Perspectives on AI Infrastructure
Research insights, technical analysis, and perspectives on the challenges shaping the future of enterprise AI and autonomous commerce.
Why Semantic Precision Matters for AI Commerce
When humans negotiate, ambiguity is a feature. Contracts are deliberately vague to accommodate unforeseen circumstances. AI agents don't have this luxury. They need semantic precision—a shared understanding of meaning that persists across different systems.
The Trust Bootstrapping Problem
How do you establish trust with an entity that has no history? For humans, the answer involves credentials, references, and institutions. AI agents lack these institutions. When an agent appears for the first time, it has no credentials, no references, no history.
Blockchain for AI: Beyond the Hype
The AI-blockchain intersection has generated more hype than insight. But beneath the noise, there's a genuine use case that most commentary misses: blockchain as enforcement infrastructure for autonomous AI agreements.
Regulatory Readiness in AI Infrastructure
The AI Act is law in Europe. Executive orders are proliferating in the US. Yet most AI infrastructure is built as if regulation doesn't exist. This is a mistake. Regulatory requirements are design constraints to incorporate from the beginning.
The Multi-Agent Coordination Problem
Single-agent AI is impressive. Multi-agent AI is transformative. But multi-agent systems face coordination challenges that don't exist for single agents—challenges that require rethinking established solutions from distributed systems and game theory.
Enterprise AI: Why Production Is Different
The gap between AI demos and AI production is wider than most realize. Demos operate on clean data with cooperative users. Production operates on messy data with adversarial users. This is why enterprise AI projects have such high failure rates.
The Future of B2B Commerce
B2B commerce is on the cusp of its most significant transformation since the internet. As AI agents become capable of autonomous decision-making, the infrastructure that enables business transactions must evolve to support machine-to-machine commerce at scale.
Enterprise AI Compliance Landscape
Navigating the complex regulatory environment for enterprise AI deployment. From the EU AI Act to sector-specific requirements, understanding compliance is essential for organizations deploying AI systems in production.
Introducing the SIR Framework
The Semantic Information Ratio provides a mathematical foundation for measuring meaning in AI communications. This framework enables precise quantification of semantic content, enabling more reliable AI-to-AI interactions.
Why AI Agents Need Trust Infrastructure
As AI agents take on more autonomous roles in business operations, they need infrastructure that enables them to establish trust, verify counterparties, and form enforceable agreements across organizational boundaries.
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