The long-standing trend of centralizing all data into a single warehouse is incompatible with the speed of AI. Large-scale data migrations are too slow. The future architecture will involve AI models operating closer to data sources for faster, decentralized operation.

As AI agents begin to conduct economic work and transact with each other, they will create an "agentic economy." Our current financial system is ill-equipped for this future, lacking the ability to handle the billions of instant, global, and micro-scale transactions that will become commonplace.

After successfully deploying three or four AI agents, companies will encounter a new challenge: the agents have data conflicts and provide inconsistent answers. The solution, which is still nascent, is a "meta-agent" or orchestration layer to manage them.

Tools like Git were designed for human-paced development. AI agents, which can make thousands of changes in parallel, require a new infrastructure layer—real-time repositories, coordination mechanisms, and shared memory—that traditional systems cannot support.

A huge unlock for the 'Claudie' project manager was applying database principles. By creating unique ID conventions for people, sessions, and deliverables, the agent could reliably connect disparate pieces of information, enabling it to maintain a coherent, high-fidelity view of the entire project.

The durable investment opportunities in agentic AI tooling fall into three categories that will persist across model generations. These are: 1) connecting agents to data for better context, 2) orchestrating and coordinating parallel agents, and 3) providing observability and monitoring to debug inevitable failures.

As AI makes digital content and transactions nearly free to create, trust evaporates. Crypto primitives like blockchains offer a solution by providing verifiable identity, provenance (chain of custody), and reliable on-chain data, which is crucial for both humans and AI agents to operate safely.

As AI capabilities accelerate toward an "oracle that trends to a god," its actions will have serious consequences. A blockchain-based trust layer can provide verifiable, unchangeable records of AI interactions, establishing guardrails and a clear line of fault when things go wrong.

The future of AI in finance is not just about suggesting trades, but creating interacting systems of specialized agents. For instance, multiple AI "analyst" agents could research a stock, while separate "risk-taking" agents would interact with them to formulate and execute a cohesive trading strategy.