Web3

AI has a landlord problem. Not because models are renting office space, although given GPU bills, perhaps they should negotiate. The deeper issue is that modern AI increasingly lives inside a small number of large platforms. The data, the compute, the model weights, the deployment channels, the safety policies, and often the user interface are controlled by the same narrow set of institutions. The result is not merely concentration in a business-school chart. It is concentration in the machinery through which other businesses now write, decide, recommend, price, design, and automate. ...

A wallet is easy to watch and hard to understand. That is the small comedy at the centre of DeFi analytics. Every transaction is public, every contract call can be inspected, every log can be dragged into a dashboard, and yet the actual question remains stubbornly human: what was this user trying to do? A swap may be a trade, a hedge, a liquidation defence, an arbitrage leg, a farming manoeuvre, or just someone clicking through a protocol interface with dangerous confidence. The chain shows the footprint. It does not provide the diary. ...

TL;DR for operators The useful reading of this paper is not “AI agents are coming to crypto.” That is already obvious, and in some corners of the market, painfully over-branded. The sharper point is that Web3-AI agents are forming a stack. At the bottom are infrastructure and trust layers: protocols, DePIN systems, verification mechanisms, execution environments, and agent-development platforms. On top sit the applications: DeFi agents, portfolio tools, market-intelligence systems, governance assistants, security auditors, creative agents, and RWA managers. The paper’s dataset of 133 projects shows this stack is not evenly valued. Infrastructure accounts for 67.8% of the analysed $6.92 billion market capitalisation, even though incubation platforms show the most project activity.1 ...