There’s a famous Marc Andreessen post from 2007 on product/market fit. One key insight there is the importance of market / timing, even over product and team. (Market is the current number, and growth rate, of users for the product.)
I’ll assert that market is the most important factor in a startup’s success or failure.
In a great market — a market with lots of real potential customers — the market pulls product out of the startup.
The market needs to be fulfilled and the market will be fulfilled, by the first viable product that comes along.
The product doesn’t need to be great; it just has to basically work. And, the market doesn’t care how good the team is, as long as the team can produce that viable product.
An @a16z podcast with @cdixon and @benedictevans had a gem on organizing thinking on market / timing. (Kudos to the team that makes the Swell app which has me listening to the best podcasts, for me, without any effort while I’m driving.)
@cdixon pointed out how enabling technologies and platforms; new applications; and users, and the business processes they’re constrained by, all co-evolve and how that provides a useful way to think about market dynamics.
Here’s a picture of my own mental model for this, with infrastructure included e.g. broadband penetration. It captures, at least in a simple conceptual way, the complexity of tech’s system dynamics.
It’s helpful to distinguish 2 kinds of complexity. Detail (or combinatorial) complexity is about feature sets, pixel placements, and granular market segmentations.
But dynamic complexity is the subtle interplay of trends in culture and user behavior with those in infrastructure, platforms, and applications. It’s often understanding this subtle dynamic complexity that helps “imagine the future” of a market more than detail complexity.
Elsewhere on market / timing and dynamic complexity:
John Sterman of MIT and Peter Senge have written extensively about dynamic complexity.
Mike Maples on the evolution of technology waves
This is not unique to tech. Several years ago I was leading analytics around Massachusetts’ regulatory framework for cleaner energy out to 2020 and beyond.
A lot of the available models and analyses focused on detail complexity—building up scenarios with granular segmentation of carbon polluting sectors and low-emissions technology alternatives.
But they lacked even a basic representation of dynamic complexity—for example, why infrastructure, fuels, vehicles, and consumer accessibility and mobility choices would co-evolve in the ways envisioned.
We were one of the first state agencies to use system dynamics modeling to explore this interplay quantitatively and isolate some (challenging) assumptions needed to make any policy mousetrap work in this area.
It was also a reminder that a big challenge with how economic policy and regulation is done today is that it feels like complex systems engineering… without any systems engineers involved.