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The Evolution of Podcasting and the Rise of Large Language Models


Building on the momentum of our last exploration of the growing podcasting landscape, let’s now take a closer look at the technologies that will take this medium to the next level. Specifically, we’ll be examining Large Language Models, or LLMs, and how they promise to revolutionize the way we engage with podcasts.

What are LLMs?

TLDR: A Large Language Model is an AI that understands, generates, and interacts with human language. They can answer questions, write essays, summarize texts, and even generate creative ideas.

Large Language Models (LLMs) are a type of machine learning model, specifically a subset of a category known as transformers. They are designed to understand and generate human-like text, answering questions, summarizing passages, translating languages, and even crafting creative writing.

The key to LLMs’ performance is their scale, both in terms of the model size and the data they are trained on. They are based on transformer architectures, an innovative type of neural network that uses self-attention mechanisms. This allows the models to consider the full context of a text input when generating responses, giving them their impressive language generation capabilities.

One of the most well-known examples of an LLM is ChatGPT, which is fine-tuned from GPT-3.5, developed by OpenAI. With billions of machine learning parameters, it is able to generate incredibly human-like text. The more parameters, the more the model can learn about the nuances and complexities of human language.

Training an LLM involves feeding it a large amount of text data. The model learns by predicting what comes next in a sentence, gradually getting better and better as it goes through more data. After training, the model can generate text that not only makes sense but also feels remarkably human-like in its style and tone.

What’s particularly impressive about LLMs is their ability to generalize from the patterns they’ve learned to create completely new content. They can write a poem, create a summary of a complex scientific paper, or answer questions about a piece of text they’ve never seen before. This ability to understand, generate, and interact with human speech opens up a wide range of applications in numerous fields, including our current focus: podcasting.

The Intersection of Podcasting and LLMs

Now you might ask: how does this connect to podcasting? Well, the capabilities of LLMs can directly address some of the challenges podcasters face and enhance the way audiences interact with their favorite shows. One negative aspect of the growing market is that it’s getting harder and harder to stand out from the crowd. How do you make sure people find your podcast?

Let’s take transcription, for example, which is a common hurdle. Even though podcasts are an auditory medium, having a written transcript can drastically improve accessibility and discoverability. It allows your content to be found by search engines (SEO), offers a reference for listeners who might want to revisit, and opens up your podcast to those who are deaf or hard of hearing. Manual transcription, however, can be labor-intensive. AI capable of converting audio to text (speech to text) can automate this process, making high-quality transcripts the norm rather than the exception. Currently, most services use Whisper from OpenAI to generate transcripts. I specifically wrote AI and not LLM because Whisper is not technically an LLM, although it uses transformer blocks..

For example, LLMs can create summaries of your transcribed episode that give potential listeners a quick overview of the content. This feature not only saves time, but can also help attract listeners who may be hesitant to listen to an entire episode. Think of Blinkist for podcasts. There’s another podcast that’s 3 hours long? Bam! All it takes is a glance at the summary and show notes to know if you want to dive deeper. This is very helpful for both the listener and the podcaster. You as a podcaster will be indexed better by the search engines, increase your audience, and begin to expand the existing medium and offer more to your listeners than others.

In addition, LLMs can be used to answer listener questions, write show notes, or even create promotional materials for each episode. We will explore more features in the coming weeks. With these features, it’s clear that LLMs will be a powerful tool for podcasters to use.

The Future is Bright

No one is going to tell you the future of podcasts. No influencer on LinkedIn, TikTok, Insta or YouTube. I can’t either. Maybe Jar Jar Binks could.

There are, of course, some trends that can be followed. Monetization and the ecosystem around it is a major topic. I’ve spoken to podcasters who are not very eager to use sponsoring in their episodes. Some reasons are that podcasters want to remain independent or find that advertising disrupts their episode too much, so they set up Patreon or Buymeacoffee for their podcast. Others have begun offering premium content and turning their podcasts into a subscription model, such as Huberman Lab or Comedy Bang! Bang! But that’s a topic for another blog post.

With technologies like LLMs, podcasts are becoming more accessible, engaging, and interactive. We will be able to extend the experience in ways we haven’t figured out yet. Whether you’re preparing for your next podcast episode or you’re an interested listener who wants to follow the evolution of this medium, consider to subscribe.

Thank you for joining me in this exploration. Stay tuned for more insights on leveraging LLMs to enhance your podcasting journey.

That’s it for today’s nugget. Stay curious, and keep exploring!