For most of the internet's history, search visibility has been built around text.
Websites, articles, product pages, news coverage, reviews and forums became searchable because machines could crawl, index and understand the words published on them. A brand could appear in a two-hour podcast discussing its category, be recommended by an expert in a video, or have its founder explain an important idea in an interview, yet much of that information remained far less accessible to search systems than a conventional webpage.
AI is beginning to change that.
Particle, the company founded by former Twitter engineers, has launched Radar, a search and intelligence platform designed to make podcast conversations searchable and accessible to AI agents. Radar has already transcribed more than 130,000 podcasts, including the Apple Top 200 across 135 categories, and says it is adding around 20,000 episodes to its index every day.
What looks like a podcast search product could signal something much bigger.
The searchable internet is expanding beyond the written web.
And as AI gains the ability to understand more of what people say, watch and discuss online, the definition of digital authority could expand with it.
A Large Part of the Internet Has Been Difficult for Machines to See
Think about how much useful information exists outside conventional webpages.
A founder explains why a product was created during a 90-minute podcast. An industry expert recommends three companies during a YouTube interview. A customer discusses a product in a video review. A CEO explains the company's differentiation during a conference panel. A specialist makes an important observation halfway through a webinar.
Humans can consume all of this information.
Machines historically have had a much harder time doing so reliably at scale.
Particle CEO Sara Beykpour described the problem simply: AI agents are generally unable to access audio unless someone or something first turns it into text. Radar is attempting to build that missing layer by transcribing spoken content and structuring what was previously buried inside audio.
The interesting part is that Radar does more than produce transcripts. Its system identifies speakers and understands entities including people, companies, brands, products and topics. Users can search those conversations, track mentions and retrieve relevant clips with timestamps. Its API and MCP integration are designed to make the same intelligence available programmatically to AI agents and other businesses.
In other words, a brand being discussed during a podcast is no longer necessarily just an audio moment.
It can become structured, searchable information.
The Searchable Web Is Becoming the Searchable Internet
Traditional search optimization developed around a relatively clear unit: the webpage.
You created a page. Search engines crawled it. They analyzed its content, links and surrounding signals, then decided whether it deserved to appear for a query.
AI discovery is less constrained by that structure.
An AI system trying to answer a question such as "Which emerging skincare brands are doing interesting work in personalization?" could potentially draw evidence from articles, Reddit conversations, product reviews, databases, social discussions and increasingly transcripts from podcasts and videos.
The source does not necessarily need to look like a search result.
It needs to contain useful information that machines can access, understand and trust.
That distinction matters.
The internet contains an enormous amount of knowledge that has historically been poorly represented in conventional search indexes. As transcription, multimodal understanding and retrieval infrastructure improve, more of that knowledge becomes available to machines.
Search is therefore not simply changing its interface.
Its information supply is getting bigger.
Podcasts Could Become Part of AI Visibility
Brands have traditionally thought about podcasts primarily through audience reach.
How large is the show? How many listeners does it have? Is the audience relevant? Will appearing on it create awareness?
Those questions still matter. But machine-readable audio introduces another dimension.
Imagine the founder of a fintech company appearing on several respected industry podcasts and consistently explaining a distinctive approach to fraud prevention. Today, those appearances might generate awareness among a few thousand relevant listeners.
Once those conversations become indexed and accessible to AI systems, they also become part of the digital evidence surrounding the company.
The founder's explanation can become searchable. The company's name can be associated with fraud prevention. The hosts' comments can become discoverable. The context surrounding those mentions can be analyzed.
A podcast appearance could therefore have value long after its initial audience has listened to it.
This changes the economics of spoken media.
The audience is no longer necessarily just the people listening when an episode is released. It may eventually include the machines researching that topic months or years later.
Brand Authority May No Longer Be Mostly Written
This has significant implications for how companies think about authority.
Until now, building digital authority has largely meant producing or earning written evidence across the web: strong website content, credible media coverage, backlinks, reviews, citations, industry profiles and discussions on authoritative domains.
Those signals will remain important.
But imagine adding another layer:
Podcast appearances.
YouTube interviews.
Conference panels.
Webinars.
Video reviews.
Expert discussions.
Founder conversations.
Product demonstrations.
Customer interviews.
If machines can reliably identify who is speaking, what organization they represent, which products are being discussed and what claims or opinions are being expressed, these formats become another source of entity information.
A company repeatedly discussed by credible people across high-quality podcasts could potentially send a very different authority signal from a company whose existence is documented only on its own website.
AI visibility then becomes less about optimizing a collection of webpages and more about building a credible footprint across the machine-readable internet.
This Could Change Digital PR Too
Digital PR has historically had a fairly straightforward SEO incentive: earn coverage and ideally receive a backlink from an authoritative website.
That model may become more nuanced.
Suppose a founder appears on five influential podcasts but receives only one conventional backlink. Under a traditional SEO framework, the measurable search value might appear limited.
Under an AI discovery framework, those five conversations could contain hundreds of pieces of useful context about the founder, company, category, products and expertise.
The backlink is no longer the only machine-readable outcome.
The conversation itself becomes data.
That could make podcast guesting, interviews and expert commentary increasingly relevant to AI visibility strategies, particularly when the discussions occur in credible environments and contain substantive information rather than promotional talking points.
It could also change how brands evaluate media opportunities. A smaller, highly authoritative industry podcast might create more useful contextual signals than a much larger entertainment show where the company receives only a passing mention.
Reach remains important, but relevance and context become much harder to ignore.
The Same Shift Is Likely Coming for Video
Radar is starting with podcasts, but Particle says its ambitions extend to other forms of audio, including YouTube videos and news clips.
That direction is important.
YouTube alone contains an extraordinary archive of reviews, tutorials, interviews, product comparisons, conference talks and expert opinions. Much of that knowledge is difficult to represent through traditional search because the information lives inside the video rather than on the surrounding webpage.
Multimodal AI can increasingly understand not just transcripts but imagery, objects, demonstrations and context inside video itself.
That means the future machine-readable internet may not stop at understanding what someone said.
It could increasingly understand what was shown.
A product demonstrated during a YouTube review, a chart presented during a webinar, or a technology explained onstage could all become pieces of retrievable information.
The boundary between "content" and "data" starts becoming much thinner.
Brands May Need to Think Beyond Content SEO
For brands, the temptation will be to respond by trying to "optimize podcasts for AI" in the same way companies once optimized articles for Google.
That would probably miss the bigger opportunity.
The more useful question is whether the brand is creating enough credible evidence across the internet for machines to understand what it represents.
If a company claims to be an authority in a category, does that authority exist outside its own website?
Are industry experts discussing it?
Are founders contributing meaningful ideas to relevant conversations?
Are customers reviewing and explaining the product?
Are credible publications covering it?
Are people comparing it with alternatives?
Does the company appear in podcasts, videos, communities, research and other places where the category itself is being discussed?
AI systems have the potential to connect these signals at a scale traditional search engines could not.
That makes genuine authority considerably harder to manufacture, but potentially much more valuable once established.
The BeyondB Perspective
At BeyondB, we have been thinking about AI visibility as something broader than ranking inside ChatGPT, Gemini or Perplexity.
The fundamental shift is that businesses increasingly have two audiences for their digital presence: people and the intelligent systems influencing what those people discover.
Until recently, making a brand understandable to those systems largely meant working with the parts of the internet machines could easily access: websites, structured data, articles, third-party coverage, reviews and other text-heavy sources.
That boundary is starting to move.
Radar making more than 130,000 podcasts searchable is one example of the infrastructure being built underneath that change. More importantly, its API is already attracting customers including hedge funds, AI search platforms and data resellers interested in information that conventional agents could not previously access.
This suggests that AI visibility strategies will eventually need to look beyond the traditional web.
A brand's authority may be shaped by what its website says, what publications write about it, what communities discuss about it, what customers review, what experts say during podcasts, what creators demonstrate in videos and what executives contribute to industry conversations.
Not all of these signals will carry equal weight, and we should be careful not to pretend the mechanics of future AI recommendation systems are already known.
But the direction is becoming clearer.
The amount of information machines can access is expanding.
