Writing
On autonomous decisions, the AI·IO·ML operating model, the shared world model, and the inversion of the planning function.
Google Research recently published evidence that an AI taught to reason under uncertainty generalises better than one taught from perfect-hindsight answers. It is a clean statement of a principle we built our decision agents on: teach the process under the calibrated band, not the answer you only know in retrospect.
A widely-shared technical retrospective makes the honest case for what today's AI cannot do: out-of-distribution reliability, self-explanation, causal reasoning, and long-horizon decisions. In supply chain, those four limits are the spec we built to. Here is the mechanism for each, and the operating model that keeps a human in control.
PwC's 'From Process to Pulse' gets the diagnosis exactly right: the bullwhip is a latency problem. But the cure is not one fast loop. It is the right loops, at the right horizons, each acting on a calibrated band.
Ram M catalogues five ways good employees accidentally weaken their organizations, each with a hidden cost. We hold AI agents to a higher standard than we hold ourselves, and there are four reasons that is exactly right.
Ari Joury's argument is right. Agents collapse the deployment cost. Here is what that looks like inside an industrial supply-chain substrate.
LLMs are still just Prediction Machines. Supply Chain Planning needs Decision Machines.
A new McKinsey piece maps the contours of agentic enterprise transformation with genuine insight. But the destination it describes requires a mathematical foundation the consulting narrative does not provide.
Every general-purpose technology since the printing press has restructured work. The firms that accepted the restructuring early captured the productivity gain. The firms that adopted the new technology in the form factor of the old one became the case studies the next generation reads. Agentic AI is running the same play right now.
Why Berkeley's "next competitive moat" isn't a metaphor, and what it takes to actually fill it.
Most AI-native risk products are solving the wrong problem, extracting a supply-chain graph from text. Autonomy starts from the other direction. The technique stack inverts.
Jordi Visser's thesis on how digital economic activity transitions from human-constrained labor to machine-driven execution, and what it means for planners.
Why your supply chain planners spend 95% of their time not planning, and how BCG's Rules of Response apply to information flow.
A supplier with a 10-day average lead time that varies from 5 to 25 days is not the same as one that consistently delivers in 9-11 days. Distribution fitting changes everything.
The supply chain planning industry is stuck. Here's our diagnosis, our guiding policy, and the coherent actions we're taking to fix it.
Why the most critical layer of supply chain knowledge has never been captured, and what changes now.
