Field
The application of scientific understanding to build tools and systems. Technology both reflects the science of its era and reshapes the questions that science can ask next.
Strip away the cryptocurrency layer and blockchain is a database with one specific property: many computers must agree before anything is written. Rasmus Risager Lindegaard explains how consensus mechanisms work, where the technology actually matters, and why the crime narrative is statistically backwards.
Apple takes 30%. YouTube decides what creators earn. The platform tax is structural, and centralised platforms cannot reduce it without disappointing shareholders. Decentralised platforms have no such constraint — and that is not hype, it is mathematics.
Blockchain crime accounts for less than 1% of transactions. Traditional fiat currency runs at 2–5%. The fear of crypto reveals something other than rational risk assessment — and the permanent public ledger makes it more traceable than cash, not less.
A model trained to detect skin cancer learned to look for rulers instead. The explainability problem is not a glitch. It is Goodhart's Law running at machine speed — and the gap between what AI systems can do and what we understand about how they do it is where the most consequential work in the field is now happening.
In 2012, a neural network cracked image recognition in a way that reshaped the entire AI field. Andreas Møgelmose traces what neural networks actually are, how convolutional architectures build understanding in layers, and why a cancer-detection model that learned to spot rulers instead tells us something important about where the field still cannot go.
Web3 proposes a third shift in how the internet works — from reading and writing to owning. Rasmus Risager Lindegaard explains platform economics, NFT royalty mechanics, decentralised finance, and what needs to happen before any of it reaches mainstream adoption.
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