The survey covers the ground where computation, money, and trust are being rebuilt.

The cloud made software easy to ship and easy to lose. Local-first architecture inverts the deal: data lives on the device, sync is a background concern, and the server becomes a peer rather than a landlord. CRDTs and modern sync engines have made this practical; the open question is economic.

We study the engineering — convergence guarantees, partial replication, end-to-end encryption over sync — and the business question underneath it: what does a software company look like when it can't hold your data hostage?

Financial infrastructure is entering a new phase. We study the platforms connecting payments, data, and services — and how advances in computing could make those systems more responsive, accessible, and adaptable.

Our infrastructure research starts with working systems. We examine reliability, interoperability, and performance under real-world conditions, with a focus on what emerging technology makes possible for financial services.

Markets are becoming more connected, automated, and informed by machine intelligence. We explore how emerging tools could reshape trading, credit, and capital allocation, from adaptive analytics to new approaches to financial decision-making.

Our questions span technology and economics. How does automation change market behavior? Where does risk move as decisions become faster? We follow the systems, incentives, and information behind the next generation of financial services.

The most valuable training data — medical, financial, personal — is exactly the data that can't be pooled. Privacy-preserving machine learning is the set of techniques that resolve the contradiction: federated learning, differential privacy, secure aggregation, and the early, expensive frontier of fully homomorphic encryption.

We benchmark the overhead honestly. Most "private AI" today is a press release; some of it is a working system with a 10× cost multiple that's falling fast. Knowing which is which is the research.

Enclaves let you trust a computation without trusting its operator. That single property — remote attestation — is becoming load-bearing infrastructure for confidential cloud, verifiable AI inference, secure financial analytics, and resilient data platforms.

We maintain a standing review of the hardware base: SGX's lessons, TDX and SEV-SNP in production, side-channel history, and the open-silicon efforts that could make attestation auditable all the way down.

Software is beginning to hold money. Agents that transact, underwriting models that read unstructured reality, compliance systems that explain themselves — the convergence of machine intelligence and financial infrastructure is where our other five quadrants meet.

The hard problems are not model quality but accountability: who is liable when an agent trades, what an audit trail means when the decision-maker is a weight matrix, and which rails — open or closed — agents will settle on.