The History of Expanso (Part 5): The Unbreakable Speed Limit
The below is a continuation of the series on the history of Expanso. Today, we're talking about the second of the three unchangeable laws of data - the speed limit. Read the whole series starting from Part 1.
The History of Expanso (Part 5): The Unbreakable Speed Limit
I was talking with an engineer at a major power company responsible for their renewable energy sites. They had a problem that was both absurd and crippling. To make minute-by-minute decisions on the energy spot market, they needed a single data point every ten minutes from their remote solar arrays. Instead, the sensors were shipping raw data every single second over thin, unreliable cellular connections.
They were moving 600 times more data than they needed.
The network choked. Transfers that should have been trivial took hours or even days to trickle in. The data was so stale by the time it arrived at the central office that their real-time failure prediction models were useless. Critical decisions about when to sell power, which needed to happen in minutes, were being made based on data that was weeks old. They weren't just inefficient; they were losing money and risking grid stability because they were fighting a battle against physics they had needlessly created.
Physics is Now a Product Manager
For a generation of applications, latency was a nuisance. A slow webpage or a batch job that took an extra hour was annoying, but it didn't break the business model. That era is over. For the fastest-growing and most valuable use cases—industrial automation, real-time AI, critical infrastructure monitoring—latency is no longer a performance metric. It's a core feature, and the laws of physics have become the strictest product manager you'll ever have.
The speed of light in a vacuum is a constant. In fiber optic cable, it's about 30% slower. This gives us a hard, non-negotiable speed limit. The round trip time for a packet of data to travel from a factory in Singapore to a data center in Ohio is, under ideal conditions, about 200 milliseconds. No amount of engineering brilliance will ever change that. That's the time tax for a single request, before you even account for network hops, processing, and database lookups.
When your business logic lives an ocean away from the event, you are physically incapable of operating in real time. You can't shut down a faulty manufacturing line after the defective parts are already in the box. You can't prevent a fraudulent transaction after the customer has walked out of the store.
The Cost of a Centralized World
The industry's default playbook - centralize all data in a single massive cloud region - is fundamentally incompatible with this reality. It was a model built for a world of batch processing and high-latency tolerance. Applying it to problems that have ANY sort of time sensitivity (let alone real-time) creates a cascade of failures that no amount of money can fix.
You can't buy your way out of a physics problem. You have to change the architecture. If the decision needs to happen in ten milliseconds, the data and the compute making the decision cannot be two hundred milliseconds apart. The playbook has to flip: stop moving data to a central brain and start moving the logic to where the data happens.
This is the only way to build systems that can react at the speed of the real world. By running the fraud detection model at a point-of-presence in the same region as the transaction, you collapse the round-trip time. By running the quality control model on a server inside the factory, you can stop the assembly line instantly. You aren't breaking the speed limit; you are simply refusing to pay the distance tax.
From Bottleneck to Breakthrough
This physical constraint was a foundational principle for Expanso. We saw that the only sustainable path forward was to build a system designed to obey the laws of physics. Expanso was created to make this compute-over-data architecture a practical reality. It gives you a single control plane to run your logic securely where your data is generated, whether that's in a retail store, a factory, or a regional cloud.
You keep the ability to manage and audit your pipelines globally, but the execution happens locally. This eliminates the latency penalty and transforms problems that were once impossible into solved ones.
The speed of light used to be a number in a textbook. Now it's a daily factor in system design. When did it stop being a theoretical constraint and become a practical, painful bottleneck for your team?