Research is subject to constraints of money, time, and resources, but is not normally estimated in the sense that software industry people would use the term.

On the contrary, constraints often mean you don't need formal estimates. (I'll come back to prioritization in a sec.)

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