Perspectives on enterprise AI, neurosymbolic AI, and AI governance from Mark Montgomery — Founder & CEO of KYield, with 30 years of R&D in knowledge systems and enterprise AI.
The escalating risks of pervasive LLM chatbot deployment without robust governance — vibe bombs detonating in production code, cognitive surrender measurably degrading judgment, the wrapper-app collapse, and the architectural alternative that compounds knowledge capital instead of degrading it. With verified findings from Wharton (Shaw & Nave 2026), Stack Overflow's 2025 Developer Survey, METR, Veracode, and McKinsey.
From Boltzmann's thermodynamics to neurosymbolic AI — a multi-disciplinary scientific history tracing uncertainty through physics, information theory, deep learning, the Semantic Web, and behavioral economics. Includes Solomonoff's proof that optimal prediction is provably uncomputable, and what it reveals about the architectural requirements of enterprise AI.
Read Article →As business leaders navigate the AI era, two distinct architectural concepts dominate the conversation: Nvidia's AI Factory and KYield's Business AI OS (KOS). Understanding the differences in governance, scope, cost, and human alignment — and the synergy between them — is essential for C-suite leaders building durable enterprise AI strategy.
Read Article →NSAI has reached an inflection point. The majority of the global economy needs precision, security, and sovereignty, which requires the application of specific types of NSAI systems. Research in this field grew 80× in a single decade — from 112 to 9,050 Google Scholar results. Design choices will separate enterprises that thrive from those that fail.
Read Article →How the KYield theorem developed in 1997 became the world's first enterprise AI operating system. The KOS integrates end-to-end data management with multi-modal AI functions into a single unified system — and how that changes what's possible for enterprises.
Read Article →The foundational case for neurosymbolic AI in the enterprise. Why the integration of symbolic precision and neural scale changes everything for decision-making, governance, and competitive advantage — and why LLMs alone cannot serve enterprise needs.
Read Article →Despite strong ROI in a small group of superstars, the super majority of companies still report low returns from AI. A quarter century of R&D and hundreds of enterprise engagements reveal the ten obstacles that stand in the way.
Read Article →How to build a Continuously Adaptive Learning Organization (CALO) — the decisive architecture for competing in a data-driven economy dominated by superstar firms. Includes the 15 EAI Management Principles.
Read Article →Even though some companies may seem well positioned, the fundamental economic and business environment is rapidly changing. Survival from this point forward will essentially require a strong AI OS for the super majority of organizations.
Read Article →What's different this time? Perspectives from Schmidhuber, Bengio, and Hochreiter on deep learning, CNNs, LSTM, and the convergence of hardware and algorithmics that made this AI moment sustainable — unlike previous cycles.
Read Article →Complex dynamics at the confluence of human and artificial intelligence. On AI ethics, the nature of creative destruction, and why the philosophy of the architect is always embedded in the code — written from the Santa Fe Institute.
Read Article →Patient Health Management — the Kyield Healthcare Platform use case scenario demonstrating semantic relationship structure across the full care ecosystem: patient, physician, hospital, care team, EHR, diagnostic lab, research, payer, and monitor devices.
Read Article →50+ editions of the Enterprise AI Newsletter published on LinkedIn and Substack, plus selected talks and white papers.
50+ editions. 6,500+ subscribers on LinkedIn and Substack. Expert analysis on enterprise AI, neurosymbolic AI, governance, and competitive strategy — written by a practitioner with three decades in the field.