AI Is Transforming Media Forever, Here’s How

I run a media company, and I love writing. So it’s bittersweet to write this, but here it is: AI is going to fundamentally change media in all sorts of ways over the next five years. It’ll make running some newsletters like running a buggy whip business, but it will also open up new opportunities for content creation that we can’t imagine today. 

Given this state of affairs, I’ve been thinking a lot about how AI affects what we do at Every—what are its opportunities, and what are its risks? We want to build for the future, while also respecting the fundamental thing that we created Every to do: create more high-quality business writing in the world. 

In my opinion, the best way to think about things like this is to experiment. So that’s what we’ve been doing. We’ve been building chatbots that make it easier to ask questions of written content. We’ve also been building little tools and workflows, some public and some not, to see how AI fits into our writing process and our strategy. My co-founder Nathan has even incubated an AI writing app. I want to lay out a few things that I’ve learned, and some predictions for what it might mean for the future. 

Transitions are hard, and this one is no exception. I think it’s right to be apprehensive about the impact this will have on creatives of all stripes, and in particular on those who work at larger media organizations that were built for a pre-AI world. For example, screenwriters are currently on strike and one of their demands is a guarantee that AI won’t be used in the scriptwriting process. 

I think these are important issues, and I'm hopeful that Hollywood and its screenwriters can find an equilibrium that uses AI to augment writers and compensate them better—rather than ban it entirely.

Because far from sucking the marrow out of the creative act, I’m optimistic that these tools will become platforms upon which more people can make better creative work, and where smaller teams of people will be able to have a larger impact.

Here’s a short rundown of the ways I predict that AI will fundamentally change the piece of media that I know best: written media.

De-risked content is going to get automated

Media companies monetize creativity, but the creative act is inherently unpredictable. Sometimes an individual or group sits down at a computer or in front of a canvas or behind a camera and produces something earth-shattering. Mostly, they produce crap. Usually, they do it behind schedule.

This makes running a media company hard. If the quality of your product is inherently unpredictable and changes every day, creating stable, growing cash flows is like throwing a leash on a feral alligator and trying to take it on a walk in Central Park: ill-advised.

Because of the inherent unpredictability of the creative act, most of the media business is about looking for ways to de-risk creativity. De-risking strategies are like SSRIs for media companies. Usually, they flatten the upside of your creative work a bit, but they seriously dampen the downsides. De-risking makes it easier to stack the bricks of your business and provide a quality product day after day. There are many strategies that can make this work:

• Summarize other people’s work. It’s easier to summarize something interesting than it is to create it from scratch. Morning Brew, which provides short voice-y summaries of business news, is a great example of this. 
• Find a content arbitrage. Take a piece of content that did well somewhere else and reformat it for your audience. Early Buzzfeed found that if they took popular posts on Reddit and rewrote them as listicles they’d reliably go viral. This is also why Hollywood turns books into movies. Finding arbitrages is easier than finding your own ideas.
• Create a format. Formats give structure to your creativity, which makes creating something new a little more like filling out a Mad Libs and a little less like alchemy. Matt Levine’s format is a few mini-essays reacting to finance news. Ben Thompson’s format is taking a piece of news about a large-cap tech company and then quoting old pieces he’s written about aggregation theory to analyze it. Axios’s format is smart brevity. Every has experimented with this a lot, too, with 3 Shorts and more.
• Make it shorter and shallower. Complexity increases exponentially with the length and depth of your creative output. The shorter and shallower your piece is, the easier it is to make. Not calling anyone out here in particular. 😆
• Get a beat. If you want to get a piece out every day, you have to play in a pond where you can effortlessly react to what’s going on without the need for too much additional research. A beat solves this by creating a box around what you need to know to make something interesting. For example, Ben Thompson sticks to large-cap tech companies, and I stick to what’s going on in AI.
• Make sequels. You don’t have to risk coming up with something new if you’re remixing something old. This is why Hollywood does sequel after sequel. Each sequel might cost a lot of money to produce, but they’d have to make 10 to 100 movies with net-new characters in order to find one that does as well—so it’s relatively cheaper to make a sequel.
• Interview people. It’s easier to have an interesting conversation than to think up new ideas yourself. Have a conversation with someone and then summarize it. I’ve done this a lot for Every.

Interestingly, while each of these strategies de-risks a human’s ability to make pretty good content—they also make it easier for AI to automate. Summary-based media products are easier to automate. Shorter pieces are easier to automate. Content arbitrages are easier to automate. Formats are easier to automate. Interviews, which are primarily summaries, are easier to automate.

I know this is true firsthand because we’ve already done it at Every. Each Sunday, we send out a digest email to Every readers linking to the articles we published that week with little one-paragraph summaries of each one. I’ve written these summaries off and on every Saturday for the past few years—much to my girlfriend’s chagrin. We’ve also had a rotating cast of writers who’ve tried their hand at it.  

Now it’s automated. Lucas Crespo, who runs our ad and course operations, built a little app that does it. You stick a headline, author, and article text into the app and it outputs a summary using GPT-4. The summaries it generates are modeled on previous summaries that we’ve written in the Digest, so it comes closer to our style. Oh, and did I mention that Lucas built this app in Python and deployed on Heroku in two days—and that he has zero prior programming experience? All he had to do was tell GPT-4 what he wanted to build, follow its programming instructions, and ask it to help him when he got stuck.

Even though we have this summarizer tool now, I still spend time editing the summaries we put in the Digest. Occasionally I’ll rewrite them if I don’t think the ones the tool produces are good enough. GPT-4’s ability to summarize is not perfect—yet. But I wouldn’t bet against it over the next year or so, especially as tools for fine-tuning and reinforcement learning through human feedback become more widely available. 

What does this mean? Individual writers will have a lot more leverage to create more media products on their own. The cost structure of creating these media products will look more like software and less like media. There will be some R&D cost upfront to encode your taste and sensibilities into a summarizer. But once that’s done, you’ll be able to let it loose on the world without having to write every single day. The same thing goes for interviewing—you’ll still have to spend time asking good questions (though AI might help even with that). But turning a conversation into a transcript into a readable interview might soon be a far less time-consuming project than it is today. It’s also possible that readers will prefer to own the summarizer themselves and curate it according to their own preferences—à la my co-founder Nathan’s Infinite Article idea.

This means that writers will be able to devote more time to gathering new facts and doing research. They’ll also be able to spend more time writing longer, more interesting articles that are less amenable to automation.

There’s creative upside here, but there’s also a lot of risk for writers who are currently writing more de-risked types of editorial products as part of their day job at larger companies. I can imagine that type of job changing significantly—and soon. If that’s what your job consists of, the best option, in my opinion, is to learn to use these tools today to get better leverage and create good work. It’s a skill in and of itself to get good summaries or interviews or formatted news hits from AI, and your value will go up tremendously if you know how to do it.

Another smart move for writers who are part of unionized workplaces is to apply collective pressure in the same way screenwriters are doing in Hollywood. Media compensation was designed for a world without AI—and it’s clear that compensation frameworks need to change as technology changes. 

The implications of AI in the media landscape extend beyond the realm of de-risking content in existing formats. It will also lead to the creation of new content formats that were previously impossible.

AI unbundles research and narrative

Writers, especially in journalistic organizations, do two things: research and write.