When chased by a bear, engineers want to run faster than the bear, managers want to run faster than you. This is known as "the best vs the good enough", and is a very common theme.

For instance, company A releases a good enough technology, company B releases the best technology on the market. B fails and A succeeds, because A releases earlier, or because A's technology is more compatible with the status quo, etc. Engineers will commonly feel sympathy for B, managers will applaud the shrewdness of A.

It's a common story and an interesting angle, but the "best vs good enough" formulation misses something. It sounds as if there's a road towards "the best" – towards the 100%. Engineers want to keep going until they actually reach 100%. And managers force them to quit at 70%:

There comes a time in the life of every project where the right thing to do is shoot the engineers and ship the fucker.

However, frequently the road towards "the best" looks completely different from the road to "the good enough" from the very beginning. The different goals of engineers and managers make their thinking work in different directions. A simple example will illustrate this difference.

Suppose there's a bunch of computers where people can run stuff. Some system is needed to decide who runs what, when and where. What to do?

An engineer will want to keep as many computers occupied at every moment as possible – otherwise they're wasted.

A manager will want to give each team as few computers as absolutely necessary – otherwise they're wasted.

These directions aren't just opposite – "as many as possible" vs "as few as necessary". They focus on different things. The engineer imagines idle machines longing for work, and he wants to feed them with tasks. The manager thinks of irate users longing for machines, and he wants to feed them with enough machines to be quiet. Their definitions of "success" are barely related, as are their definitions of "waste".

An engineer's solution is to have everybody submit their jobs to a queue managed by a central server. The Condor software is an example implementation; there are many others, with many subtle issues and differences – or you can roll your own. The upshot is that once a machine becomes idle, it can immediately yank a next job from the server's queue. As long as there's anything left to do, no machine is idle. This is the best possible situation.

A manager's solution is to give the ASIC team 12 servers: asic01, asic02, … asic12. QA gets 20 machines, qa01 … qa20, and so on. If you want to work on a machine, you ssh to it and you work. If you're an ASIC engineer and you want to use a QA machine, you can't log in. If users fight over their team's machines, they can go to their manager. If their manager decides the team needs more machines, he goes to upper management. Good enough.

The "good enough" is not 70% of "the best" – it's not even in the same direction. In fact, it's more like -20%: once the "good enough" solution is deployed, the road towards "the best" gets harder. You restrict access to machines, and you get people used to the ssh session interface, which "the best" solution will not provide.

Which solution is actually better? Tough question.

The manager's solution requires no programming or installation, and trivial administration.

The manager's solution requires no changes of habits (ssh is the standard).

The engineer's solution yields ~100% utilization, the manager's 50%, 30%, or 10%, depending.

The manager's solution will cause less headache to the managers, on average. Once in a while, you buy a team some more machines, and they shut up. The engineer's solution requires to set priorities at times of overload, and people will constantly argue about priority with managers.

The engineer's solution doesn't run things on machines that are already busy anyway. Users armed with ssh will tend to do this, possibly with horrible slowdowns due to swapping, etc.

The engineer's solution can provide the lowest latencies.

The engineer's solution makes it trivial to utilize new machines – no need to set up sessions, just submit jobs as previously. This is good – and bad: people will demand new machines instead of reducing the load.

So there are many conflicting considerations. Their importance varies between cases, and is very hard to measure.

"The best" solution is not necessarily the best – rather, it's what the mindset of looking for the best yields. Likewise, the "good enough" solution is not necessarily good enough – it's just what you come up with when you look for a good enough approach.

Obviously, portraying "engineers" and "managers" this way is a gross oversimplification, and most real people can look at things from both angles. I like this oversimplification for two reasons:

It does help to understand and predict many common arguments and reactions. A person's perspective frequently does coincide with the title: engineers aim at the best, managers look for the good enough. Just the title is thus a good basis for prediction.

How does title affect judgement? There are arguments to the effect of "aiming at the good enough is wiser, and managers are in a better position to achieve wisdom", or vice versa. However, I don't believe either viewpoint is more correct than the other. Rather, they are biases. People in different positions acquire different biases, because they have different incentives and constraints.

The difference in incentives is that engineers are rewarded for success, while managers are rewarded for non-failure.

An exceptional engineer is unique and valuable, like a great chef – being an OK engineer is more like cooking for McDonald's. An engineer needs unusual success to be noticed. One reason such success is achievable is because he works within his area of expertise, on things that he understands. There's rarely a formal quality metric, but his deep understanding creates in him a sense of beauty that serves as his metric.

A manager is fine as long as the project doesn't fail. Most projects fail – if yours didn't, it's quite an achievement. One reason they fail is that there are a lot of ways to fail. Forget just one item in a lengthy checklist, and it won't matter how well the other 99 items are handled. People may not buy a car because there's no convenient place for a resting arm. A manager looks after a whole lot of items, usually without being able to understand most of them in any depth. He's inclined to look for simple pass/fail criteria.

If your success function is a continuous metric, you'll aim at the best. If it's a long list of Boolean values – V or X – you'll want "good enough" (V) at every entry. "The best" is just a costlier way to get a V, at a higher risk to end up with an X. The manager's outlook leads to binarization.

Another difference is that engineers and managers control different things – "One man's constant is another man's variable". In our example, the engineer doesn't decide how many machines to purchase or how to allocate them. If the number of machines is constant, what remains is to make the most of them. The manager, on the other hand, doesn't directly control utilization, and frequently doesn't know how to improve it. If utilization is constant, what remains is to properly ration it.

More generally, engineers tend optimize within constraints set by management, in part because they can't change those constraints. As to managers, they tend to settle for "good enough", in part because they can't optimize.

Which biases lead to more sensible solutions depends on the situation. However, there's a subtle reason to like the engineer's devotion to excellence, despite the manager's pressure for mediocrity. It's precisely when the manager is right in that excellence will not contribute to sales when the engineer contributes the most to users.

If an engineer's job is done so poorly as to make the product non-marketable, the product will fail and won't be used. When the quality along all dimensions passes the adoption threshold, every improvement along any dimension is effectively a gift to the users – who'd be using the product anyway. This is false where improvements contribute significantly to popularity, but true where they don't.

The improvements are inconsequential for business, but consequential economically. Economically, anything that helps people is good. For business, anything that creates profit is good. Often these definitions of "good" coincide, but sometimes they don't. In these cases, people who aim at excellence for excellence's sake help bridge the gap.

The manager's checklist approach – the business angle – ensures that the product is marketable, which is great because otherwise it won't get used. But the engineer's urge to optimize is closer to the really important goal: making the best of what we've got; this is the economics angle. So while it can lead to problems just like any other bias, it's likeable that way.