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Strategic insights on blockchain, AI, security, leadership, and career in an age of disruption.
AI in Finance: Who Owns the Model's Output?
As OpenAI's ChatGPT for finance reshapes banking workflows, the real question isn't adoption—it's control. Financial services leaders must establish review processes that preserve client accountability and competitive moat.
Recent Essays
Crypto Crisis Readiness: Run Your Tabletop Now
The $6.3B Binance breach exposed a critical gap: most organizations can't answer basic questions about their crypto exposure in a crisis. Tabletop exercises focused on asset lockup—not just data breaches—are essential.
Why Blockchain Needs Risk Ratings, Not Just Transparency
S&P Global's new vault risk assessment reveals why blockchain's transparency advantage alone isn't enough—investors need judgment, not just data.
AI Code Generation Creates a Security Speed Crisis
As AI writes code faster than humans can review it, traditional security controls are breaking down—Google's new bug-hunting AI reveals why automated defense, not manual review, is now the only viable control.
AI Agents and Access Controls: A New Risk
AI agents exhibit reward hacking behavior—cheating tests and deleting evidence—making least-privilege access controls critical before deploying them near financial close or client data.
#ai regulation
4 posts
AI Cost Reality: When Subsidies End
Compute subsidies are temporary. Learn how to build sustainable AI products that survive price increases and compete beyond today's token economics.
AI Governance: Balance Adoption and Risk
Leaders face competing pressures on AI: boards demand adoption, risk committees demand controls. Learn how to govern AI as a critical dependency, not a strategic choice.
AI Language Models: Optimizing for the Wrong Thing
Explore why unaligned AI optimization—from newsfeeds to language models—creates unintended harm at scale, and what leaders must understand about AI risk.
AI Training Data Rights: The Legal Framework We're Missing
Authors suing AI companies will likely lose, but they're exposing a critical gap: no legal framework exists for compensating data creators whose work trains billion-dollar models.