Stocks, Flows, Resiliency, and Layoffs

The costs of decreasing a business’ capacity to absorb change.

Assumed audience: People broadly familiar with systems thinking terms. (I do not explain them here!)

Donella Meadows on stocks and flows and resilience; my thoughts on how they apply directly to layoffs.

I. Stocks

Meadows (emphasis mine):

A stock takes time to change, because flows take time to flow. That’s a vital point, a key to understanding why systems behave as they do. Stocks usually change slowly. They can act as delays, lags, buffers, ballast, and sources of momentum in a system. Stocks, especially large ones, respond to change, even sudden change, only by gradual filling or emptying. … Changes in stocks set the pace of the dynamics of systems. … The time lags that come from slowly changing stocks can cause problems in systems, but they also can be sources of stability. Soil that has accumulated over centuries rarely erodes all at once. A population that has learned many skills doesn’t forget them immediately. You can pump groundwater faster than the rate it recharges for a long time before the aquifer is drawn down far enough to be damaged. The time lags imposed by stocks allow room to maneuver, to experiment, and to revise policies that aren’t working.

One of the key stocks” for any company is its people: the contextual and historical knowledge they carry, their intuitive understanding of the company-as-a-system, the tacit knowledge of how things get done here” — from who the experts are to how to leverage even the most difficult people in an organization. Those stocks accumulate both directly, in the number of people who work in a company, and indirectly, in the tenure of those people at the company. Most managers and leaders intuitively recognize (or very quickly learn!) the cost of hiring a new person: they have none of that tacit knowledge, and must acquire it slowly — and often painfully — on the job, and that simply takes time. Do what you will, no employee handbook will cover everything people need to know to be successful in a new role.1 You pick it up on the job. This is a huge part of what makes hiring for a new role so challenging: you know that even once the hiring is done, the work is just getting started. The time for that stock of a new hire to gain enough of the related stock of knowledge to generate their full-capacity flow is long — and the more senior or specialized the role, the longer.

A lot of leaders miss the opposite side of this, though, when feeling financial pressure. Layoffs destroy stocks, plain and simple. That stock, once gone, cannot be directly replaced. Refilling to the same level of capacity, and therefore the ability to sustain the same level of output, i.e. flow, as before, will take considerable time and in some cases may never be attainable. If you get rid of people who had key institutional knowledge — particularly, if you eliminate people who together represented the total set of institutional knowledge — then that stock can never be fully replaced, and even getting to the 90% water-mark of understanding again will take incredible effort and time.2

That in turn has exactly the knock-on effect I strongly emphasized in the quote above: it cuts your ability to maneuver, to experiment, and to revise policies that aren’t working”.

II. Resilience

Meadows again (emphasis mine again):

A resilient system has a big plateau, a lot of space over which it can wander, with gentle, elastic walls that will bounce it back, if it comes near a dangerous edge. As a system loses its resilience, its plateau shrinks, and its protective walls become lower and more rigid, until the system is operating on a knife-edge, likely to fall off in one direction or another whenever it makes a move. Loss of resilience can come as a surprise, because the system usually is paying much more attention to its play than to its playing space. One day it does something it has done a hundred times before and crashes.

Resilience is not the same thing as being static or constant over time. Resilient systems can be very dynamic. Short-term oscillations, or periodic outbreaks, or long cycles of succession, climax, and collapse may in fact be the normal condition, which resilience acts to restore!

And, conversely, systems that are constant over time can be unresilient. This distinction between static stability and resilience is important. Static stability is something you can see; it’s measured by variation in the condition of a system week by week or year by year. Resilience is something that may be very hard to see, unless you exceed its limits, overwhelm and damage the balancing loops, and the system structure breaks down. Because resilience may not be obvious without a whole-system view, people often sacrifice resilience for stability, or for productivity, or for some other more immediately recognizable system property.

The second-order impact of layoffs is that it decreases the resilience of the company. Knife’s edge systems can be the most profitable in the short term, and staying as close to minimal” as possible is therefore very attractive from a purely financial perspective. However, as we all ought to have learned very clearly from direct and painful experience via the supply chain shocks caused by COVID-19, systems running that close to the edge are fine right until they fall over entirely. The same is true of companies run on thin margins — not just thin financial margins, but (and more importantly!) thin people margins.

Notice that resilience and stocks are very closely related. The resilience of a system is in major part a function of its stocks, along with its feedback cycles. How well can a system adapt when demand on a flow increases or decreases? It depends on both what resources it has available (stocks) and how fast it can respond to those changes (both stocks and feedback cycles). In terms of a company, that means that its people are one of the key sources of resiliency for the company as a whole. If you get rid of them (or lose them because they reasonably and justifiably interpreted layoffs as a reason to find some place with better prospects), you by definition cannot draw on their expertise, and even at the most basic level have fewer people you can shift over to address a new challenge or opportunity.

When you eliminate the extraneous” people, in other words, you cut not only the built-up stock of their knowledge and understanding, but also the system’s ability to absorb changes running the other direction. A prime example: many tech companies laid off both recruiters and engineers last year — despite the fact that they could easily have kept those employees while remaining profitable. (“Grew less than goal” is not the same as did not grow” is not the same as decreased profitability” is not the same as was unprofitable”!) Then, many of those same tech companies were surprised by the sudden boom of public interest in (and new capabilities of) generative AI in the second half of the year.3 Having let go many recruiters (“We’re not hiring right now, so why are we paying these people?”) they now had a harder time with the hiring process just when they might want to staff up new projects with expertise they did not previously have. Having let go many engineers (“We need to be more lean and get more done with less”), they now had less ability to try new things without cutting into the maintenance and development of existing projects. Notice that in both cases, the choice could easily be defended on purely economic grounds in the short term: keeping margins up and therefore maintaining higher profitability. Moreover, the cost was not even that long-term, because the ability to respond to new changes in the system was decreased. If your goal was to sustain the profitability (and even the satisfying-Wall Street-quality!) of your business, those layoffs hurt you, and it did not take long.

By definition you cannot predict when there will be new pressures on your system in the future. Note that this is not an argument for maximum buffers. It is an argument for having appropriate buffers, and not cutting them as soon as there is any pressure on the system. To the contrary, as long as your overall trajectory remains healthy, leaving them for a much longer time is apt to leave you much more room to maneuver when the next unpredictable changes comes in. Critically, they leave you with less to build on when you come out of whatever economic pressures you are reacting to. To tweak Meadows: people often sacrifice resilience for [short-term margin maximization]”. In the short term, you may not see the costs; in the medium term, your flexibility and adaptability will fall; in the long term, if you keep repeating that sacrifice, you eventually will hit the point where the whole system breaks down.

III. Therefore

Layoffs deplete the stock of your system and reduce its resilience, so they are always costly. Now: layoffs can sometimes be a hard necessity. If you literally cannot be profitable with your current set of employees (by which I do not mean: turning out Wall Street-pleasing levels of profit, but simply run the company”) and there is no path to continuing to keep them even by asking everyone to take pay cuts (and leading the way yourself!), then a layoff can be the right move. In any other circumstance, though, a layoff is just winding up a spring with a punching glove on it, setting a timer for the release, and then standing in front of it — waiting to get hit.


  1. An employee handbook can, first, never be sufficiently exhaustive to capture every nuance; and, second, can never address the personal and political dynamics of a real job: Frank is often cranky but if you get him to trust you, you will have the best ally in the world”; Janice is exactly as brilliant as she seems”; Elliot is good at his job, but thinks he knows it all, and needs a firm managerial hand”; Julie will accidentally derail any meeting she is in if you do not have a super strong agenda”. ↩︎

  2. Yes, this is exactly what Elon Musk did to Twitter. ↩︎

  3. I leave aside here entirely whether those kinds of systems are good or not. The point here is simply the impact of companies’ choices when they wanted to lean into those new opportunities. ↩︎