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Law of Diminishing Returns
Law of Diminishing Returns
Law of Diminishing Returns
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Think back to the last time you overindulged in a huge, delicious dessert. It’s likely that the first bite was divine, but perhaps the subsequent bites left you wondering whether you’d regret overeating, and the final few bites might have just left you feeling sick. If you’ve had that experience, congratulations! You’ve gained a physical understanding of this model. 

The Law of Diminishing Returns predicts that increasing one variable in a production process, while keeping the others constant, might initially increase output but, after a point, will result in smaller returns. Put simply, too much of a good thing will, eventually, return less value. 


Also known as the Law of Diminishing Marginal Returns, this model is a foundational economic principle typically applied to production decisions and planning. It has a number of assumptions, primarily around factors such as technology remaining constant — See Limitations below for more. 

The Law of Diminishing Returns predicts that new inputs will progress through a cycle of increasing returns, followed by diminishing returns, and will finally result in negative returns. You can view it in conjunction with the Fixed versus Variable Cost model, as typically Fixed factors will remain constant (e.g. building size) while a variable factor will be increased (e.g. labour) when seeking greater value. However, the fixed factor will provide limitations to growth over time. For example, only so many employees (variable cost) can work effectively in a given building (fixed cost). In addition to fixed costs, other causal factors behind this ‘law’ might include limited demand, lower productivity returns, and the negative impact on parallel factors. 


The Law of Diminishing Returns can be applied to almost anything, on an economic,  personal, or societal level, that involves input to create value. For example: 

  • Hiring new employees in a shop, at first might return more sales before the returns trail away. 

  • When developing a new skill, you’ll find quick progress initially then, at a certain point, each hour you invest will result in less progress. 

  • If you are farming a piece of land, fertiliser will help to maximise your outputs but, at a certain point, more fertiliser will not return more produce.

  • Working longer hours might deliver greater work success to a point, but will likely be counterproductive over time. 

  • An hour meeting with a group of people might return important results, but extending it to a two-day workshop won’t necessarily provide exponential returns. 

  • Launching a marketing campaign on Google? Paying x amount might see strong click-throughs, but investing exponentially more won’t necessarily result in equivalent exponential growth. 

  • Sleeping longer at night will make you feel more energised and healthy but, after a certain point, extra sleep will stop returning noticeable benefits. 


This model can be seen as a negative version of the Pareto Principle and involves a Tipping Point which indicates the beginning of Diminishing Returns. One way to get the most of your high return phase is to apply Parkinson's Law, and Timebox your investment before you reach Diminishing Returns. The Law of Diminishing Returns offers a stark contrast to Compounding, and it could be argued that progress will tend to either follow one or the other rather than continue in a consistent, straight trajectory. 

One of the reasons why you might continue to invest time or money into Diminishing Returns is the Sunk Cost Fallacy. And finally, better understand models such as Deliberate Practice and the Paradox of Choice with your understanding of Diminishing Returns. 

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Actionable Takeaways
  • Find the optimal peak. 

Know that ‘more of a good thing is not always better’, so focus on identifying the optimal peak or Tipping Point for your investment or efforts, at which point you will start to receive Diminishing Returns. 

  • Consider other variables rather than focusing on a single factor. 

If you want to increase returns or outputs, consider other options besides simply just increasing a single predictable input. You might want to look at the systems, production process, or broader inputs rather than focusing on a single variable that might have worked for you in the past. 

  • Apply the Pareto Principle and Parkinson's Law. 

Consider the Pareto Principle in identifying where your investment and energy will be best spent, prioritising the ‘20’ that will return the ‘80’. Likewise, consider applying Parkinson's Law and Timeboxing to limit how much you invest into a given area to maximise your results.


The Law of Diminishing Returns is based on a number of assumptions that are not always relevant to real-world situations. These include: 

  • No change in technology

  • A short period of impact

  • Consistent, homogeneous units

  • Measurement of product using tangible units. 

Each of these assumptions might be challenged in the real world. Technology is always progressing, the units of input are likely going to vary (hiring one person compared to another person will have different impacts) and even the questions of measurement will be more complex beyond the narrow assessment of weight or financial value. 

It relies on a static, predictable system which is rarely true or at least has clear limitations when applied to the complexity of reality.  

In Practice

Too Many Cooks. 

A useful example to explain this model is to consider a small food truck that sells doughnuts. If that truck has one cook, they might be able to produce 20 doughnuts every hour. Adding one more cook might increase that output to 40 doughnuts every hour. However, adding a third cook might only allow them to produce 45 doughnuts every hour because of the limited space and resources in the truck, and adding a fourth cook might not add any returns at all.

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Origins & Resources

The concept of Diminishing Returns has a rich and long history, being traced back to economists such as Adam Smith, Jacques Turgot, and Thomas Mathus. The earliest explicit references to this model were in relation to farming outputs and can be attributed to Thomas Malthus and David Ricardo.

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