In the next decade, the most valuable data will not be on the internet.

In the industry, there are not many articles that truly impact an investment era. The only ones I can think of are:

▌Fat public chains, thin applications

▌Public chains are nations

Now, another article should be added to this list:

Data At The Edge

This is the latest report released by top US venture capital firm USV. It discusses one thing, but this thing could reshape the investment logic of the next decade:

The most important data in the internet era comes from software and networks; in the next phase, the most important data will come from the real world.

USV was founded in 2003, with representative investments including Twitter, Tumblr, Etsy, Coinbase, Uniswap, and others.

These projects may seem unrelated, but they share a similar structure—network effects.

1/ What are network effects?

Simply put, it is a positive feedback loop.

▌More users → More content/data → Products become more valuable → Attract more users → Stronger network effects

This is the common moat of great companies in the internet era. Facebook, X, Airbnb... all are like this.

And USV points out in the new report: This data flywheel is about to undergo a significant migration.

2/ Internet vs. Real World: Two Data Flywheels

Data flywheel in the internet era:

More users → More content or data → Products and networks become more valuable → Attract more users

Data flywheel in the real world:

Deploy more devices and robots → Generate more real-world data → Data improves models → Models make devices more efficient and cheaper → Deploy more devices and robots

In the internet era, companies accumulate data through user behavior; in the future, the most valuable data will come from sensors and robots, from every corner of the real world.

3/ Robots: Dual Identity

In this new framework, robots play two roles:

First, they are execution tools. Moving goods, checking equipment, cleaning environments.

Second, they are mobile data collectors. Each task they complete generates a large amount of data, which in turn feeds into models.

Robots are not just labor; they are data machines themselves.

4/ Dilemma: Which Came First, the Chicken or the Egg?

It sounds wonderful, but the robotics industry is trapped in a vicious cycle.

▌To build sufficiently good embodied robots, a large amount of real data is needed

▌To obtain a large amount of real data, sufficiently good robots need to be deployed to customers

The industry needs a breakthrough.

5/ Caspius: A Different Path

The company I am focused on, @caspius_ai, is attempting a different path.

It does not directly make robots but is building data infrastructure. Specifically, it collects first-person perspective data to establish a learning foundation for embodied AI.

The path is as follows:

Ordinary users wear devices → Record first-person operation videos → Accumulate human behavior and interaction data → Train embodied AI models → Help robots learn vision and operational capabilities → Eventually deploy in real scenarios → Generate real execution data from robots

Caspius addresses the "data cold start" problem in the robotics industry.

It first allows models to learn how humans observe and operate in the real world, then helps robots acquire initial capabilities.

This type of data was historically very difficult to obtain at scale. Now, with declining hardware costs and technological advancements, there is finally an opportunity to scale data collection through community collaboration.

6/ In Conclusion

The internet has developed all easily accessible data; the next generation of opportunities may come from real-world data that was previously unobservable, incomprehensible, or unautomatable.

Robots are at the center of this change.

And the significance of Caspius should be understood in this context. Sometimes, the hardest part is not the flywheel itself, but the initial push to get the flywheel turning.

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