Data Consumption is Changing

Author

Matt Ober
July 09, 2025

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The tools we use, the answers we get. How we consume data is changing.

With the explosion in AI driven tools, the way we consume data, the answers we get, everything is changing. We went from searching Google, and SEO to using ChatGPT, Grok, & Perplexity which now has dozens of GEO tools being built. When consuming data, how we chart it, how we explore it, how we find it, how we pay for it- consumption based! and everything in between is changing so quickly.

CarbonArc, a company where I know the founder well from my hedge fund life, just released their demo of their new platform release. It’s exciting to think we will talk to data via voice or chat. Or we will really just talk about the problem or questions we are trying to answer, and then the interface will find the data for us, visualize it, chart it or combine it with other datasets to give us the insights we need.

Exploring Carbon Arc Platform 2.0

In this tutorial, I walk you through the new CarbonArc platform 2.0 experience launching in July 2025, accessible at app.carbonArc.co. We explore the redesigned homepage, the builder for constructing insights, and the updated data library and ontology. I demonstrate how to filter data, select metrics, and add insights to a workspace, including a specific example of analyzing credit card spend across Europe. I also highlight the new features for developers, including access to documentation. I encourage you to explore these updates and familiarize yourself with the platform's capabilities.

www.loom.com/share/479a8660e4704e80b70db4e18a2f87d0?sid=ab092673-ca23-49db-8247-5bb93f820c2f

Finding data is becoming easier, especially with platforms like InitialdataOffering.com, and how we process, collect, and interpret is changing at lightning speeds. Companies are building new datasets to compete with the incumbents, and the moats are slowly going to be challenged. Unless you have truly unique data from a specialized community or source like Stocktwits.com, for example, it is hard to build a moat on data that is becoming more and more accessible. The one area that is hard to break into is areas where you need Point in Time data, where you can’t recreate history, but over time, you can build a competing product. It’s why building data companies takes so long, because a good Point in Time dataset needs 2-5 years of history.

Data is changing, who we consume it, how we pay for it, the rules, the laws, the scraping, the buyers, the sellers, and everything in between. It’s fun to watch and will be fun to invest in.

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