennies per minute, not dollars. Accuracy typically lands around 95-98% depending on audio quality, background noise, and how clearly everyone speaks.

Jason Snell, who has been podcasting for over a decade, tested Whisper against older transcription methods and found it “staggeringly better” than anything he’d tried before. For Apple financial analyst calls, which are full of specialized terminology, “almost all of them were rendered correctly by Whisper.”

Second, generation. You take that transcript and ask an AI to create show notes. The prompt can be simple: summarize this, identify topic sections with timestamps, pull out quotable moments.

Third, review. You read through what the AI produced and fix the parts it got wrong. This is the step you cannot skip.

The 80% Problem

AI doesn’t finish the job. It gets you most of the way there.

“It feels like the transcripts are 75% of the way there, but still require a human to fix that last 25%,” wrote Justin Jackson after testing multiple AI podcast tools. “We’re not at the stage where we can have all of this on auto-pilot.”

Den Delimarsky, who built a custom transcription pipeline for his podcast, put it more directly: “For now, it gets me 80% of the way there, and I consider that to be a good start.”

That remaining 20-25% matters more than you’d think. Names get mangled. “Sean” becomes “Shawn.” Company names come out as nonsense. Technical terms get transcribed phonetically into gibberish. Your guest won’t appreciate being called by the wrong name in your public show notes.

AI also misses context. If you referenced something from a previous episode, the AI won’t catch that connection. If a moment was funny because of how someone said it, the transcript shows flat text. If you said something sarcastically that reads sincerely, the show notes might highlight it as a key insight when you’d rather it disappear.

The time savings come from AI handling the tedious parts: listening through, noting timestamps, drafting summaries. The quality comes from you handling the parts that require understanding what actually happened in the conversation.

Tool Options

Several tools focus specifically on this workflow.

Podsqueeze generated the most useful outputs in head-to-head testing, according to the Transistor review. Timestamps, titles, key quotes, and blog post drafts. The interface keeps things simple.

Castmagic had the most accurate transcripts with excellent speaker identification. The user experience felt polished. But transcripts sometimes misattributed large chunks of text to the wrong speaker, which defeats the purpose if you don’t catch it.

Descript was the fastest at producing a fairly accurate transcript. If you already edit your podcast in Descript, adding show notes is seamless since the transcript already exists. The tool can read your transcript and generate show notes without uploading anything new.

You can also use general-purpose AI tools. Get your transcript from any source, paste it into Claude or ChatGPT, and ask for show notes. The specific tool matters less than having a workflow that you’ll actually use.

Prompts That Work

For a summary, something like: “Here’s a podcast transcript. Write a 3-paragraph summary capturing the main topic, key insights, and who would benefit from listening.”

For timestamps: “Identify topic changes in this transcript. For each section, give the topic and timestamp in [MM:SS] format.”

For quotes: “Find the 5 most quotable moments from this transcript. Look for insights that stand alone and represent the value of the episode.”

For SEO: “Write an episode description under 200 words. Include the main topic, guest name, and 2-3 keywords people might search for.”

Verity Sangan, who uses ChatGPT for show notes on multiple podcasts, noted that results improve with practice: “I’ve used several times with gradually improving results.”

The improvement comes from refining your prompts based on what the AI gets wrong. If it keeps missing your intro segment, add instructions to skip the first two minutes. If it overemphasizes tangents, tell it to focus on the main topic thread.

What the Review Step Catches

AI makes errors that humans catch immediately.

The transcript might say your guest is from “Acme Corporation” when they actually said “AXA Corporation.” The AI might flag a throwaway joke as a key insight. The timestamp might be off by 30 seconds because the transcript markers weren’t perfectly aligned.

“Always, always, always, double-check the end result,” advises Lower Street’s AI podcasting guide. “Proofread for fact-checking or even general typos. Make sure to have a human eye give it a review. They can often make mistakes.”

The review also catches tone mismatches. AI writes in its voice, not yours. If your podcast has a casual, joking style, the AI will probably produce something that reads like a corporate summary. You’ll need to inject your personality, add your characteristic phrases, make it feel like an extension of your show rather than a generic description.

Picking Your Depth

Different podcasts need different show note styles.

For some shows, minimal works. Episode title, one paragraph summary, guest bio, 3-5 timestamps, links mentioned. Quick to produce. Gets the job done.

For podcasts chasing SEO, the blog-style format makes sense. A full article expanding on episode topics, embedded player, complete transcript, detailed timestamps. More work, but search engines have more to index.

For podcasts with active marketing, the comprehensive approach: summaries in multiple lengths, timestamp chapters, quotable moments with graphics, social posts for each platform, email copy. AI makes this feasible. Generate all assets from one transcript instead of doing each from scratch.

The Time Comparison

Manual approach for a 45-minute episode:

• Listen through: 45-60 minutes
• Notes during: parallel task
• Write summary: 15-20 minutes
• Create timestamps: 20-30 minutes
• Pull quotes: 15 minutes
• Write description: 10 minutes
• Total: roughly 90-120 minutes

AI-assisted approach:

• Transcription: 2-3 minutes (automated)
• Generate drafts: 5 minutes
• Review and edit: 10-15 minutes
• Total: roughly 15-20 minutes

That’s an hour or more saved per episode. Weekly podcasters save 50+ hours per year on show notes alone.

“It’s nice, especially when you’re tired, to have a service that makes recommendations, which you can edit and tweak,” Jackson wrote. “It does make the publishing process faster.”

The Stuff That Goes Wrong

Publishing without review is the most common mistake. AI makes errors. Timestamps drift. Names get wrong. Key points get missed. Your guest is a “Professor of Economics” and the AI calls them a “Professional Economist.” Small things that make you look like you didn’t pay attention.

Over-engineering comes second. You don’t need every possible asset for every episode. Start with what you’ll actually use. Add more as your workflow matures and you learn what drives engagement.

Ignoring audio quality creates downstream problems. Garbage in, garbage out. If your recording has background noise, crosstalk, or mumbled speech, transcription accuracy drops and everything built on that transcript inherits the errors.

Beyond Show Notes

Once you have a quality transcript, other content becomes straightforward.

Turn the show notes summary into a full blog post by expanding each section. Pull insights for your email newsletter. Create a week of social posts from one episode’s best moments. Use timestamps to identify which clips work for short-form video.

The transcript is the raw material. Show notes are one output. The same source feeds everything else.

The Part Nobody Talks About

AI tools for podcasting keep multiplying. New options launch monthly. The features blur together: transcription, show notes, social clips, blog drafts.

What actually matters is whether you ship episodes consistently with show notes attached. The specific tool doesn’t matter as much as having a workflow you stick with.

The 80% that AI handles frees you to focus on the 20% only you can do: knowing what mattered in your conversation, what your audience cares about, what represents your show accurately.

That’s the trade. The tedium gets automated. The judgment stays yours.