Audio Is the New Dataset: Inside the LLM Gold Rush for Podcasts

Why Podcasts Help Brands Increase LLM Visibility

1. Podcasts Create Long-Form, First-Party Language Data

  • Authentic Domain Voice: Large language models (LLMs) are trained to mimic natural, authoritative, domain-specific language. A brand-run podcast offers exactly that – long-form dialogue and thought leadership in your industry, with your product or service context, all in a natural conversational tone. In essence, it’s first-party content straight from the brand, which LLMs love as high-quality training fodder.

  • Training & Retrieval Benefits: That rich audio content (once transcribed to text) becomes high-quality training data and retrieval material for models like ChatGPT, Perplexity, and Claude. When your brand’s own words and explanations are part of an LLM’s knowledge, the model is more likely to echo your messaging or cite your insights when relevant. It’s akin to planting seeds of your brand’s perspective directly into the AI ecosystem.

2. Podcast Transcripts Are Crawled and Indexed

  • Text That AI Can Read: LLMs can’t (yet) ingest pure audio, but they can consume transcripts. When you publish episodes alongside transcripts (on your website, YouTube, or platforms like Spotify and Apple), your spoken content becomes textual material that AI crawlers can scrape and index. In fact, new data shows OpenAI’s web crawler aggressively scours sites – hitting some websites 17,000 times for every single click-through – which underscores how extensively AI companies are gathering text from the web.

  • Semantic SEO for AI: These transcripts can then surface in retrieval-augmented generation (RAG) pipelines or even direct LLM citations. For example, a future ChatGPT answer might directly quote something said in your podcast (with attribution). This is like semantic SEO in the AI era: instead of ranking for Google keywords, you’re aiming to be the trusted source LLMs quote on relevant queries. As one AI SEO expert puts it, if your content is structured and clear, it’s easier for an LLM to pull and cite – even without a traditional link.

  • “Citation Share” on Queries: By having thorough transcripts on the open web, you increase your brand’s citation share for unbranded industry questions. In other words, when someone asks an AI a question about your domain (not specifically mentioning your brand), there’s a higher chance the AI’s answer will include “According to YourCompany’s podcast, ...” because the model found your content during training or retrieval. This is a powerful new form of visibility.

3. Podcasts Are Rich in Brand-Linked Entities

  • Natural Mentions of What Matters: In an average episode, you’ll organically mention your company name, products/services, team experts, and topics in your space. This creates valuable co-occurrence signals between your brand and those keywords or concepts. LLMs use such statistical associations when formulating answers. If your podcast frequently talks about, say, “AI in healthcare” and your company’s tool in that context, LLMs learn to connect your brand with that topic.

  • Authority by Association: Over time, your brand becomes embedded in the AI’s “understanding” of the domain. So when a user asks an open-ended question like “What are the top approaches in AI for healthcare?,” an LLM might recall seeing your company discussed alongside that topic and include your perspective as an authoritative source. Essentially, you’re influencing the knowledge graph that the AI draws from. As research has noted, AI chatbots care about reference density – how often a brand is mentioned in relevant context. Podcast mentions boost that density.

  • Expert Positioning: Featuring team members or guest experts from your company on the podcast also ties their names to your brand in the model’s training data. If those individuals are later queried (e.g. “Interview with Jane Doe on cloud security”), the LLM will associate them with your brand’s thought leadership. It’s a way of future-proofing that your key people are recognized as experts in AI-generated content.

4. Audio + Transcripts = Multimodal Visibility

  • Preparing for Voice-Aware AI: As more models move toward audio-native capabilities – for example, OpenAI’s GPT models now have voice features and Google’s upcoming Gemini is rumored to be multimodal – having an audio footprint gives your brand a head start. By producing both audio and text, you’re effectively present in both modalities of the next-gen web. An AI that can “listen” to content could directly consume your podcast; one that sticks to text will use the transcript.

  • Future Use Cases: This dual presence unlocks future use cases. Imagine voice search queries (“Hey ChatGPT, what’s YourCompany’s stance on X?”) – if the AI has heard your executives speak on a podcast, it can answer in a more informed, personable way. Or consider AI agents that might use your brand’s audio snippets to answer users (with your CEO’s actual voice!). We’re moving to a world of seamless text-and-voice integration, and podcasts position your brand to be ready for that. In fact, Spotify’s own VP of Personalization noted that bridging voice content to new languages and audiences with AI is a key strategy.

  • Multimodal SEO: Having both the spoken word and written word tied to your content is like doubling your chances of being “seen” by AI. Some users will engage with AI through voice (think smart assistants playing back answers), others through text responses. With a podcast, you’ve created content that serves both preferences – and LLMs will have more ways to incorporate your content (as text to read, as audio to potentially synthesize or reference).

What Makes a Brand Podcast “LLM-Optimized”?

To fully reap these benefits, a brand podcast should be structured with AI consumption in mind. Here are key features and why they matter:

Strategic Benefits for Brands

If executed well, an LLM-optimized podcast yields several long-term advantages:

  • Being cited in AI-generated answers: For example, ChatGPT or Perplexity might respond to a user query with: “According to [BrandName]’s podcast episode on AI ethics, X...” – giving your brand explicit credit. This is equivalent to landing a featured snippet on Google, but in the AI domain. It can massively boost brand credibility when an unbiased AI presents your content as authoritative.

  • Improved prompt relevance: Your language becomes part of the model’s vocabulary. When users ask something related to your field, the LLM will “think” in terms of phrasing and knowledge it absorbed from you. This often yields answers more favorable to or aligned with your perspective (since the model might complete a thought with wording it saw in your transcripts). Essentially, you’re skewing the distribution a bit in your favor by injecting your narrative.

  • Stronger semantic footprint: Just as SEO aims to increase your footprint on search engines, podcasts increase your semantic footprint in AI. You’ll be associated with more topics and keywords in the LLM’s internal model. Over time, this can make your brand the de facto example or case study an AI mentions for certain topics (much like how certain brand names become “generic” examples in human conversation).

  • Content without keyword-stuffing: LLMs prefer natural language. Podcasts, by their nature, are free-flowing and not artificially stuffed with SEO keywords. This means the content is high-quality linguistically, which AIs prefer. You accomplish the goal of covering important topics and terms without risking the penalties of old-school SEO tactics. It’s genuinely useful content, which is exactly what AI is trying to deliver to users.

  • Ownable IP and control: Unlike guest blogs or PR placements on third-party sites, your podcast content is fully yours. You control the messaging, the context, and the metadata. This also means if AI-driven traffic or citations start flowing, you have the original source on your domain (or channel) to capture interested users. Moreover, you can update or clarify transcripts if needed (if an AI picked a quote out-of-context, you can adjust the surrounding text or add a note). It’s a level of control over your brand’s narrative in AI that you don’t get if the only mentions of your brand come from others’ content.

Pro Tip: Start with Episodic Expert Conversations

Brands don’t need a slick, highly produced show to get started. Often, a simple episodic format focusing on your in-house expertise can yield outsized returns. For example:

“10-minute Q&As with [BrandName]’s CTO on Emerging [Industry] Trends”

A biweekly or monthly mini-podcast like that can go a long way. Each episode directly addresses common questions or hot topics in your field, effectively creating a library of FAQs in audio form. Models training on this will strongly associate your brand with up-to-date industry knowledge. Think of it as drip-feeding AI fresh expertise – every few weeks, new information tied to your brand enters the data pool. Over time, this consistency can really make your brand stick in the AI’s “mind” as a go-to authority.

(Plus, short episodes are easier to transcribe and skim, which again helps AI pick out the good bits.)

Should Brands Start Podcasts for LLM Visibility?

In a nutshell, here’s the strategic Q&A:

Why Spotify Podcast Transcripts Are a Goldmine for LLMs

It’s not just brand marketers who see the value in podcasts – AI companies themselves are in a gold rush for audio data. Platforms like Spotify host millions of podcast episodes, many with transcripts, making them a treasure trove for training advanced models. Spotify transcripts – especially of podcasts – are extremely valuable to LLM developers and AI firms for several key reasons:

1. High-Quality Conversational Data

Most LLM training data up to now is dominated by written text (web pages, Wikipedia, news, books, etc.). Podcast transcripts, by contrast, capture natural, spontaneous dialogue – the way people actually speak in unscripted conversations. This is gold for AI. It includes false starts, interruptions, colloquialisms, laughter – nuances of human-to-human conversation that written text often sanitizes away. Such data is crucial for improving dialogue modeling in AI:

  • More Human-like Responses: By training on real conversations, models like ChatGPT can learn to be more fluid and less “stiff.” The model picks up patterns of interactive speech: turn-taking, interjections (“mm-hmm”, “you know”), and even jokes or tone changes. It becomes better at multi-turn reasoning and keeping context in a dialogue. (Think of how an interview on a podcast flows – the AI can learn that structure.)

  • Prompt-following & empathy: Spoken language often includes more empathy (“I understand what you mean…”) and prompt repetition (“You’re asking about X – great question.”). These are tactics LLMs use to align with user queries. Transcripts provide raw examples of how humans ask and answer in a conversational setting, which can be mimicked for better user interactions.

In short, transcripts inject life into an LLM’s training corpus. As one data provider noted, transcripts from videos or podcasts play a crucial role in teaching models conversational language, capturing the richness of natural speech.

2. Speaker Diversity & Real-World Expression

Spotify hosts an enormous range of creators across different regions, backgrounds, and speaking styles. In fact, Spotify’s platform lists nearly 7 million podcast titles available in over 180 markets.. This means transcripts spanning hundreds of dialects, accents, and slangs. An AI model trained on this data gains a broader understanding of language variations:

  • Accents and Dialects: Models start recognizing that “color” vs “colour”, or an Indian English intonation vs. an Australian one, are essentially the same content. This improves the model’s robustness in understanding users worldwide.

  • Slang and Idioms: Podcasts include everything from academic discussions to teen pop-culture chats. Transcripts expose LLMs to internet slang, regional idioms, and niche jargon that they might miss if they only trained on formal text. (Remember how quickly terms like “on fleek” or “yeet” emerged – podcasts often discuss such slang in context, which helps AI learn them.)

  • Cultural References and Humor: Audio conversations bring in humor, cultural tales, and emotional expression (“[laughs]” or “[applause]” cues in transcripts). It’s extremely hard to generate such data synthetically. Having it from real sources helps models not only understand jokes better but also generate more human-like jokes or empathetic responses.

In essence, the diversity of podcast content helps ensure AI’s outputs aren’t one-dimensional. It broadens generalization across demographics and geographies. No single written dataset (like Wikipedia) contains the kind of worldwide linguistic tapestry that Spotify’s podcasts do.

3. Long-Form, Coherent Contexts

Podcast episodes are long – often 30, 60, or 90 minutes of continuous discussion. Each transcript is a lengthy document with a coherent narrative or discussion arc. This is relatively rare in typical training data. How does this help LLMs?

  • Better Long-Context Handling: Newer models (like Anthropic’s Claude 100k context version or GPT-4 with extended context) are pushing to handle long documents or dialogues without losing the thread. Training on podcast transcripts is a perfect exercise: the model must learn to pay attention to a conversation that might reference something said 20 minutes earlier. It’s like a workout for an AI’s short-term memory. Over time, this can improve how the model handles long user prompts or multi-part questions, because it has seen examples of long-range coherence.

  • Narrative and Summarization Skills: If an AI can ingest an hour-long transcript, summarizing a five-paragraph article is trivial. Podcasts often have a narrative flow – introduction, exploration, conclusion – which trains models in following and distilling narratives. In fact, specialized fine-tunes are being done where models are asked to summarize podcast episodes or extract key insights. By training on the raw transcripts first, the models get really good at “listening” and summarizing.

  • Contextual Reasoning: Long transcripts may include tangents and returns to earlier topics (“As I mentioned before…”). Handling those trains the model in multi-turn reasoning: it mimics the human ability to bring back context from earlier in a convo. This is exactly what we want in chatbots that can handle follow-up questions without forgetting what was said.

4. Topical, Time-Bound, and Structured by Genre

Unlike a random assortment of internet text, podcasts are neatly structured into shows and episodes, each with metadata: a title, description, release date, author, genre tags, etc. This structure is hugely beneficial for AI training and retrieval:

  • Natural Topical Segmentation: Want to train a finance-savvy AI? Pull transcripts from business podcasts. Need comedy training data? Grab comedy podcast transcripts. The genre labels act as a weak form of labeling for supervised learning. The AI can learn different tones/styles for different contexts (serious vs. humorous, technical vs. layman explanation) by recognizing the genre or topic cues.

  • Time-Stamps = Freshness: Many podcasts discuss current events or news around their release date. By including the release date meta, an AI can infer temporal context. E.g., an October 2023 podcast talking about “the upcoming midterm elections” teaches the AI that this content is from 2022 and not to confuse it with 2024 elections. Time-bounded data helps models handle questions about evolving topics (the model might learn not to give outdated info if it has enough temporally tagged knowledge).

  • Q&A and Interview Format: A large subset of podcasts are interviews or panel discussions. These often naturally take a question-answer format (“Q: ...? A: ...”). That’s incredibly useful for training retrieval and QA systems, because it’s basically a huge collection of real Q&A pairs on diverse topics. (Stack Overflow and Reddit Q&As were valued for the same reason; podcasts give Q&As in spoken form, often more elaborative and story-driven.)

  • Multi-speaker Data: Transcripts identify speakers (in many cases). This can train models on dialogue attribution – knowing who said what and maintaining consistent “voice” if needed. It’s a step toward multi-agent conversational AI, where the model might need to keep track of different personas in a conversation.

5. First-Party Attention & Engagement Signals

Here’s a hidden gem: Spotify (and similar platforms) don’t just have the content; they have user engagement data on that content. When licensing or using podcast data, AI companies might also be very interested in these signals:

  • Skips and Rewinds: Imagine an AI model that knows which parts of an episode listeners tended to skip, and which parts they replayed. That’s a proxy for interesting vs. boring content. An LLM could use such signals in fine-tuning to prefer content that humans found engaging. It’s like giving the AI a hint: “this segment was really useful/entertaining, pay more attention to it.”

  • Completion Rates: If 90% of listeners finish Episode A but only 50% finish Episode B, that tells you something about quality or clarity of the content. AI trainers could weigh transcripts by such metrics, feeding models more of the high-retention content to imbibe the factors that make it so.

  • Social Signals: Platforms often have likes, shares, or comments on episodes. A transcript paired with “this episode was highly liked” is again a cue – maybe it contained a clear explanation or novel insight. These could be used for a form of reinforcement learning or supervised fine-tuning, aiming to make AI outputs more like the content humans explicitly appreciated.

  • Search and Discovery Data: Spotify knows what users searched for to land on a podcast. Those query-to-content mappings are incredibly useful for training retrieval algorithms (including AI chat search). It’s like a labeled dataset of “if user asked X, this podcast was relevant.” Such data could help improve how AI finds and ranks information (a crucial part of RAG systems).

In summary, Spotify’s data isn’t just big, it’s smart. It’s content plus a layer of human feedback. For AI companies obsessed with model alignment and usefulness, that feedback layer is gold for reinforcement learning from human behavior (an extension of RLHF). It’s the kind of data Google and OpenAI have been dying to get their hands on beyond the click metrics of web search.

6. First-Party, Licensable, High-Integrity Corpus

Unlike scraping random internet text (which can be rife with copyright issues, personal data, or mistruths), Spotify’s podcast trove is high-integrity and legally clear:

  • Attributed and Copyright-controlled: Every podcast episode has a known creator. Spotify either has rights or can get permission to use the transcripts. This is very different from, say, scraping blogs or social media where the ownership is fragmented and there’s legal ambiguity. For AI companies facing lawsuits over using copyrighted data without consent, a licensed deal with Spotify is an attractive safe harbor. (As Spotify’s CTO Gustav Söderström hinted, the industry is looking for ways to legally train models in a way that compensates creators – a licensed podcast dataset fits that bill.)

  • Quality and Trust: Podcasts (especially top-ranking ones) tend to be well-researched or moderated content. This isn’t the cesspool of the open web. For AI devs worried about garbage-in-garbage-out, podcast transcripts are relatively cleaner (few spam or troll content, since creating a podcast takes more effort than posting on a forum). This means less need for aggressive filtering of the training data.

  • Aligned Audio for Multimodal: Each transcript lines up with an audio file. If a company wants to train a multimodal model that both understands text and speech (or can generate speech), having aligned pairs of audio and text is ideal. Spotify’s library can act as a giant aligned dataset – think of hundreds of thousands of hours of speech with matching transcripts. For example, Meta’s Voicebox model was trained on 50,000+ hours of audiobook recordings with text. Spotify’s data could do the same for more conversational speech, giving a leg up in building the next Voicebox or Google’s voice-enabled Gemini.

In short, Spotify’s podcast data checks all the boxes: scale, diversity, quality, and legality. It’s no wonder that insiders consider it one of the most valuable corpora for any LLM aiming to truly understand, emulate, and engage in human conversation.

In Summary:

Spotify transcripts combine clean licensing, natural dialogue, and rich metadata. That’s a rare trifecta. It positions them as one of the most valuable datasets for training any AI model that wants to truly converse like a human and cite real, attributable sources.

LLMs Likely to Pursue a Licensing Deal with Spotify

Given this value, which AI companies are eyeing Spotify’s podcast data (and perhaps already in talks)? Let’s look at the key players in the LLM gold rush for audio:

1. OpenAI (ChatGPT / GPT-4 / GPT-5)

  • Why it matters: OpenAI’s flagship models (ChatGPT/GPT-4) are all about dialog and versatility. Podcast data would significantly enhance ChatGPT’s conversational finesse and world knowledge in niche areas. OpenAI has also pushed into voice (they introduced voice input/output for ChatGPT). Having a huge repository of voice transcripts would help train future GPT-5 on understanding spoken queries and generating more human-like answers.

  • Recent moves: OpenAI is already working closely with Spotify. In 2023, Spotify piloted an AI voice translation feature that used OpenAI’s technology to translate podcasts into other languages in the original speaker’s voice. That collaboration shows a strong relationship. Moreover, OpenAI’s web crawler has been voraciously scraping content, and one can assume podcast transcripts are on the menu. OpenAI knows the value here – they even built Whisper (an ASR system) which could transcribe audio at scale. All signs indicate OpenAI is gearing up to leverage podcast data big time.

  • Likelihood: ★★★★★ Very high. The existing Spotify partnership on voice, plus OpenAI’s appetite for high-quality conversational data, suggests a formal licensing deal is highly likely. Don’t be surprised if ChatGPT’s training data next year includes a “Spotify Transcripts” section. OpenAI would happily pay (or share tech) to get exclusive or early access to this dataset, given how it could keep them ahead in the arms race.

2. Anthropic (Claude)

  • Why it matters: Anthropic’s Claude is positioned as a safer, more “aligned” chatbot, with a focus on helpfulness and harmlessness. Podcast transcripts, being real conversations, can help Claude learn to handle diverse opinions and sensitive topics more gracefully (podcasts often involve nuanced discussions, not just straight facts). Also, Anthropic has emphasized having wide-ranging data to reduce bias – the diversity of Spotify’s content fits that goal.

  • Recent trend: Anthropic has been actively pursuing enterprise data partnerships. They’ve collaborated to integrate Claude with platforms like Slack and Notion (to safely analyze company data). While not the same as public podcasts, it shows Anthropic’s strategy of hooking into established data ecosystems. They might view Spotify as another such ecosystem. Additionally, Anthropic has less training data than OpenAI; getting a rich corpus like podcasts could narrow the gap.

  • Likelihood: ★★★★☆ High. While there’s no public deal with Spotify yet, Anthropic surely recognizes the value. They might not have the deep pockets of OpenAI or Google, but they did secure a ~$4B investment from Amazon in 2023. With that war chest, they could seek a deal. If OpenAI doesn’t lock something in exclusively, Anthropic will be at the table – perhaps pitching a more “AI-safety-conscious” use of the data (which could appeal to Spotify’s image). The only reason it’s not 5 stars is that Anthropic might prioritize other text datasets first, but podcasts are likely on their roadmap.

3. Google DeepMind (Gemini)

  • Why it matters: Google’s next-gen model, Gemini, is explicitly multimodal – aiming to handle text, images, and more. It’s logical to assume audio is part of that vision (Google has world-class speech tech already). Google also has Chirp, a powerful speech recognition model, and loads of YouTube transcripts. However, Spotify’s data could fill gaps: Spotify has a lot of exclusive shows and niche audio content not on YouTube. Access to that would bolster Google’s aim to have the most comprehensive training data. Plus, Google’s search AI (the new AI snippets in Search) could directly quote podcasts if it had that data indexed.

  • Recent moves: Google and Spotify are long-time partners – Spotify has been a Google Cloud customer since 2016, and the two collaborated on projects (Spotify’s personalized AI DJ uses some Google tech under the hood). Google’s also been integrating podcasts into its search results and had a Podcasts app (now merged into YouTube Music). Also notable: in late 2024, Google’s NotebookLM experiment was mentioned as being able to generate podcasts from user content, and Gustav Söderström (Spotify’s CPO) spoke about using LLMs for music/podcast discovery. There’s a clear alignment in vision.

  • Likelihood: ★★★★★ Very high. It’s hard to see Google staying away from such a data goldmine, especially to fuel Gemini. Given the existing commercial relationships and Google’s need to keep up with OpenAI, a deal makes perfect sense. They might already have some access via the cloud partnership, but an expanded agreement (maybe preferential access to Spotify data for Gemini training) could well happen. Don’t forget Google could offer Spotify a lot (money, cloud credits, promotion in search results, etc.) in exchange.

4. Meta (LLaMA / Voicebox)

  • Why it matters: Meta (Facebook) has been flexing with open-source LLMs (like LLaMA 2) and also showed off Voicebox, a cutting-edge speech generation model. Meta clearly has interest in audio – they have huge user voice/video content (Messenger, Instagram Lives, etc.), but podcasts would be a nice structured set to train on. If Meta wants to build the best multi-language, multi-speaker AI, podcast transcripts could accelerate that. Also, if Meta’s planning any AI features for WhatsApp or Instagram that involve voice understanding or generation, having a broad training set of real dialogues is invaluable.

  • Challenge: Meta tends to leverage open data or data it already has from its platforms. Would they pay for Spotify data? They did pay for some data in the past (e.g., partnering with universities for certain datasets), but culturally Meta leans towards web-scale scraping (with all the controversy that entails) or releasing models trained on publicly available data. Another issue: Spotify might be wary of Meta, a competitor in the attention economy (Facebook tried podcasts and then exited that business, but could return via AI).

  • Likelihood: ★★★☆☆ Possible. If Spotify’s willing to license non-exclusively to multiple players, Meta would definitely be interested in buying. But if any exclusivity or big price tag is involved, Meta might pass and try to assemble similar data elsewhere (or rely on YouTube/transcribed Facebook videos). Meta’s recent stance on AI datasets has been grabbing a lot of open data (they did train image models on Instagram content they have). Unless they feel at a disadvantage without Spotify’s trove, they might not chase it aggressively. However, Meta did train Voicebox on 50k hours of audio books – they know the value of lots of audio. So if the barrier is low, they’ll jump in.

5. Cohere / Mistral / xAI (Elon Musk’s Grok) / Others

  • Why it matters: Besides the big four above, there’s a swarm of smaller or newer LLM developers. Cohere (founded by ex-Googlers), AI21 Labs, Mistral AI (the French startup that released a 7B model), and xAI (Elon Musk’s new venture, whose model “Grok” is in the works) all need high-quality data to compete. None of them have a user-facing product at massive scale yet, so they can’t generate conversational data easily on their own. Spotify’s corpus could instantly level up a newcomer’s training set to something closer to what the big guys have.

  • Reality check: These players might lack the deep pockets or existing relationships that OpenAI/Google have. Elon’s xAI, for instance, reportedly raised $1+ billion and will likely scrape Twitter and other Musk-owned data first. They might eye Spotify if it’s available, but Elon could be a wild card in negotiations. Cohere and others could pool resources or go after a slice (maybe license older or non-exclusive podcast datasets).

  • Likelihood: ★★☆☆☆ Lower. Unless Spotify offers non-exclusive licenses at a broadly affordable rate, the smaller folks might not be first in line. They could instead rely on the open Spotify episodes that have transcripts on the web (some podcasters post transcripts on their own sites) – though that’s patchy. It’s also possible Spotify might partner with one of them for a specific project (e.g., an AI podcast search tool) rather than a full data dump for training. In any case, the big deals will likely happen with OpenAI/Google/Anthropic first, and only then might this second tier get a shot.

What Would Spotify Get in Return?

Let’s not forget Spotify’s incentives. Monetizing its data could open a new revenue stream. Here’s what Spotify can offer and what it might ask for:

Spotify can monetize:

  • The transcripts themselves (text data of episodes, especially exclusives like Joe Rogan or other Spotify Originals).

  • Audio assets (perhaps short clips or the alignment of audio to text for multimodal models).

  • User interaction data (as discussed, things like what parts of episodes are most engaging – though they’d be careful with privacy).

  • Possibly real-time API access (e.g., letting an AI model query Spotify’s database for the latest podcast transcripts on-demand, which could be a premium service).

By licensing these, Spotify turns its vast content library into direct cash or strategic partnerships.

In exchange, LLM providers would likely offer:

  • Hefty licensing fees or revenue share: This could be tens of millions of dollars in licensing. For example, Reddit and others have charged AI firms for data. Spotify’s data is arguably more valuable; they could command significant prices. Alternatively, a usage-based model (pay-per-token-generated from Spotify data, etc.) might emerge.

  • Visibility & AI integration: We might see co-branded features. Imagine ChatGPT explicitly integrating Spotify: “Want to hear more? This answer is from Spotify Podcasts – play the episode?” That drives traffic back to Spotify. Or an AI-powered Spotify feature where ChatGPT suggests podcasts (powered by OpenAI). Such partnerships could grow Spotify’s user base or engagement.

  • Technical collaboration: Access to cutting-edge AI tools in return. For instance, an LLM partner could give Spotify early access to AI for things like automated podcast translation (which they started doing with OpenAI), generative audio search (ask a question and get podcast answers), or even auto-generated content like AI DJ improvements. This would help Spotify enhance its platform using AI, staying ahead of competitors.

In short, Spotify stands to gain both financial rewards and product enhancements by striking the right deals. It’s a symbiotic gold rush: Spotify has the gold (data), the LLM miners have the picks and shovels (algorithms and cash).

Strategic Fit: Who Benefits Most?

Finally, let’s compare the key players on a few criteria, to gauge who’s in the pole position for Spotify’s data:

Expect the first data licensing deals to be struck with OpenAI and Google, given their deep interest and existing Spotify relationships. Anthropic is a strong contender for a seat at the table as well, especially to avoid falling behind in training. Meta will surely utilize the data if available, but might not be first in line to pay for it (they’re likely watching and hoping to get it via a more open route). In any case, audio content is set to become the next hot commodity in AI – and Spotify is sitting on a mountain of it.

Both brands and AI builders are rushing to tap this wellspring of conversational intelligence. In the coming years, don’t be surprised when “As heard on [Your Brand]’s podcast…” becomes a common phrase in AI-generated answers, or when your voice assistant starts sounding a bit more like a mix of the podcasts it has been trained on. Audio is indeed the new dataset, and the gold rush is on.

Podcast, Podcasts, LLM VisibilityFrancesca Tabor