The media landscape is moving toward extreme personalization. Soon, major creators like MrBeast might have millions of different versions of their content, each tailored to an individual viewer based on data. This shift means content must be more data-driven to survive.

This is an 18-wheeler headed straight for us. If we do not get out of the way or figure out how to stop it, we are going to get crushed. Things are going to have to be a lot more personalized. There will be 440 million different MrBeast versions for each person based on the data.

Jamie AI is an AI platform designed to help podcast networks navigate this change. It allows content organizations to analyze their portfolio at a deep level to improve sales and production. By looking at retention graphs, producers can see exactly where audiences are most engaged. These peaks and valleys help creators refine their hooks and editing styles before they even start recording.

A major part of this process involves guest research. Revan explains that the platform can scan up to 500 hours of a guest's previous appearances. This identifies which topics resonate with audiences and which questions have been asked too many times. This data helps producers create interview maps that ensure the conversation is both fresh and engaging.

We can look at past interviews and figure out what has actually worked well on social for them. We watch up to 500 hours of content to figure out exactly what they have talked about before and what they have not talked about.

This system is used across various genres, from sports networks like Wave Sports to pop culture hubs like Wondery. Because the platform relies on data and audience retention patterns, it works effectively regardless of the specific subject matter.

When Revan first started building his product about a year and a half ago, there was little buy-in from production teams regarding AI. While tech hubs like San Francisco or New York were early adopters, the rest of the media world viewed AI as a threat or a tool that was not yet ready for creative work. Early AI models often produced poor results when asked to write stories or scripts, leading many producers to write off the technology after a single failed attempt. However, the narrative has shifted as tools have become more sophisticated. Instead of providing generic outputs, modern AI can now offer specific, line-by-line insights that actually assist the creative process.

AI is a big deal in tech bubbles, but a year and a half ago, most companies weren't in the state of mind that it would change everything. Now, they see it as an 18-wheeler headed straight for them. If they don't figure out how to stop it or get out of the way, they're going to get crushed.

Revan initially envisioned his tool as a way to reduce the cost of pre-production by automating tasks typically handled by multiple producers. Over time, he realized that businesses are less interested in saving money than they are in making more of it. This led to a pivot from cost-cutting to revenue generation. This evolution also involved bridging the gap between data and the creative arts. While many creators view their work as a pure art form, successful shows like Diary of a CEO have demonstrated the power of a data-centered approach. These shows often prioritize audience retention over the natural flow of conversation.

When you're really optimizing for retention and for views, it works. Steven Bartlett might not ask a heartfelt follow-up question after a guest shares something personal because he's optimizing for retention. He cuts straight to the next question so that the moment you feel done with an answer, you are already onto the next thing.

This data-driven editing can sometimes feel heartless or unnatural to the viewer because it bypasses the emotional nuances of a typical conversation. However, the dramatic growth of channels that use these techniques suggests that optimizing for data is becoming an essential strategy for scaling media brands.

While data analytics and optimizations are important, their impact depends heavily on the size of a show. For smaller channels, the priority should be building a human connection rather than worrying about retention metrics. Even major creators like MrBeast started by simply building an audience through authenticity before focusing on high-level optimizations. The goal for a new creator is to make sure people love the content regardless of the data behind it.

Initially you just have to have an audience that truly loves what you're making. I think a lot of that just comes from being authentic and being yourself and then figuring out the optimizations later.

When it comes to demographics, specific traits like age or geography matter less than the depth of the connection. Different demographics offer different advantages. Older audiences often have more money to spend, while younger audiences tend to be more interactive and watch more content. Revan points out that a creator like Jake Shane, with a deeply connected audience of 2 million, can be more successful than someone with 20 million followers who lack that same bond. A loyal audience will show up for brand promos and events, which is the true measure of a creator's influence.

Physical presence is a massive lever that many creators are not yet pulling. The ability to get thousands of people to show up at a physical venue for a personal appearance or event is worth millions of dollars in the eyes of brands. Revan suggests that creators should always look for third-party sponsors for these real-life events. While some creators prefer closed-off events for authentic fan time, major tentpole events like Coachella or the Super Bowl represent prime opportunities for creators to treat their brand as a full-scale business.

Saying to a brand that you can get real pull here is actually the difference of millions of dollars versus not having that in your back pocket.

Technology shifts usually involve slow adoption across different age groups. Short-form content has changed this pattern. For the first time, people of all ages are using the same platform. Children as young as eight and adults in their eighties are both consuming content on TikTok and Instagram. This universal adoption is rare. Even with newer technologies like AI, the usage remains segmented. Younger people and top executives are using AI tools, but the middle and older generations have been much slower to adopt them.

Short-form content has allowed plenty of the generations to come together. Eight-year-olds are watching TikTok and Instagram and 80-year-olds are watching TikTok and Instagram. I don't think that's ever really happened in a technology shift.

The simplicity and addictive nature of these platforms have driven this growth. This widespread use has fueled a massive increase in digital advertising. Companies spent around 700 billion dollars on digital ads in the past year. Revan notes that it is incredible to see nearly 100 percent of a demographic using a new technology within only six years of its rise.

Within six years, you have probably 100 percent of the demographic using a piece of technology. I think it is pretty insane.

Sales teams are moving away from simple brand awareness. In the past, companies just looked for podcasters with large audiences to promote their products. Now, the focus is shifting toward proving a unique audience fit. Brands want to see real evidence that their target customers overlap with a creator's following. This is especially true as the perceived ROI of generic content starts to dip. Even massive brands like Pepsi or Coca-Cola need proof of this overlap rather than just general reach.

Providing this proof at scale is a significant challenge. If a sales team manages dozens of shows, they cannot watch every minute of content or scan every social media post. Technology can bridge this gap by showing how often a creator naturally interacts with a brand. For example, an agency might show a brand that many of their creators already feature a product in their vlogs. This creates a much more compelling pitch than a generic offer.

We have 50 creators that in 40% of their vlogs drink Pepsi. Great. We can clip that, send that to the brand and really have a differentiating factor rather than just like, hey, this makes sense because we have all these creators and you should do a deal with us.

The relationship between creators and brands is also evolving. Larger creators are asking for better terms, such as equity or backend percentages on sales. Instead of just a flat fee, they want to share in the upside of the marketing engine. Revan suggests that creators should prioritize long-term partnerships. Working with a brand for only three months provides little value. It is better to align with companies on the rise and grow alongside them.

There is no point to work with a brand for 3 months and then never work with them again because you are essentially branding yourself alongside them. What you want to look for is brands that are on the up and up that you can grow with.

Personalization will define the future of content over the next twenty years. Revan predicts that platforms will eventually provide hyper-specific versions of creators for every individual user. Instead of one standard broadcast, there could be millions of different versions of a show based on personal data. This shift toward mass personalization means users will eventually only see content they love every moment of. While this might feel like a loss of authenticity, many viewers may not mind if the content is perfectly tailored to their tastes.

I think there will be 440 million different MrBeast versions for each person based on the data. You will hopefully only watch the content that is most applicable to you and that you love every moment of it. AI agents will work on everything to make your life as personalized and perfect for you.

The rise of AI creators does not mean the end of physical gatherings. Fans often attend events to be around others with shared interests rather than just to see the creator. However, these events are increasingly designed for a digital audience. For example, a recent Super Bowl performance used human stages and hedges that were efficient to move but blocked the view for people in the stands. The focus has shifted from the thousands of people in the stadium to the hundreds of millions watching on social media.

It was a horrible experience for basically everybody at the stadium this year. We are going to tailor it for the perfect TV and social media content. We are going to blow this out because who cares about the 80,000 people that are here? We care about the 800 million people that are going to see this.

Generative AI is rapidly becoming a staple in content creation, particularly on platforms like TikTok and Instagram. In China, apps like Kwai are already integrating video models directly into their user interface. Revan points out that audiences generally do not care if a human or an AI created the content as long as the quality is high and the information is relevant.

I actually think there's a huge growing force there in terms of we don't really care if this is a human or not, we just want the best content. I think that you're starting to see a lot of that.

Brands like Nike and Porsche are early adopters, using AI for marketing assets because the speed and economics are unparalleled. Nick notes that the commercial industry has been quick to embrace these tools due to compelling financial benefits. This shift in filmmaking is similar to historical uses of CGI. Revan argues that people have been creating things that don't exist for a long time, such as filming in Canada to look like New York. AI is simply a new tool in that lineage.

The technology allows animators and writers to pitch dozens of ideas instead of just a few. For example, a team could pitch thirty movies instead of three. This leads to better content because the best ideas have a higher chance of being selected. Despite its utility, AI faces a branding problem. Revan mentions that some graduating students boo AI at commencement speeches, highlighting a disconnect between tech-optimists and the general public who may see it as a negative force.

AI's catching a lot of flack for the name, what it is, but people have been doing some version of making things that didn't exist for a long, long time within content. Whether that's making something look like New York, but it was filmed in Canada. It happens all the time.

Nick suggests that AI might spark a renaissance for mid-budget films like romantic comedies. These genres have mostly moved to streaming because they are expensive to produce for theaters. By lowering the cost of sets and scenes, creators can make high-quality films without needing a studio to provide hundreds of millions of dollars. This could shift the theatrical landscape away from a reliance on massive blockbusters.

Innovation often creates more jobs than it replaces. While many people fear that AI will cause job scarcity, it is likely to have a net positive impact. For instance, the demand for data centers requires massive cooling systems. This could create hundreds of thousands of high paying jobs in the HVAC industry. Similarly, AI in radiology will not eliminate doctors. It will simply allow for more frequent and detailed scans, requiring more experts to interpret the data.

AI can now analyze X-rays. So all radiologists won't have jobs. No, people are just going to get more X-rays or people are going to need to understand every little thing a tiny bit better. There's just going to be more jobs.

Software engineering is also poised for growth rather than decline. Currently, we ignore many small problems because building software to fix them is too expensive or technically difficult. AI lowers these barriers. This allows engineers to build 100 times more software to address every part of daily life. Jeff Bezos describes AI as a bulldozer helping someone who has been digging a basement with a manual shovel. It makes work more efficient rather than making the worker obsolete.

Revan highlights that AI has changed his own career path. He was initially planning to sell podcast ads but shifted to building AI products. This shift allowed him to have a much larger impact on people. New graduates have a unique opportunity to use these tools to automate the parts of corporate jobs they dislike or to start their own companies. It has never been easier to turn a personal pain point into a product that serves millions of people.

The landscape of software is undergoing a massive shift. Revan believes AI is ten times as large as the internet. He predicts a total breakup of mid-sized software companies. The market will soon consist of two extremes. On one end are the massive model companies like OpenAI and Anthropic. These will be trillion dollar entities. On the other end are small, hyper-vertical companies that serve specific niches better than any general model can.

I don't think there's going to be any $500 billion software companies that exist because they're just going to get crushed by the models or they're going to get crushed by the really small company that's just doing it better. Everything that can be hyper-vertical is going to exist. And then everything that isn't is just going to be these large language models.

Nick suggests this creates a larger economy of small and medium businesses. These specialized companies may function as profitable lifestyle businesses run by a new middle class of entrepreneurs. However, a major risk exists. Large models could eventually build features that replace these niche tools. Revan argues that small companies have a unique advantage. They can make a customer feel that a product was built exactly for them. Large corporations struggle to provide that level of specific attention. This personal touch creates a moat that even massive models find difficult to cross.

The big company is never going to be able to service the specific exact customer and make them feel like this product was extremely built just for them, which I think is the difference. And I think customers will be willing to pay for that.

These massive AI companies are starting to resemble nation states more than traditional businesses. They are staying private for longer and accumulating immense power. This concentration of wealth and power among a few individuals could lead to social unrest if the benefits are not shared. Revan compares this to the early days of the Hershey factory. The company built an amusement park to compensate the town for the noise and pollution of the factory. AI companies may need to find a modern equivalent, such as supporting universal basic income, to prevent public backlash as automation impacts employment.