Deep dives into the intersection of safety-critical systems, enterprise AI, and technical leadership.
As autonomous AI agents move from prototypes to production infrastructure, the choice of runtime framework becomes a critical architectural decision. A deep-dive into the performance, security, and strategic implications of the two dominant paradigms.
In SaaS, 'move fast and break things' is a motto. In industrial AI, it is a liability. The only way to safely deploy autonomous systems is to break them in simulation first.
The current wave of enterprise AI adoption is built on a foundation of linguistic probability, not physical understanding. This creates a fundamental ceiling for mission-critical applications.
The fractional CTO model is broken. Early-stage startups need embedded technical leadership that builds, not advisors who observe from the sidelines.
Aviation has spent decades developing rigorous safety standards for software. As algorithmic trading systems become increasingly autonomous, fintech should be paying attention.
As AI systems move from assistants to agents, from tools that respond to tools that act, we need new frameworks for ensuring they behave safely and predictably.