ListenHub

5-28

Mars: You know, I keep hearing about moats in the business world. Always pictured, like, medieval castles, right? Drawbridges, high walls, the whole shebang. But now, everyone's saying in AI, the real moat isn't patents or network effects – it's *usage*. What the heck does that even *mean*?

Mia: Haha, yeah, it sounds kinda counterintuitive at first. You know, back in the old internet days, your moat was just how many users you had. Think Facebook or eBay. But in AI, every click, every chat, every bit of data someone throws in... that teaches the system something. That learning, all those interactions, that's what becomes your barrier.

Mars: Okay, so it's like... the more you use an app, the more it *knows* you? Is that just super-charged personalization, then?

Mia: It's personalization cranked up to eleven! Think about Google back in the day, right? Sure, PageRank helped, but the *real* treasure was all that click data. Every search, every result people clicked on. That feedback loop made their algorithm smarter than anyone else's because nobody else had that mountain of real user behavior. It's like they were picking up breadcrumbs from every single search.

Mars: Ah, so they were basically scooping up hints from every single search to refine their predictions. I get it.

Mia: Exactly. And the same thing applies to modern AI. OpenAI didn't launch ChatGPT just for kicks. It was, and still is, a giant data-collection engine. Every conversation you have, every correction you make, every follow-up question... it all feeds the model. More data, better performance, stronger moat.

Mars: But what if someone just builds a clone? Can't they just scrape data too?

Mia: They can try, but replicating billions of genuine, *quality* interactions? That's tough and expensive. It's like trying to catch lightning in a bottle, except the bottle's a moving target. Plus, think about vertical apps, like AI for medical imaging or legal contracts. They collect super-niche data. That specificity is gold. Your competitor can't just walk in and steal that.

Mars: So, it's not just volume, it's also how unique that usage data is to your particular product?

Mia: Bingo! It's a two-lane highway. Broad platforms get a ton of generic interactions, which still matter, and niche platforms get fewer but highly specialized interactions. Both create barriers. Think about a fitness app that tracks your runs, your heart rate, even how you breathe. Over time, it knows your form better than *you* do. A new app trying to break in? They'd need millions of runs to catch up.

Mars: Gotcha. So, when people say usage is the new moat, they mean this feedback loop from real users is the fortress wall?

Mia: Yep. Every interaction adds another brick. If you can't gather or process that data, you're outside the castle walls, waving a white flag.

Mars: Makes sense. So to wrap up, in the AI era, your advantage comes from usage-driven insights. Those are your real barriers to competition.

Mia: Couldn't have said it better myself.

Mars: Alright, that’s a wrap on usage as your AI moat. Thanks for unpacking that.

Outline

Usage is the Moat

In the internet economy, the network effect was the moat. In an AI economy, usage could be the moat.
The next trillion-dollar moat may not be built on patents or network effects— it will more likely be constructed from billions of user interactions that your competitors can't replicate.
A moat in business is generally referred to as some significant barrier of entry that prevents competition from coming in and eating your lunch.
Each new technological revolution has new potential moats in them. In the advent of telephony, we saw a very powerful moat - the network effect.
This AI era poses a new question: can usage be a moat. In particular, can the data collected by customers using a product create a sustainable moat.
Perhaps the biggest moat of this kind in history is from Google. Google initially was able to automate search with their PageRank algorithm. However, the real advantage that they had was all of the click data that they were gathering from previous searches.
The intent behind ChatGPT was less to create a gangbuster consumer product and more to get this kind of usage data feedback.
More data can mean a better AI product.
I assert that the value of a product increases logistically with the amount of usage on the platform.
The moat is in creating a feedback loop from individual customer-specific usage and their specific problem. One could argue that this effect is greater in vertical applications where this data is the most specific and proprietary.
In this era that seems to be led by artificial intelligence, moats might be different. We have to ask the question of if a moat has changed in this period of time. Perhaps, usage is the moat

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