Jevons Paradox

More efficiency, more budget

credit: The Simpsons - Disney

In economics, the Jevons paradox (/ˈdʒɛvənz/; sometimes Jevons effect) occurs when technological advancements make a resource more efficient to use (thereby reducing the amount needed for a single application), however, as the cost of using the resource drops, overall demand increases causing total resource consumption to rise.^[1][2][3][4] Governments have typically expected efficiency gains to lower resource consumption, rather than anticipating possible increases due to the Jevons paradox.^[5]

In 1865, the English economist William Stanley Jevons observed that technological improvements that increased the efficiency of coal use led to the increased consumption of coal in a wide range of industries. He argued that, contrary to common intuition, technological progress could not be relied upon to reduce fuel consumption.^[6][7]¹

To summarise: the saving driven by something becoming cheaper to use < the amount of extra use triggered by the reduction in cost.

One of the things I used to be concerned about at Cube was whether saving customer spending on their cloud data warehouse bill would make these vendors less interested in partnering with us, because of potential lost revenue.

However, the real world doesn’t work like this: when people and teams use less of their existing spending, they don’t just return their budgets. Budgets are like stored potential energy—power. Teams would prefer to reallocate their budgets to do something else useful with them and increase their output and value instead. This debunks the idea that saving money through efficiency will reduce spending.

Teams can be asked to reduce spending more directly and then may look for optimisations, but it’s often easier to reduce headcount than reduce tech spend, which then requires a migration or change of use of something else, which then uses up labour, which is often more expensive than the tech spend.

I think there is a further point here—even on the same budget, greater efficiency will only increase the retention of these customers, as they will find it harder to find a more efficient alternative and will be less likely to complain about pricing. Using the tool more efficiently will also reduce the chance of big spikes in consumption where their pricing is linked to usage, which again leads to greater satisfaction, lower price sensitivity, and higher retention.

Another result of greater ease and efficiency in using a product (ease equates to lower cost and higher efficiency because of labour savings) is the discovery of new use cases that have now become economically viable on a time or cost basis. When I introduced dbt and Snowflake at Lyst and invited other engineering teams to provision their own warehouses (not expecting much to happen), they started moving workloads off AWS Glue and Athena, because it was a much easier toolchain to work with.

So, not only do teams find new use cases viable, but old ones running elsewhere get cannibalised, increasing the share-of-wallet for the more efficient and easy-to-use tool. Making new use cases economically viable through labour or cost savings justifies allocating more budget to unlock more profit. This is also why the speed and performance of a data warehouse are trumped by ease-of-use when considering whether it will be a successful product or not—associated labour is almost always more expensive than any other consideration of using a tool.

Performance is the most common metric that database nerds like me use to measure our importance, and like sports fans, we tend to pick teams that we root for against everyone else. If your favorite database wins the benchmark wars, you have bragging rights at the watercooler. You can brandish your stats, backed up by blog posts, to prove to anyone who will listen that your favorite DB is the champ.

Performance in general, and general-purpose benchmarking in particular, is a poor way to choose a database. You’re better off making decisions based on ease of use, ecosystem, velocity of updates, or how well it integrates with your workflow. At best, performance is a point-in-time view of the time it will take to complete certain tasks; at worst, however, it leads you to optimize for the wrong things.²

So yes, save yourself money on the same workload—save a lot of money—but you won’t spend less overall. Being more efficient with your budget is good stewardship, and you will be rewarded with a larger budget. You will spend more but smarter.

This is really a standard principle of investment yields. You will be trusted with a larger budget than before, as you can return a higher return on investment with the budget entrusted to you. This is absolute gold from a vendor’s perspective - no one drops the tool that made them look smart in front of their CFO. That’s the tool you plug in on day one at your next company.

1. https://en.wikipedia.org/wiki/Jevons_paradox

2. https://motherduck.com/blog/perf-is-not-enough/