Use AI to Create Personal Podcasts
AI will change how we consume content. It will generate most of what we consume. Here's a great example you can do today.
The future of marketing:
The Marketer & AI:
1. Marketer crafts a prompt to create a content asset
2. AI improves on prompt and creates content asset
3. AI helps marketer to distribute asset to consumer
The Consumer & AI:
1. The Consumer gives AI access to it's media feeds
2. The Consumer gives AI instructions on specific media assets they want
3. AI creates one-to-one podcasts, newsletters and more.
Most people are talking about the first one of these. However, the seconds impact will be way more significant.
Yes, AI will be able to automate a lot of marketing tasks. It leads to a lot of channel saturation.
But, the second part of this changes who consumes everything marketers create.
Today, an increasing amount of media I consume is created by AI just for me:
- Personalized podcasts. The only consumer is me
- Personalized newsletters. The only consumer is me
- Personalized courses. The only consumer is me
You get it; AI is consuming the content I used to and turning it into media just for me.
That's the most crucial AI trend for marketers - AI consumes the content we publish and uses it to create content tailored for a single person.
It's hard to break through the noise and reach people if the consumer of your content is AI.
AI Use Case: Personalized Podcasts
Here's a very simple, but incredibly powerful example of personalized media in action.
1. Deep Research (OpenAI) / Deep Think (DeepSeek) / Gemini Flash 2.0
You want a prompt that will provide you with a well researched project on your topic of choice. You can use any of the deep research tools (I've been using the below with OpenAI Deep Research).
Prompt:
Conduct in-depth research on [INSERT TOPIC] and present the findings in the following structured format:
1. Introduction to [INSERT TOPIC]
Provide an overview of the topic, explaining its fundamental concepts and real-world applications.
Define key terms and principles relevant to understanding the topic.
Contextualize the topic's significance in its respective field (e.g., finance, technology, health, etc.).
2. Current Knowledge Level & Learning Path
Assuming the user is at the [Insert Level], I will outline the steps to progress to next level e.g. Beginner -> Novice -> Expert
Key skills, knowledge, and frameworks required at each stage.
Common challenges and misconceptions beginners face when learning about call options.
3. Case Studies & Real-World Examples
Multiple case studies from the past five years of successful call option trades.
Breakdown of why these trades were successful, the strategies used, and lessons learned.
Inclusion of quantitative analysis, historical context, and industry insights.
4. Current Trends & Potential Opportunities
Analysis of the present landscape of call options trading.
Emerging opportunities in the market, with data-driven reasoning on their potential.
Highlighting key trends, innovations, and risks associated with call options trading.
5. Key Takeaways & Mastery Guide
Summary of the most critical insights and best practices for mastering call options.
Actionable steps for continued learning and improvement.
Recommendations for trading platforms available in Ireland.
Common pitfalls to avoid as one advances in call options trading.
Add the output to a Google doc.
2. Notebook LM
Go to Google's Notebook LM. Simply add the Google Doc as the source for a new project. And you'll get an entire podcast episode in the audio overview. Example is for learning about ‘Call Options’.
But, here's the best thing, you don't just get a personalized podcast, you get a podcast with a call-in option and you're the only caller. Check it out.
Today, AI can already create personalised media for us, it's going to just keep getting better. I suspect in a couple of years, a lot of the content we consume will have been created just for us.
Until Next Time,
Happy AI’fying,
Kieran