David A. Duryea

I recently watched a developer describe a task to their AI assistant: "Build me a customer dashboard with real-time metrics." Then they went to lunch. When they returned, the assistant had created the dashboard, debugged the API connections, written the documentation, and deployed it to staging. That's not a demo. That's Tuesday. We're in the middle of a quiet revolution in enterprise AI. Not because models got smarter—though they did—but because we stopped asking AI to compl

There's a persistent myth in enterprise AI: you can have responsible, ethical, safe AI—or you can have profitable, scalable, high-performing AI. Not both. I've heard variations of this false choice throughout my career. "We can do this the right way or we can do it profitably." "Safety slows us down." "Governance is a tax on innovation." After 38+ years building and transforming enterprises across 11 industries, I've learned something different: the approaches that work long-

Every enterprise AI company faces the same challenge: their technology is more sophisticated than their ability to explain why it matters. I've watched this pattern for 38 years across every major technology wave—ERP, cloud computing, mobile, and now AI. Companies build genuinely innovative solutions, then struggle to articulate value in ways that drive adoption. The gap between technical capability and market success isn't usually about the technology. It's about the adoptio

I recently watched Daniela Amodei, President of Anthropic, discuss their approach to AI development in a CNBC interview. One phrase stopped me cold: "maximum capability per dollar of compute." Not because it was novel—but because it crystallized a principle I've practiced for 38 years. In enterprise technology, we're conditioned to believe scale is destiny. Bigger models. More parameters. Larger training runs. The assumption is that whoever spends the most wins. But Amodei ar