Picture a deadlift platform. Chalk hangs in the air. A big athlete is working up to a heavy single, music low, the room half watching. In the corner, a team from an AI company is parked behind their laptops. They have fed months of this athlete's training into
This one is a little different. No org charts. No governance spines. No executive playbooks. But I would argue it is the most important kind of post I can write, because it gets at the question underneath everything else I talk about: what does it actually look like when someone
A colleague of mine recently described their organization's AI training program to me. They covered data structures, the importance of clean data, indexing, the do's and don'ts of their chat platform. Solid foundational content. Well-intentioned. And still, when I pressed them on whether
Artificial intelligence has functionally raised the floor on what a single person can execute. Because of this new baseline, the ability to build, prototype, and demonstrate technical capabilities has become infinitely easier and virtually free. But as I argued in The Omni-Tool Trap, this velocity introduces a massive new
Artificial intelligence has functionally raised the floor on what people can do when it comes to building new things. Because of this new baseline, the ability to innovate, prototype, and demonstrate capabilities has become infinitely easier. But this speed has introduced a massive new problem: mass execution without operational discipline.
Most organizations believe generative and agentic AI will clear their execution bottlenecks. The reality is far more dangerous. When we give our teams infinite capacity to generate content but fail to give them the structural context of why we are doing it, we do not get alignment. We get a
Most organizations believe they are making progress on AI. The dashboards look healthy. Usage is up. Licenses are deployed. And by conventional measures, adoption is succeeding. But here is the problem: usage does not equate to understanding. And understanding is what actually predicts value. Think about a gym membership. Someone
For the last two years, most organizations have treated Large Language Models as universal engines. One model. One interface. One cognitive layer across everything. It made sense. Large Language Models are broad, abstract, and capable of handling diverse inputs. They think in wide spaces. They synthesize across domains. They are
AI transformation is not a tooling initiative. It is a people, process, and performance initiative disguised as technology. If it does not change how your organization learns, operates, and simplifies itself, it is decoration. Most executives are asking what they should start doing in the age of AI. The more
The way we design teams today assumes a set of constraints that no longer exist. We still optimize for coordination, alignment, and information flow as if humans are the only systems capable of synthesis. Meetings are the mechanism for alignment. Headcount compensates for uncertainty. Collaboration compensates for missing information. Agentic
AI isn’t replacing good trainers and coaches. It’s exposing who the good ones really are. The organizations that adapt intentionally will build capability faster — the ones that don’t will drown in their own noise. Physical and Cognitive Training Follow the Same Rules For the past several years
Introduction For years, the cloud’s biggest selling point was its promise of elasticity — the idea that you could scale up when you need it and scale down when you don’t. It’s the digital equivalent of oxygen: always available, pay only for what you breathe. But that idea