Optimal Size

Why Getting It “Just Right” Matters.

Every system has an optimal size—stray too far in either direction, big or small, and you waste resources or create new problems. Figuring out that “Goldilocks” zone isn’t always easy or obvious.

Organization Size

Take organizations, for instance. Many can run leaner than people expect. At one point, iNTERFACEWARE had almost 30 employees and was supposedly “focused” on healthcare—though in reality, it was drifting in too many directions. Over time, I (Eliot Muir) realized that the truly optimal size was dramatically smaller, closer to 2 or 4 dedicated people. We’re still refining that, but the lesson is clear: bigger isn’t always better.

When larger scale is needed—say, handling big layoffs or building new tech—we can tap our affiliate network. This gives us flexibility without unnecessary bulk. Surprisingly, a clear mind and a critical approach to business processes often accomplish as much, or more, than simply throwing more people at the problem. Clients might clamor for complicated solutions, but sometimes the most efficient move is to resist unnecessary noise.

Network Packet Size

The principle applies in technology, too. In computer networking, data travels in “packets” of a certain size. Oversized packets slow things down, since one error means resending a lot of data. Tiny packets also hurt efficiency, as they create excessive overhead. That’s why engineers carefully choose the maximum transmission unit (MTU) to strike the best balance of speed and reliability.

Transportation: Buses vs. Cars

A city’s transport system makes a great analogy. If everyone drives alone (like tiny network packets), congestion and wasted effort are guaranteed. But traditional public sector solutions to transport are bad. Buses are not safe for women and children and often slow the flow of traffic down. More flexible solutions which emphasize decentralized modes of communiction and trust are often actually better in the long run.

Public sector organizations can unwittingly damage the market for better solutions to emerge to these problems.

Other Everyday Examples

This “optimal batch size” logic pops up everywhere: