Turn Preasons Into Reasons

A remarkable amount of geekery-advice comes in the form of rules & slogans accompanied by appealing. intuitively correct, theoretical reasoning using simple logic applied to pre-existing abstractions.

I’m gonna pull a GeePaw-ism here and relabel “appealing, intuitively correct, theoretical reasoning using simple logic applied to pre-existing abstractions”. I’m gonna shorthand these to “Preasons”.

So. We get a lot of preasons in our trade. And they’re applied at every level, including but not limited to Coding, Designing, Tool-Using, Planning, Teaming, and Managing.

Why do we use preasons at all?

Well, simply put, we’ve no choice.

If I don’t know how to do a thing I have to make a guess about how to do it. If I am remotely interested in success, I have to have get some preasons, choose among them, and give my favorite a try. There are lots of ways to gather preasons, including making them up myself. (There are even preasons about the best way to do this.)

The only possible fix for that is to always only do things one knows how to do already.

But doing only things you already know how to do is a losing proposition. It will lose in business. It will lose in geekery. It will lose in management, and so on and so forth.

We have to change to win, and that means we have to have preasons to guess which change will work.

Why do the geek trades use so many?

I’m told by people in other trades that it applies to all trades equally. I don’t believe it.

It’s true I have only a passing familiarity with most other trades, so I could be wrong. But in my experience, preasons dominate the geek trade like no other. I see this every day in my work with teams all around the world. I see it in my RSS feed and I see it in twitter.  I see it extensively in hucksters and third-rate thinkers.

I have come to believe there are a variety of factors that cause this huge reliance on preasoning in geekery in particular. But if I lump all of these factors under a single rubric, it would be this one:

Ignorance-Discomfort: we don’t know how geekery works yet and we really don’t like that.

So what’s wrong with preasons, anyway?

Nothing is wrong with preasons. Something is wrong with how we use them.

Regardless of whether any given preason has merit, there is one thing that every preason lacks, by definition. Preasons are always “pre-“. Pre-experience. Pre-results. Pre-data. Pre-implementation. Pre-local.

Preasons don’t account — can never account even in theory — for the actual experience of the actual team that is actually acting on them.

Now, this isn’t the fault of the preason. We absolutely have to have preasons when we don’t know what to do. There is simply no way around them. We cannot know with certainty the result of applying a given preason to our given situation until we apply it.

The sin isn’t in using a preason. It’s in not reviewing the result from using it.

Preasons don’t become reasons automatically.

I got this theme, you’ve already heard it: Act-Then-Listen is the fundamental template of successful geekery.

What I am aiming at with that theme is a failure pattern I see again and again. We grab for a preason, use it to justify an action, then grab another and do the same, ad infinitum, ad nauseum, ad legacy, ad enterprise, ad ass-hattery on an intergalactic scale.

In the act-then-listen pattern, the listening has equal value with the action. In the preason-act-preason-act-preason-act pattern, the listening is what’s missing.

This missing step, the part where we listen to see what effects our action has, is how we can convert preasons in reasons. If we don’t listen to our own results, preasons never become legitimate reasons.

If we fail to do that conversion, in a world in which there’s so much we don’t already know, we’ll create something as awful, useless, demoralizing, and largely ineffective as — well — the geek trades.

So How Can We Change This?

There are lots of things of specific things we can do differently, depending on the various domains and preason-based actions. But generally? These things:

Suspect all systems for developing software. All of them. Suspect anyone claiming to have a drop-in system for you or your team. Anyone. We do not know how to geek, still less can we tell people how to geek using abstractions applied from a great distance.

Suspect “try harder” answers to negative experiences. I am not saying here that you don’t have to try, or that you don’t have to practice. I’m saying at some point, a long sequence of “try harder” has to be replaced with “try different”. How long? It depends on context, but my generalized advice to the mode of the bell curve: shorter than that.

Suspect large resource commitments to preasons. Large resources include millions of dollars, your entire software development enterprise, any step that takes longer than a breadbox. Even if your current preason-experience with a project or a sub-team is positive, roll it out one step at a time.

When You Don’t Know What To Do,

What To Do Is Act-Then-Listen

 

 

 

Estimating: Stop Trying Harder

(Note: Lightly edited and adapted from a twitter thread, where I’m @GeePawHill. Noobs be advised, I speak freely there.)

Accuracy in estimating software development times is a powerful example of forty years of “try harder” not producing any positive results.

Now, given some small change X and some substantial knowledge of the current state of my software, I can usefully estimate short-term work, from a few minutes up to a 50% hit-rate around about a week. This is because I have been a successful independent programmer for 35 years.

Without making any ridiculous boasts about my mad geek chops, I can still say this: I am good at programming, and very experienced. I am telling you that my estimating skill starts having more misses than hits in units measured in single-digit days. And that is when I have strong knowledge of the existing code base.

A week. Maybe two.

I Have Seen This

VLCAs[1] routinely spend weeks estimating things that are months away. And they do it over and over again in spite of the lack of consistent value.

Companies spend millions of dollars on this waste, then won’t buy hardware for their teams, even teams that are currently winning. (I am not exaggerating for effect. I know I often do, but I am not doing so now. Sometimes I say such strange but true things that I have to make this clear.)

The dollar waste is truly staggering. And dollars don’t begin to capture the cost in team stress.

It is to laugh. Or cry. I can never quite decide.

This Theory Is Bunkum

They patiently explain to me, as if I were a goodhearted but somewhat simple neophyte, the theory of how their planning works. It’s centered around the sophisticated concept of something called “addition of numbers”.

Sadly, the numbers are a) made from whole cloth, b) made entirely without consideration of each other, and c) not able to take into account even minor changes in market.

Why are people so resistant to the dramatic evidence that comes from every side of our trade? I think it’s a combination of confirmation bias and third-rate thinking.

People have any two successes in a row and they think they’ve discovered a method. Sometimes it’s just one partial success. When they have three in a row, they give it a catchy acronym and pretend it’s a system others should pay to learn.

A Cheap Experiment And A Proposal

Flip a coin and see how long it takes you to get 3 heads in a row.

That’s what a 50% success rate looks like.

When it happens, do you believe you have invented a new technique for coin-flipping, require it of all future coin-flippers, and start buying up clever domain names to advertise it? If so, I have any number of wagers I’d like to make with you. I will travel, if the stakes are sufficient.

For a small handling fee, I’ll even procure your domain names for you.

What Do We Do Next?

Meanwhile, in that same part of the forest, VLCAs constantly ignore the far more urgent question: what is the next most important change we should make?

Not only is “what’s next” more important than “where’s it end when”, it’s also vastly easier to determine. No one can describe the state of their software a year from now. Everyone has an idea about the next important small bit.

Now, given that we have failed at this accurate-long-term-planning thing ten thousand times, maybe we need to rearrange things so we don’t have to do it at all any more.

You can know where you are now and where you will be tomorrow. I mean, literally, “tomorrow”, not metaphorically.

Try Different

It seems strange to have to coach giving up, but that’s what I do. Please give up trying to predict the state of your software a quarter from now, because it can not be done for less money and time than spending a quarter getting there.

They say, “If we don’t know the future we don’t know the optimal way to get there.”

I say, “You can’t know the future and you can’t know the optimal way to get there until it is past.”

Change your business model so you don’t have to reliably predict the state of your software more than a month ahead. There are myriad ways to make this happen.

Or continue to spend hundreds of millions of dollars every year to not get what you want, in service to some over-simple theory, in the absence of consistent real data that the theory is valid.

Optimize Knowing What To Do Now

[1] VLCA is a GeePaw-ism: It means Very Large Corporation of America, an allusion to the Monty Python short called “The Crimson Permanent Assurance”, typically but not universally shown as the opening short film before “The Meaning Of Life”.