Essays and updates. First principles, not playbooks.
After Fable 5, enterprise AI is consolidating around auditable open models, sovereign stacks, and security as the adoption gate. Three paths forward for operators building agent workflows that survive policy shifts.
16 min read →Serial chat caps your compounding rate. The new bottleneck is concurrency: how many agent loops you run before lunch, not how clever your next prompt is.
10 min read →Agentic coding tools are useful for more than creating product. They can act as context engines and workbenches for complex initiatives that need context, organization, communication, and a clean output package.
9 min read →Claude Fable 5 shipped as the most capable model ever released to the public, then a government directive took it offline within days. The real risk was never the model. It is who holds the off switch on frontier intelligence, and why open source should be America's strategy, not its loophole.
11 min read →Task automation makes work cheaper, but closed loops make the company smarter by turning every cycle of work into memory, correction, and compounding advantage.
8 min read →AI is most useful when it removes the repeatable tax around work so people can spend more of their careers on judgment, creativity, relationships, and owning outcomes.
8 min read →Your best operators should not be bottlenecks. Skills make expert judgment available to every employee, 24/7, before you add agents, tools, and APIs.
13 min read →AI may expand software TAM by making solo operators and tiny teams capable enough to need enterprise-grade tools, while enterprise products become easier to sell downmarket through agents, better interfaces, and lower adoption friction.
11 min read →AI agents only become useful in finance when they can work directly on governed data. Agentic performance management starts at the fabric layer, where data, definitions, permissions, and business context are close enough for agents to help form the first read of performance.
10 min read →A digital twin is not a voice clone. It is a way to scale your philosophy and judgment so cheap output does not drown you. Second brain was memory. The next layer is how you choose. Build it on purpose, or platforms will infer a cheaper you.
6 min read →When cognitive work gets cheap, scarcity migrates. Production, selection, and standards become the new staircase. Five skills that reprice: taste, framing, customer proximity, standards, and loop ownership.
10 min read →The AI-native company is not defined by AI features. It is defined by redesigning work around abundant intelligence, lower coordination costs, and human judgment reserved for where it matters most.
10 min read →Token budgets feel like control, but they are a poor proxy for business value. Real AI ROI comes from measuring Verified Work Units and Time-to-Verified Outcome.
5 min read →Human latency is the drag on compounding. The next companies win by architecting work so progress rarely waits on a person, and by trading dashboards for sovereign systems.
5 min read →AI looks cheap because it is subsidized. A compute war is mispricing intelligence, and the naive builder can exploit the gap before the toll arrives.
3 min read →Why staying naive is the ultimate strategic advantage in business and how the greats operated from first principles.
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