I was talking to a designer friend yesterday.

I was wondering whether I should get a Mobbin subscription.

I had one when I was working at my last company. I was not sure if it made sense to buy a subscription when I am neither a designer, nor someone who will be using it regularly.

And I saw quite a few cheaper alternatives. So I asked my friend if I should just go for a knock off of Mobbin.

I also asked him why his company pays a premium for it when cheaper alternatives exist. His answer was that if existing apps change their UI, Mobbin will the updated UI soon. The cheaper alternatives will still be showing the same old design six months from now.

Mobbin isn’t just a collection of screenshots, it’s a live feed of an app’s design evolution. You pay because you have access to an ever increasing supply of design inspiration that is refreshed constantly.

That conversation got me thinking about data companies.

Why do some charge X while others can command 5X the price for what looks like the same information.

The answer isn’t about having more data. It’s about having data that’s alive. We will talk about 2 ideas in this post:

  • Museum and News channels.
  • Truth vs Religion vs Currency (That I discovered thanks to the Crunchbase CEO who discussed Crunchbase’s moat in a recent podcast appearance.)

There are really two kinds of data businesses: Museums (with a constant collection that does not add more art) & News channels.

They just preserve old artefacts.

Once you’ve seen the Mona Lisa as a user, you don’t need to check it again next week. The artefact becomes stale after the first visit. There is no new version of the Mona Lisa every month. Most data vendors, especially the copycat ones who just scrape the incumbent, are museums.

They scrape something once, file it away, charge admission forever.

Take a typical Crunchbase competitor for example. Company founding dates. Patent records. Historical funding rounds. Cap table.

Useful? Sure. But also easy to copy. And because they are copycats whose only differentiator is pricing, they can’t even update their artefacts or add new artefacts as the same velocity as Crunchbase.

News channels are different. They matter because what they show today is different from yesterday. No one’ opens the television to catch yesterday’s news.

Museums sell truth in the past tense. News channels update you everyday on the latest ‘truth’ or ‘facts’ and tell you signals about the future. Think of the weatherman telling you how the weather will be today so that you can be better prepared.

You tune in because today’s truths aren’t yesterday’s. They are fresh. They are evolving.

Static data is a race to the bottom. Someone will scrape your museum and build a cheaper one next door. First mover charges X/month. Six months later, five competitors offer the “same” data for 1/5th the price. Live data that is updated constantly is different.

Here’s what makes data alive:

  • It changes faster than competitors can copy
  • Missing updates breaks something downstream
  • Users need it fresh, not just accurate

Facts or truths aren’t truly defensible long term, especially with AI eating the world.

Yes, you can have some unique data, that competition does not have, but competitors can often automate similar pipelines or license the same sources. Without barriers such as exclusive capture rights, strong network effects, or switching costs, truths (stale or live) by itself may not sustain a price premium.

Now coming to Truths vs Religion vs Currency.

Religion is prediction.

Excerpts from a Crunchbase CEO’s podcast appearance in the World of DaaS podcast:

“Can I predict with any sort of accuracy what we think a future valuation might be, for instance? So we’re talking about like, how do we do valuation predictions? And that’s where Crunchbase actually does the valuable thing that you always wanted to do. Because when you were coming to Crunchbase before, those are the questions you were asking and we’re just giving you like one data point saying, well, here’s the last funding round. And you would have to sort of figure that stuff out on your own. Now we’re using thousands of feature vectors that go and figure it out. There’s religion, which is what will happen in the future, which is what you were just talking about. Here’s the companies that will be selling, here’s the companies, there’s some sort of prediction. It sounds like what you’re saying is that religion will be more valuable than truth. It has to be. The tricky part of all that, of course, is tell me any prediction engine you’ve ever seen that’s good. They’re terrible. Almost all of them are terrible. That’s changing. In our case, we have 95% precision. It’s hard to even believe it’s that good because we have all these little secrets that make it easy to figure out, but no one else has access to them. They don’t think that way. The user has to start believing it and the user has to say, I’m going to put my business future into the hands of a company that’s predicting stuff when I have a history of not believing in predictions. Like real religions, if you believe a certain thing and someone else will think you’re going to hell, you say you’re a Bayesian, I hate Bayesians, well then it’s off right there. And the only way that any of these religion companies have ever become big companies is if they’ve crossed the chasm from religion to currency. And then it’s like, well, people actually don’t really believe in the religion, but it’s priced like a FICO. Maybe people don’t even believe in the FICO score, but it’s the currency. It’s in every single loan. So therefore, you have to trade in FICO scores. It’s almost like the US dollar in a way. And so therefore, the company becomes super valuable. How do you go from religion, where you could have thousands of religions to, okay, this is the canonical one that we’re all gonna agree with. You don’t want to have thousands of competitors either. At the end of the day, it does come down to prove us in the pudding. The nice thing about predictions is someday they come true. So I can show, it’s not just a faith-based thing then. It is now this thing like, look, we have proven time and time again. In our case, how we talk about it is, in April, 600 of our predictions came true. So we were able to go and say, these companies got acquired and they got funding. What was that worth? Because you missed it, what did you miss out on that? For you doing deals, if you miss out on the hot company, that company sells for $5 billion, zero ROI on not buying Crunchbase is very, very clear. If we do get competitors in that space, we’ll go and say, well, here’s our accuracy rate in the last six months, let’s compare, let the best one win. And that’s where we feel really good because no one else has any in the now data, unless they’re somehow getting into the process itself before it happens, which is unlikely to happen.”

(Any error in the above transcription & interpretation is mine.)

Back to this post.

Tegus is another successful data play. Started as an archive of expert call transcripts. Sounded like a museum. But investors schedule new calls every week. Tegus adds these fresh transcripts almost daily. Miss a month, and you miss the latest insights on things that matter to you as an investor. It is a living feed because users themselves demanded fresh insights.

I believe the strongest moat sits at the intersection of two things: predictions that refresh continuously and can become currency.

Currency > Religion > Live truths > Truths.

From “This company has raised fundraising” to “This company might raise funding” to “This company’s probability of raising in the next 3 months just went from 50% to 80% this week based on their hiring velocity and the positive feedback by Tegus experts.”

Building a successful data business will be determined by:

  • You capturing data at the source.
  • The data being accurate. And hence the ‘truth’.
  • The data being captured in a scalable way.
  • Ideally multiple connectors/ sources from where you pull the data, so that you are not dependent on one connector.
  • The data being constantly refreshed, time-series data, not stale data, so that if someone scrapes your data too that data will be stale while yours will always be updated.
  • Transparency around data. “Last updated 2 dats ago” builds more trust than vague claims of “comprehensive up to date data.”
  • Predicting (religion) and helping key decision makers. You need to sell decisions, not just data.
  • Prove your predictions works.Your prediction scores will become a currency only if it works. If your predictions help customers make more money, they’ll keep paying. Show the ROI.

There is another reason I have been digging into this topic. Every venture capitalist says that proprietary data is the key moat in today’s AI world.

You might remember some of my research prompts about finding overlooked proprietary data sources. I’ve been focused on this over the past few months. Tegus has become one of my favourite products, and I often wish I had built something similar. Tegus commands pricing power, charging $20,000 per subscription because its insights help VCs and hedge funds confidently deploy tens of millions.

Recently, a founder urged me to copy Tracxn and rebuild it for the AI age, claiming that deep research and AI agents now make it easy to gather information and launch a data platform.

However, I believe that simply scraping Tracxn’s database will never be enough to create a lasting data business. To succeed, you need a richer plan. This post shares my research on how to do that.