Estimates are difficult for developers and product owners

Tasks that are easiest to estimate are tasks that are predictable, and repetitive. If I ask you how long it'll take to add a new database field, and you've added a new database field 100s of times in the past and each time they take 1 day, your estimate for it is going to be very spot-on.

But in the software world, predictable and repetitive tasks are also the kinds of tasks that are most easily automated, which means the time it takes to perform those tasks should asymptotically approach 0.

But if the predictable tasks take 0 time, how long a project takes will be dominated by the novel, unpredictable parts.

That's why software estimates are very hard to do.

As you say, worthwhile software is usually novel. And to justify our expense, it needs to be valuable. So to decide whether a project is worth doing, we're looking at some sort of estimate of return on investment.

That estimate will also, at least implicitly, have a range. That range is determined by both the I and the R. If you don't have a precise estimate of return, making your estimate of investment more precise doesn't help anything. And I've never seen an estimate of return both precise and accurate; business is even less certain than software.

In my opinion, effort put into careful estimates is almost always better put into early, iterative delivery and product management that maximizes the information gained. Shipping early and often buys much clearer information on both I and R than you can ever get in a conference room.

Of course all of this only matters if running an effective business is more important than managerial soap opera and office politics. Those often require estimates in much the same way they're required from Star Trek's engineers: so the people with main character syndrome have something to dramatically ignore or override to prove their dominance over the NPCs and material reality.

But that is rarely how it works. In the dozens of different projects across ten or twelve companies I’ve had insight into, “doing Agile” is analogous with “we have a scrum master, hold stand ups, and schedule iterations” while the simple reality is “Agilefall.”

You might be speaking a little more broadly than I am interpreting.

Startups are a great example. When you raise your first chunk of money, the size of that isn't really driven by a carefully considered, detailed plan with engineering hours estimated per task. What you get is basically determined what's currently fashionable among angels and small-end VCs, plus who's doing your fundraising. (If you're Jeffery Katzenberg and Meg Whitman, you can raise $1bn. [1] https://en.wikipedia.org/wiki/Quibi But the rest of us have to make do with what we can get.)

So at that point you have a strong constraint (whatever you raised) and some relatively clear goal. As I said, cost isn't nearly as relevant as ROI, and nobody can put real numbers on the R in a startup. At that point you have two choices.

One is just to build to whatever the CEO (or some set of HiPPOs wants). Then you launch and find out whether or not you're fucked. The other is to take something akin to the Lean Startup approach, where you iteratively chase your goal, testing product and marketing hypotheses by shipping early and often.

In that later context, are people making intuitive ROI judgments? Absolutely. Every thing you try has people doing what you could casually call estimating. But does that require an estimation practice, where engineers carefully examine the work and produce numbers? Not at all. Again, I've done it many times. Especially in a startup context, the effort required for estimation is much better put into maximizing learning per unit of spending.

And how do you do that? Relentless prioritization. I was once part of a team that was so good at it that they launched with no login system. Initial users just typed their names in text fields. They wanted proper auth, and eventually they built it, but for demonstrating traction and raising money there were higher priorities. It worked out for them; they built up to have millions of users and were eventually acquired for tens of millions. On very little investor money.

Being great at prioritization makes estimation way less necessary. The units of work get small enough that the law of large numbers is on your side. And the amount of learning gained from the things released change both the R and I numbers frequently enough that formal estimates don't have a long shelf life.

So I get what you're saying in theory, but I'm telling you in practice it's different.

[1] https://en.wikipedia.org/wiki/Quibi

I think you are well missing the point - everything you put into your rebuttal is about estimates - in time, money, or resources

What I am talking about here, though, is a practice of software estimation where programmers produce detailed numbers on the amount of time requested work will take. Which is certainly the common meaning of estimating around here, and also what the original article is about.

I mean, if this is upsetting you, take a minute and go for a walk.

> You might be speaking a little more broadly than I am interpreting.

(Or I could be interpreting it more broadly)

In either case I don't think your response is apt