Graham McNicoll

Optimization to Enshitification: Dark Patterns in A/B Testing A post allegedly written by a software engineer at a meal delivery company went viral recently. It described an A/B test on a “Priority delivery” fee, and not by making priority orders faster, but by deliberately slowing regular ones. Whether or not the story is true, such dark patterns are absolutely being used. And this raises an important ethical question about the use of these techniques in experimentation. Dark patterns are design or product choices that nudge, mislead, or coerce users in ways that benefit the company at the user's expense. Some examples of dark patterns include: - Making the free/default experience worse to upsell - Hiding cancel options - Obscuring fees until late in the funnel - Hiding real price with introductory pricing Often, such experiments are judged by short-term metrics, and if they are very positive, they can mask the long-term costs of the change. The costs of such changes tend to be delayed and externalized. Some of the longer-term risks of dark patterns, that are not as obvious, include: reputational risk, regulatory exposure (many of these dark patterns cause the ire of legislatures), team morale and attrition (employees, like the original poster on Reddit, if real, may quit), and risk from competition. In other words, dark patterns trade long-term value for short-term gains. Solutions There are practical ways to help solve the problems above. Many companies establish ethics committees or adopt value principles that guide product development. However, these can erode over time, especially under financial pressure. The most practical way to avoid dark patterns is to use the right metrics. If you only measure immediate revenue or conversion, you will eventually design experiments that extract value rather than create it. To counteract this, teams need to deliberately include metrics that reflect longer-term outcomes, such as retention, repeat usage, complaint rates, refunds, customer support contacts, brand sentiment, or even qualitative feedback. Not all of these can be perfectly measured- or measured at all (like the cost of losing key employees). Good product judgment requires acknowledging uncertainty. An experiment that produces a short-term lift but plausibly damages trust should be treated with skepticism, even if the expected lift is excellent. Ultimately, the goal of experimentation is not to prove that you can move a number. It is to learn how to make something people genuinely want. A/B testing is a powerful tool in the service of that goal, but the further you drift from it, the more your “wins” become signals of underlying enshitification rather than progress. Make sure your metrics reflect your real goals as much as possible. In the long run, the most effective optimization strategy remains the simplest: make the product better.

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