1) I love the domain name. So relevant to the topic.
2) I fundamentally disagree with the premise and content of this article. It is ALL window dressing.
Organizational Design is a field that deals with this issue in depth. The OODA loop is one of the key ideas for understanding decision latency. Basically, it takes time to take in data, figure out what to do with it, decide on a solution, and then enact it. Basically the quicker you run through this loop, the quicker your organization's clock speed is.
But why would that speed change depending on the size of the organization? Well, because the first three steps in the loop take more time with more stakeholders even if the final one wouldn't change.
Humans do not think infinitely fast. As more stakeholders become involved, more thinking is required. But we systematically ignore this cost, despite it being very present and observable, because it involves thinking about thinking. See that article on the front page about how K8s are cost efficient for personal projects? It fully discounts the cost of the thinking required to learn and understand the system.
The head of your multi-billion dollar corporation wouldn't need board slide deck summaries of financials broken out by division, or updates regarding process change initiatives at the [X] level if they had infinite clock speed. They'd just parse the inhuman levels of data and come to a sensible conclusion. But they can't. This means there is a ton of work repackaging and communicating information across levels of an organization to fit the available thinking time available at each level.
From the perspective of someone dealing with the organization, however, things appear monolithic: when a customer service agent gives us an absurd reply to a query, we assume the entire organization is stupid.
2) I fundamentally disagree with the premise and content of this article. It is ALL window dressing.
Organizational Design is a field that deals with this issue in depth. The OODA loop is one of the key ideas for understanding decision latency. Basically, it takes time to take in data, figure out what to do with it, decide on a solution, and then enact it. Basically the quicker you run through this loop, the quicker your organization's clock speed is.
But why would that speed change depending on the size of the organization? Well, because the first three steps in the loop take more time with more stakeholders even if the final one wouldn't change.
Humans do not think infinitely fast. As more stakeholders become involved, more thinking is required. But we systematically ignore this cost, despite it being very present and observable, because it involves thinking about thinking. See that article on the front page about how K8s are cost efficient for personal projects? It fully discounts the cost of the thinking required to learn and understand the system.
The head of your multi-billion dollar corporation wouldn't need board slide deck summaries of financials broken out by division, or updates regarding process change initiatives at the [X] level if they had infinite clock speed. They'd just parse the inhuman levels of data and come to a sensible conclusion. But they can't. This means there is a ton of work repackaging and communicating information across levels of an organization to fit the available thinking time available at each level.
From the perspective of someone dealing with the organization, however, things appear monolithic: when a customer service agent gives us an absurd reply to a query, we assume the entire organization is stupid.