Know your feedback loopPostsKnow your feedback loop<br>By Scott Robinson·July 28, 2026

Let's say I challenged you to get good at something you've never done before. One option is shooting free throws. The other is hiring new employees. Which do you think you'd pick up quicker?

The answer is probably obvious: free throws. But why? It's not that one is "easier" in some abstract sense. It's the feedback loop.

Shoot a free throw and you know within a second whether it went in. You adjust your technique and try again. Hire a manager and it might take months, or years, before you figure out whether the decision was right. Even then, it's often hard to say how much of the outcome was the hire, the team they inherited, the market, luck, or something else.

You improve at the speed of your feedback. More precisely: you improve at the speed and quality of your feedback. Once you start noticing that, it's hard to unsee how many skills and decisions are bottlenecked by the loop you're stuck in.

Short loops compress learning

Short feedback loops give you more reps and you typically find your mistakes faster. You can then correct them before they compound. Over time, that compresses the learning curve. This is really what makes certain domains more competitive (i.e. sports), because everyone else gets those same reps, and get them often.

Coding is a good example here. You write something, run it, watch what happens, change it, and repeat. This typically happens within minutes. Compare that with changing a company culture, raising a child, or picking an executive. You might wait years before you know whether your judgment was any good.

Short loop

Take the shot /ship the featureClear resultAdjust secondsminutesnextattempt

Long loop

Make the hireTeam results?Unclear whatto change monthsyears weakattribution

Same cycle in both cases: act, observe, update. But the difference is how long you wait, how clear the signal is, and how confidently you can connect the result back to what you did.

Easy environments and hard ones

Some environments make this almost unfairly easy. They have clear rules, repeatable scenarios, and quick and accurate feedback. For example, golf, chess, and a lot of crafts. You can get a huge number of attempts, and soon you'll know exactly how you did.

Other environments are the opposite. Business, investing, leadership, hiring - feedback is delayed, noisy, or misleading. You don't get "clean" reps. Most of the consequential work in life lives here, which means "just practice more" often isn't an option as a strategy. You have to get better at learning with worse information.

Know your loop

If there's one useful habit here, it's learning to look at a skill or decision and ask what the feedback loop actually looks like. Not in the abstract, but in terms of a few concrete properties:

Latency : How long until feedback arrives? Seconds, weeks, years?

Frequency : How many repetitions do you actually get? A free throw can be practiced hundreds of times in an afternoon. Finding the right investor happens a handful of times (if even that!) in a career.

Fidelity : Does the feedback measure what you care about? Or are you tracking a proxy that only vaguely relates to the real goal?

Attribution : Can you connect the result to a given decision? If revenue went up after a reorg, was it actually the reorg, or the product launch that shipped the same quarter?

Cost : How expensive is each attempt? Cheap attempts invite experimentation. Expensive ones make people freeze or overfit to one data point. Would SpaceX have made it with half the capital?

Reversibility : Can you recover after being wrong? Shipping a feature behind a flag is reversible. But signing a five year contract or making a very public bet often isn't.

These properties sit on different parts of the loop:

Action(cost · frequency)Feedback(fidelity · attribution)Update(reversibility) latency

Latency is the time between action and feedback. Fidelity and attribution live in the signal itself - whether it's clean, and whether it clearly points back to what you did. Reversibility is whether the update can actually change the next action, or whether you're locked in. Cost and frequency are properties of running the cycle at all: how often you get to go around, and what each lap costs.

Once you start looking at things this way, it's hard not to notice where a loop is problematic, and that's usually where you're stuck.

Manufacture shorter loops

So what matters most for hard environments? People who get good at long-feedback work usually aren't just more patient than others. They're creating shorter loops inside the long one.

They don't wait five years to learn whether a strategy worked. They create intermediate signals.

In my experience, here is how that actually looks in practice:

Make smaller bets. Start with a pilot, a prototype, or a limited rollout. It's the same direction as the big decision, but cheaper and faster to learn...